argumentative essay fake news

‘Fake news’ – why people believe it and what can be done to counter it

Director Institute of Cultural Capital, University of Liverpool

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This piece was commissioned by the Campaign for Social Science.

University of Liverpool provides funding as a founding partner of The Conversation UK.

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Barack Obama believes “fake news” is a threat to democracy. The outgoing US president said he was worried about the way that “so much active misinformation” can be “packaged very well” and presented as fact on people’s social media feeds. He told a recent conference in Germany:

If we are not serious about facts and what’s true and what’s not, if we can’t discriminate between serious arguments and propaganda, then we have problems.

But how do we distinguish between facts, legitimate debate and propaganda? Since the Brexit vote and the Donald Trump victory a huge amount of journalists’ ink has been used up discussing the impact of social media and the spread of “ fake news ” on political discourse, the functioning of democracy and on journalism. Detailed social science research is yet to emerge, though a lot can be learnt from existing studies of online and offline behaviour.

Matter of trust

Let’s start with a broad definition of “fake news” as information distributed via a medium – often for the benefit of specific social actors – that then proves unverifiable or materially incorrect. As has been noted, “fake news” used to be called propaganda. And there is an extensive social science literature on propaganda , its history, function and links to the state – both democratic and dictatorial.

In fact, as the investigations in the US and Italy show, one of the major sources of fake news is Russia. Full Fact , a site in the UK, is dedicated to rooting out media stories that play fast and loose with the truth – and there is no shortage.

An argument could be made that as the “mainstream” media have become seen as less trustworthy (rightly or wrongly) in the eyes of their audiences, it makes it hard to distinguish between those who have supposedly got a vested interest in telling the truth and those that don’t necessarily share the same ethical foundation. How does mainstream journalism that is also clearly politically biased – on all sides – claim the moral high ground? This problem certainly predates digital technology.

Bubbles and echo chambers

This leaves us with the question of whether social media makes it worse? Almost as much ink has been used up talking about social media “bubbles” – how we all tend to talk with people who share our outlook – something, again, which is not necessarily unique to the digital age. This operates in two distinct ways.

Bubbles are a product of class and cultural position. A recent UK study on social class pointed this out. An important subtlety here is that though those with higher “social status” may congregate, they are also likely to have more socially diverse acquaintance networks than those in lower income and status groups. They are also likely to have a greater diversity of media, especially internet usage patterns . Not all bubbles are the same size nor as monochromatic and our social media bubbles reflect our everyday “offline” bubbles .

In fact social media bubbles may be very pertinent to journalist-politician interactions as one of the best-defined Twitter bubbles is the one that surrounds politicians and journalists.

This brings back into focus older models of media effects such as the two-step flow model where key “opinion leaders” – influential nodes in our social networks – have an impact on our consumption of media. Analyses of a “fake news story” appears to point – not to social media per se – but to how stories moving through social media can be picked up by leading sites and actors with many followers and become amplified.

The false assumption in a tweet from an individual becomes a “fake news” story on an ideologically-driven news site or becomes a tweet from the president-elect and becomes a “fact” for many. And we panic more about this as social media make both the message and how it moves very visible.

Outing fake news

What fuels this and can we address it? First, the economics of social media favour gossip, novelty, speed and “shareability”. They mistake sociability for social value. There is evidence that “fake news” that plays to existing prejudice is more likely to be “liked” and so generate more revenue for the creators. This is no different than “celebrity” magazines. Well researched and documented news is far less likely to be widely shared.

The other key point here is that – as Obama noted – it becomes hard to distinguish fake from fact, and there is evidence that many struggle to do this. As my colleagues and I argued nearly 20 years ago , digital media make it harder to distinguish the veracity of content simply by the physical format it comes in (broadsheet newspaper, high-quality news broadcast, textbook or tabloid story). Online news is harder to distinguish.

The next problem is that retracting “fake news” on social media is currently poorly supported by the technology. Though posts can be deleted, this is a passive act, less impactful than even the single-paragraph retractions in newspapers . In order to have an impact, it would be necessary not simply to delete posts but to highlight and require users to see and acknowledge items removed as “fake news”.

So whether or not fake news is a manifestation of the digital and social media age, it seems likely that social media is able to amplify the spread of misinformation. Their economics favour shareability over veracity and distribution over retraction. These are not technology “requirements” but choices – by the systems’ designers and their regulators (where there are any). And mainstream media may have tarnished their own reputation through “fake” and visibly ideological news coverage, opening the door to other news sources.

Understanding this complex mix of factors is the job of the social sciences. But maybe the real message here is that we as societies and individuals have questions to answer about educating people to read the news, about our choice not to regulate social media (as we do TV and print) and in our own behaviour – ask yourself, how often do you fact-check a story before reposting it?

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Four Theses on Fake News

Fake news undermines free speech culture by impairing our ability to develop and express our thoughts. To fix the problem, w e need to police intent rather than content.

Was Twitter right to ban former President Trump for spreading lies about election fraud? Should Representative Marjorie Taylor Greene have been stripped of her committee roles? Did the Parler app deserve to be shut down for providing a platform to echo all those lies? And what should we do about Facebook, the Death Star of fake news?

We are struggling to answer these questions. A big reason why is that we still do not have a clear understanding of what fake news is, why it is bad, and how we can fix it. Here are four theses that might be of some help:

1.) Fake News is not Free Speech

Fake news requires the intent to deceive others about some current event or issue. It is speech produced by a person or organization who does not believe what the speech conveys, and yet they intend to convince others of its truth. This is why not all false news is fake news. People may accidentally say untrue or misleading things, but they are not thereby generating fake news.

What, then, is wrong with fake news? The problem is not just that a few liars are ruining social media feeds. The deeper problem is that fake news undermines our free speech culture. That may initially seem flat out false: after all, fake news is an exercise of free speech, not an abridgment of it. But that is not the case. To see why, we need to appreciate the moral reasons for protecting freedom of speech.

Freedom of speech gives us the ability to think and speak freely. As UCLA Professor Seana Shiffrin argues, we are morally justified in protecting the freedom of speech because it is necessary for us to live flourishing human lives. Developing and expressing our thoughts is an essential part of living well, and freedom of speech creates the environment in which that is possible. Freedom of speech opens the so-called marketplace of ideas where we come to understand the world and our place in it. Without free speech culture, our lives would be truly impoverished.

Fake news undermines our free speech culture because it impairs our ability to develop and express our thoughts. It does so by polluting public discourse with speech that is deliberately deceptive. In such an environment, sincere speech is not only harder to come by, but also harder to trust. It is more difficult for us to believe and to be believed. And, as Hannah Arendt points out, this imperils our capacity to think: “a people that no longer can believe anything cannot make up its mind. It is deprived not only of its capacity to act but also of its capacity to think and to judge.”

2.) Fake is Worse Than False

Fake news is likely worse than misinformation in two respects. First, the fake news uttered from some soapbox will often reverberate through the echo chambers until it comes out as something no longer just said, but believed. A lie from the Rose Garden becomes gospel at the dinner table. Second, and more importantly, fake news has a much greater corroding effect on free speech culture. Americans worry not so much that the media are accidentally wrong, but that they are willfully biased. According to a recent poll by Gallup and the Knight Foundation, “Americans perceive inaccurate news to be intentional – either because the reporter is misrepresenting the facts (52%) or making them up entirely (28%).” While every society can tolerate some degree of insincerity and deception, in America the well of trust has become almost unpotable.

3.) Police Intent, not Content

How, then, do we fix the problem of fake news? We need to police intent rather than content. We do that by authorizing agencies and institutions to regulate and disincentivize deceptive information masquerading as news. Whether those agencies are governmental or corporate is an open question. But, contrary to thinking by folks like Mark Zuckerberg , those agencies should not also monitor the truth of news content. Zuckerberg saw the obvious difficulty in doing so : “I believe we must proceed very carefully though. Identifying the ‘truth’ is complicated.” This is correct, but misses the point. In order to combat fake news, Facebook does not need to become the “arbiter of truth.” Fake news is fake because of its intent, not content. So in order to regulate fake news, we need to delete bot and sockpuppet accounts, not build algorithms that detect false information.

On this score Facebook could improve. In a recent SEC filing , Facebook estimates that up to 5% of its monthly active users are false accounts. That means that as many as 140 million monthly users are using Facebook with deliberately deceptive intent. Moreover, these phony users have been given the ability to design custom bots that automate their communications with fellow Facebook users. Facebook is handing liars a megaphone. That may be good for business, but it is bad for our free speech culture.

Of course, there will be cases of alleged fake news – on Facebook or elsewhere – in which it is difficult to determine if there was an intent to deceive. But in this respect fake news does not differ from defamation. Both depend on determining the intention of the accused, and the burden of proof (for defamation: clear and convincing evidence) is consequently high. When it comes to restricting speech, having such a high burden of proof is a very good thing. It has prevented defamation case law from sliding down a slippery slope, and we should expect the same to hold for fake news regulation. It is no accident, though, that the crackdown on fake news is now coming most aggressively through such cases. Smartmatic recently filed a defamation lawsuit against Fox Corporation, seeking $2.7B in damages allegedly caused by fake news about its products.

This is not to say that there are no grounds for regulating false content. There may be cases where misinformation poses risks so great as to warrant its being removed or otherwise censored. Just as we should not be permitted to yell “fire!” in a crowded theater, there are things we should not be allowed to post on social media because they threaten the safety and integrity of the public sphere in which free speech is possible. But in this current media environment, where fake news is a primary source for such misinformation, to regulate content is to treat the symptom, not the disease. So while regulatory agencies like Facebook’s Oversight Board may deem it necessary to moderate content, their real focus should be on intent.

4.) Cancel Trump, not Parler

If all this is right, then Twitter was probably right to cancel Trump, but Amazon wrong to cancel Parler. According to the Washington Post, while in office President Trump made 30,573 false or misleading claims . The newspaper is reluctant to call any of them “lies,” but only because intent cannot be definitively determined. Nevertheless, a reasonable case can be made that President Trump eroded free speech culture, and that his bullhorn needed to be taken away, his social media accounts shut down, his press briefings no longer aired. For Parler, the case is different. Parler itself has not spread any fake news, although it provided a platform for those who do. Should we cancel Parler for that? Probably not – at least so long as we allow the lights to stay on at Facebook.

There are two lingering worries we might have about regulating fake news and those who produce it. Neither of these worries, though, gives us a compelling reason against regulation.

For one thing, we might fear that regulating fake news invites abuse. A regulating agency might misuse its power and restrict news deemed detrimental to its own interests. This seems to be the fear motivating German Chancellor Angela Merkel’s condemnation of Twitter’s decision to ban Trump. Abuses of regulatory power are no doubt possible, but their likelihood diminishes if we keep in mind that fake news is fake not on account of its false or partisan content, but rather on account of its deceptive intent. If the agency accordingly regulates only on the basis of intent, then it will be less likely to restrict news out of self-interest or greed.

We might also worry that regulation would have an overall chilling effect on free speech. But this, too, seems unlikely. The effect of punishing liars is to encourage people to express claims they genuinely believe, even if they turn out to be wrong. Similarly, the effect of punishing fake news would be to encourage people and organizations to share news they genuinely believe. We should expect, then, that regulating fake news is more apt to stimulate than to stymie the expression of sincere speech. And that would be truly welcome news.

Carlo DaVia

Carlo DaVia is a Lecturer in philosophy at Fordham University, as well as an instructor at the CUNY Latin/Greek Institute.This academic year he will also serve as a fellow at the UC Center for Free Speech and Civic Engagement.

Donald Trump
free speech
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Seana Shiffrin

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My anti-vax friends, religionists, corporatists, libertarians, and the most war-mongering militarists are – I’m convinced – sincere in their advocacy for policies that demonstrably produce unnecessary harm (evil).

Fox News classifies much of its content as “entertainment”. It’s intent (to the extent a legal fiction can be said to possess such a thing) is partisan politics, profit, and power.

What are the criteria by which we may sort intent in such situations?

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Argument: Why do People Fall for Fake News?

Overview for instructors (“why do people fall for fake news”).

The essay “Why Do People Fall for Fake News?” by Gordon Pennycook and David Rand can be found on The New York Times website.

This article presents two possible theories as to why people fall for fake news. Although the writers argue in favor of one theory, it is balanced, respectful, and fair. It touches on bias, rationalization, and cognitive laziness. Students relate to the topic easily and see themselves in the examples. It would pair nicely with the article, “Misinformation and Biases Affect Social Media…” The article Includes outside research and the authors’ own research. It also links to a lengthy, scholarly article.

The following instructional activities, assignments, and documents are included for this reading. They are explained in the chart below and can be found in the module.

Supporting English Language Learners in First-Year College Composition Copyright © by Breana Bayraktar is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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5.5: Use of Evidence- Fake News

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Learning Objectives

This chapter focuses on the use of evidence in Why Do People Fall for Fake News?

Finding Claims & Support

Research unit – sources, introduction.

All pieces of writing have a purpose.

The article “Why Do People Fall for Fake News?” by David Rand and Gordon Pennycook has a different purpose: to support the authors’ argument. In casual conversations, the word “argument” often suggests that there is a “winner,” who ends up being right and a “loser,” who is proven wrong.

But an academic argument – the kind that college students like you read – are a little different. The authors of an academic argument want to convince you that their position on a topic or their solution to a problem is worth considering.

In Pennycook’s and Rand’s article, for example, the authors’ purpose is to offer and support their answer to the question stated in the article’s title: Why do people fall for (believe) fake news? The authors begin the article by offering two possible theories that would explain why people believe things that aren’t true. The authors’ theory is in the column on the right. A theory proposed by other researchers is on the left.

Writers of argumentative essays, like Pennycook and Rand, use claims to support their thesis. However, like the thesis, claims are not necessarily true on their own. Claims need support, usually in the form of evidence. Evidence could be a scientific study, an interview with an expert, or statistical information.

Claims and Evidence

In the following exercise, you will:

Examine the claims and evidence presented in the article “Why Do People Fall for Fake News?” by Gordon Pennycook and David Rand.
Determine if the evidence supports the claims.

Table 1. The Rationalization Theory

Pennycook and Rand want you, the reader, to trust them. If you don’t trust them, you probably won’t accept their theory about believing fake news. One strategy they use to build your trust is thoroughly and respectfully explaining an opposing theory. In this case, the opposing theory is the Rationalization Theory, which they discuss in detail in paragraphs 5-7. (Paragraph 5 begins “The rationalization camp…”).

Column 1 contains two claims that support the Rationalization Theory. In column 2, type the evidence that supports the corresponding claim in column 1. (The evidence could be a link to a study.)

Click on the links to evidence and skim the articles that you find. Then answer these questions:

Can you access the evidence that is linked in the article? If not, why not?
What evidence did you find that supports the second claim (about the effect of passion)?
What evidence did you find that supports the third claim (about the effect of politics)?

Table 2. The Cognitive (Mentally) Laziness Theory.

Use Table 1as a guide to complete Table 2, which asks for claims about and support for the Mentally Lazy Theory. The explanation of this theory begins with the paragraph that starts this way, “A great deal of research in cognitive psychology…” Table 2 is completely blank so that you can practice finding both the claims and the evidence on your own. Remember that a claim can be supported by more than one piece of evidence.

What evidence did you find that supports the first claim that you found?
What evidence did you find that supports the second claim that you found?

Evaluating “Why Do People Fall for Fake News?”

In your opinion and based on what you learned by completing the tables, which theory do you support: The Rationalization Theory or the Mentally Lazy Theory? Please explain your choice thoroughly. This question asks for your opinion so there are no right or wrong answers. Please write a short paragraph (about 75 words) and be very specific.

Activity

Distinguishing between properly documented and plagiarized outside sources used in student examples.

Guidelines

Students will be evaluating whether the content taken from “Why Do People Fall for Fake News” has been used appropriately when documented in a sample student paper. The objective is to identify whether the sample student paper is documented correctly or if plagiarism has occurred (Word-for-Word plagiarism or Paraphrased plagiarism).

In order to avoid plagiarism, the following conditions should be met:

Signal phrase, content (word-for-word or paraphrased content), in-text citation, works cited entry (reference).

In the following examples, examine the original source material along with the sample student work to determine if plagiarism has occurred. Focus on the bold content from the original source to assess if the content in the student version has been used correctly.

Original Source –

Our research suggests that the solution to politically charged misinformation should involve devoting resources to the spread of accurate information and to training or encouraging people to think more critically . You aren’t doomed to be unreasonable, even in highly politicized times.

Works Cited –

Pennycook, Gordan, and David Rand. “Why Do People Fall for Fake News.” New York Times, 19 Jan. 2019. https://www.nytimes.com/2019/01/19/opinion/sunday/fake-news.html .

Student Version –

It will take continual teaching and the promotion of critical thinking in order to alter misinformed political stances that spread fake news (Pennycook and Rand).

____ Documented Correctly

____ Word-for-Word Plagiarism

____ Paraphrased Plagiarism

If plagiarized, what is missing or incorrect? __________________________________________

In general, our political culture seems to be increasingly populated by people who espouse outlandish or demonstrably false claims that often align with their political ideology . The good news is that psychologists and other social scientists are working hard to understand what prevents people from seeing through propaganda.

By understanding that people will make “false claims that often align with their political ideology,” researchers can continue to strive “to understand what prevents people from seeing through propaganda” (Pennycook and Rand).

A great deal of research in cognitive psychology has shown that a little bit of reasoning goes a long way toward forming accurate beliefs. For example, people who think more analytically (those who are more likely to exercise their analytic skills and not just trust their “gut” response) are less superstitious, less likely to believe in conspiracy theories and less receptive to seemingly profound but actually empty assertions (like “Wholeness quiets infinite phenomena”).

Research clearly identifies that reasoning can have a lasting impact on “forming accurate beliefs” and ward off inaccurate or misleading theories.

Fake news on Social Media: the Impact on Society

Open access
Published: 19 January 2022
Volume 26 , pages 443–458, ( 2024 )

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Femi Olan ORCID: orcid.org/0000-0002-7377-9882 1 ,
Uchitha Jayawickrama 2 ,
Emmanuel Ogiemwonyi Arakpogun 1 ,
Jana Suklan 3 &
Shaofeng Liu 4

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Fake news (FN) on social media (SM) rose to prominence in 2016 during the United States of America presidential election, leading people to question science, true news (TN), and societal norms. FN is increasingly affecting societal values, changing opinions on critical issues and topics as well as redefining facts, truths, and beliefs. To understand the degree to which FN has changed society and the meaning of FN, this study proposes a novel conceptual framework derived from the literature on FN, SM, and societal acceptance theory. The conceptual framework is developed into a meta-framework that analyzes survey data from 356 respondents. This study explored fuzzy set-theoretic comparative analysis; the outcomes of this research suggest that societies are split on differentiating TN from FN. The results also show splits in societal values. Overall, this study provides a new perspective on how FN on SM is disintegrating societies and replacing TN with FN.

How to Identify Fake News on Social Media: A Systematic Literature Review

Effects of Conspiracy Thinking Style, Framing and Political Interest on Accuracy of Fake News Recognition by Social Media Users: Evidence from Russia, Kazakhstan and Ukraine

Analyzing the Antecedents of Fake News Sharing in Online Social Networks

Avoid common mistakes on your manuscript.

1 Introduction

In cascading news and sensitive information, the fundamental principles are embedded in the concepts of truth as well as the theories of accuracy in communication (Brennen, 2017 ; Dwivedi et al., 2018 ; Orso et al., 2020 ; Pennycook et al., 2020 ). However, in the past five years or so, social media (SM) has redefined the structure, dimensions, and complexity of the news (Berkowitz & Schwartz, 2016 ; Copeland, 2007 ; Kim & Lyon, 2014 ). The impact of SM, specifically on political affairs, has been attracting more interest, as SM platforms, notably Twitter, Facebook, and Instagram, enable the broad sharing of information and news (Vosoughi et al., 2018 ). In addition to providing information, another main purpose of SM is to enable people to engage in social interaction, communication, and entertainment (Hwang et al., 2011 ; Kuem et al., 2017 ). In particular, many SM posts are looking for support, where reposting aims to spread messages via the multiplicative effect. Consequently, this study purpose is to address the research problem and gap which suggest that SM platform providers are doing little in tackling the spread and cascading of FN on SM.

By providing unlimited access to a large amount of information, people can share different beliefs and values (George et al., 2018 ; Kim et al., 2019 ; Rubin, 2019 ). However, the risks and implications of this new resource remain unclear to most of the population. One such risk is fake news (FN). FN, although unvetted, has a credible and professional appearance, ensuring that people cannot always distinguish it from true news (TN) (Kumar et al., 2018 ). The effects of FN cut across the society, for example, the spread of FN on SM determines how governments, organizations, and people respond to events in the society. Majority of FN is targeted to a specific sample of the population with the aim of promoting a certain ideology by stimulating strong beliefs and polarizing society (Chen & Sharma, 2015 ). According to Kumar et al. ( 2018 ); Lundmark et al. ( 2017 ); Tandoc et al. ( 2019 ), a periodic review of FN on SM is thus required to limit discord and violence by groups or individuals in society.

FN has become a major part of SM, raising doubts about information credibility, quality, and verification. Studies investigating the influence of FN on SM have appeared in various fields such as digital media, journalism, and politics; however, in-depth analyses of the impact of FN on society remain scarce. Furthermore, despite the growing body of research on FN and SM —a significant factor in the fight against FN —(Tandoc et al., 2018 ), an adequate review of the impact of FN in SM on society is also lacking.

Hence, The aim of this study is to explore the role of SM platform providers in reducing the spread of FN in the society, as the research gap identified from previous studies (Kim & Dennis, 2019 ; Kim et al., 2019 ; Knight & Tsoukas, 2019 ; Roozenbeek & van der Linden, 2019 ) on the limited research on the impact of FN on the society, leading to this study finding answers to the following research questions (RQs):

RQ1. Why is FN cascading impacting negatively on the society?

RQ2. Are the big SM organizations taking actions in reducing FN cascading?

Based on the foregoing, this study provides a holistic view of the three focus areas (FN, SM, and societal acceptance) by reviewing research publications, case studies, and experts’ opinions to produce a conceptual framework, an insightful and comprehensive meta-framework. This study then analyzes the associations among the three distinct fields from theoretical and practical perspectives. These associations derived from the literature are tested using an analytic technique called fuzzy set analysis to show if they are supported, thereby indicating society’s efforts to combat FN. We find that people’s interpretations of what is TN or FN affect societal efforts to reduce the spread of FN.

The findings of this study contribute to research on FN on SM, specifically looking at societal impacts. They provide experts and researchers in these fields with insights into how communities are effectively combating the spread of FN and how to implement the useful ideas from this research to strengthen the inputs in tackling FN on SM. Further, the findings of this research not only provide support for the associations but demonstrate a model for societal strategies to manage the spread of FN as well as fact-checking and information verification, thus equipping society with the tools to recognize the differences between FN and TN.

The remaining sections in this study are organized as follows: the theoretical development of the conceptual meta-framework explains the literature for the concept of FN, SM, and societal acceptance. This is followed by researched method section that describes the data, analysis and presents the results of the study. Further, there is a discussion section on the results, implications of this study for research, practice, and the society, finally limitations and future research.

2 Theoretical Development of the Conceptual Meta-Framework

FN is shaped to replicate TN by mimicking its characteristics (i.e. accuracy, verifiability, brevity, balance, and truthfulness) to mislead the public (Han et al., 2017 ; Kim & Dennis, 2019 ; Kim et al., 2019 ). FN is not a new phenomenon, according to Burkhardt ( 2017 ), FN can be traced back to at least Roman times when the first Roman Emperor had to announce fake news to encourage Octavian to destroy the republican system. During the Roman period, there was no way of verifying and validating the authenticity of news, as challenging authority was classed as treason. The 20th century heralded a new era of numerous one-to-many communication modes such as newspapers, radio stations, and television stations, marking the beginning of misinformation in news (Aggarwal et al., 2012 ; Kim & Dennis, 2019 ; Kim et al., 2019 ; Knight & Tsoukas, 2019 ; Manski, 1993 ; Preti & Miotto, 2011 ; Roozenbeek & van der Linden, 2019 ). With the emergence of multimedia corporations, the content of FN has been gaining new audiences (Oestreicher-Singer & Zalmanson, 2013 ), and the arrival of the Internet towards the end of the century improved the phenomenon of FN (Kapoor et al., 2018 ). As technology advanced in the 21st century, SM arrived, multiplying the dissemination of FN using both one-to-many and many-to-many strategies.

2.1 Understanding FN

FN content, which is divided into individual opinions and scientific consensus on trending issues such as COVID-19, evolution, and climate change, has long existed (Knight & Tsoukas, 2019 ). However, constant changes in political strategies have fundamentally impacted how information is defined, viewed, and interpreted at all levels of communication (Massari, 2010 ). Aggarwal and colleagues argued that incorrect scientific, political, and belief-oriented information has significant causes and consequences on individuals that are more politically inclined and those aiming to drive their ideas to wider society (Aggarwal et al., 2012 ). Therefore, individuals actively seeking information are united in their pursuit of knowledge and political action (Aggarwal & Singh, 2013 ). It is impossible to change their values and beliefs, abandon old ways and accept the fact-checked news, new methods to enlightening individuals or people with similar beliefs to adopt new states to a degree of news verification and validation (Cao et al., 2015 ; Centeno et al., 2015 ; Kim & Lyon, 2014 ).

As FN is fundamentally built on untraced and misleading phenomena, experts and researchers have noted a rising interest in the development of fact-checking tools to spot the spread of FN content in society (Berkowitz & Schwartz, 2016 ; Hwang et al., 2011 ; Miranda et al., 2015 ; Miranda et al., 2016 ). However, despite the large investment in innovative tools for identifying, distinguishing, and reducing factual discrepancies (e.g., ‘Content Authentication’ by Adobe for spotting alterations to original content), the challenges concerning the spread of FN remain unresolved, as society continues to engage with, debate, and promote such content (Kwon et al., 2017 ; Pierri et al., 2020 ). Indeed, the gap between fact-checking and the fundamental values and beliefs of the public discourages people from promoting fact-checking rather than accepting the dangers of FN (Kim & Lyon, 2014 ; Lukyanenko et al., 2014 ). Therefore, these tools do little to reduce the spread of FN in practice.

2.2 SM and Society

SM provides an environment in which individuals can exchange personal, group, or popular interests to build relationships with people that have similar and/or diverging beliefs and values. For example, most people of a particular age group share similar interests courtesy of growing up in the same era (Gomez-Miranda et al., 2015 ; Lyon & Montgomery, 2015 ; Miller & Tucker, 2013 ; Nerur et al., 2008 ). People’s characteristics are often inherited from educational institutions, communities, and family lifestyles (Matook et al., 2015 ). Further, certain age groups continue to hold onto specific values and beliefs, as reflected in the public’s response to the 2016 and 2020 U.S. presidential election and the 2019 UK general election (Prosser et al., 2020 ; Wang et al., 2016 ). Accordingly, Venkatraman et al. ( 2018 ) argued that values and beliefs are passed down through family generations, making it possible for a group in society to continue to hold onto specific philosophies.

SM plays an important role in helping people reconnect with friends and families as well as find jobs and purchase products and services (Kim & Dennis, 2019 ; Leong et al., 2015 ; Lyon & Montgomery, 2015 ; Miller & Tucker, 2013 ; Nerur et al., 2008 ; Pierri et al., 2020 ). SM platforms are also channels for recruiting interested parties for the continuity and propagation of a long-held ideology. Moreover, people with common demographic attributes use the instant messaging services on SM to communicate more than those without such shared demographics (Baur, 2017 ). SM platforms are thus online services that mirror real-world activities (e.g., dating services from Facebook, live Instagram feeds from parties).

The societal acceptance strategy can reduce the spread of FN (Haigh et al., 2018 ; Lundmark et al., 2017 ; Lyon & Montgomery, 2015 ; Miller & Tucker, 2013 ; Nerur et al., 2008 ; Sommariva et al., 2018 ). However, the expansion of multiple access points for information and news sharing on SM platforms contributes more to the spread of falsity than reducing its impact. Nevertheless, societal acceptance is considered to be a game-changer for controlling the spread of FN by SM (Egelhofer & Lecheler, 2019 ). Some empirical studies have analyzed the spread and flow of FN online (Garg et al., 2011 ; Gray et al., 2011 ), but little research examines how human judgment can differentiate truth from falsity. To reduce the spread of FN in society, it is important to understand the triangle of FN, the relationships between the constructs from each circle, and the associations that bind the circles, and then analyze the strength of the relationship (Chang et al., 2015 ; Chen & Sharma, 2015 ; Matook et al., 2015 ).

2.3 Meta-framework on the Impact of FN

This study developed a meta-framework based on the literature on FN, SM, and societal acceptance. Each of these perspectives, depicted as circles in the meta-framework, discusses the constructs that contribute to defining the clusters in theory. The constructs that then emerge from each perspective are the foundation for the meta-framework discussing the relationships among their associations. This study further develops notations to define the associations. By combining the three defined circles, these perspectives provide a new theoretical framework, as previous studies have shown that feasibilities to conceptualize phenomenon are at a wide spectrum (Table 1 ).

This study adopted the epidemiological model as a suitable theory for discussing the meta-framework perspectives. In particular, it employed the conceptual model of the disease triangle. In the 1960 s, the disease triangle was developed by George McNew to understand the pathology and epidemiology of plants and their diseases (Scholthof, 2007 ). This model stated that for a disease to manifest, three fundamental elements are required: the environment; the infectious pathogen that carries the virus, bacteria, or other micro-organisms; and the host. In this study, FN is defined as an ‘infectious pathogen’, as it is an epidemic that consists of varieties of fake news (Pan et al., 2017 ). According to Scholthof ( 2007 ), the environment determines whether the infection can be controlled; here, as shown in Fig. 1 , SM is conceptualized as the environment, the hosts are the readers, individuals, and society.

Fake news triangle

SM as an environment for cascading of FN has a structure (Chen et al., 2015 ; Miller & Tucker, 2013 ; Scholthof, 2007 ). The aim of the SM structure is to generate contents that attract millions of views by re-sharing news or information targeting a set of specific viewers. As the contents are shared and attained a viral status in the society, SM organizations are leveraging increased profits (Mettler & Winter, 2016 ). Primarily, SM structure is designed on contents ranking system constructed by algorithm ranking techniques, the method of data management and significance leveling in data priority (Hamamreh & Awad, 2017 ). News and information are ranked in a methodological order that links constructing a natural distribution by connecting between nodes of the SM (Gerlach et al., 2015 ; Matook et al., 2015 ). To understand the ranking system in SM, each node is assigned a unique code by creating iterative process of weights in network, these weights are assigned according to the content structure of the SM node (Brennen, 2017 ; Burkhardt, 2017 ; Chen, 2018 ). According to Brennen ( 2017 ); Burkhardt ( 2017 ); Chang et al. ( 2014 ); Chen ( 2018 ); Maier et al. ( 2015 ); Massari ( 2010 ), SM as the environment for infectious contents like FN comprises of communication channels such as websites, mobile applications, and platforms that facititate relationship forming among users of contents with similar interest. Hence, the relevance of SM to various aspects of life is of high singficance to users, government policies, and the economy.

This is somewhat consistent with the argument of the Director-General of the World Health Organization (WHO) – Tedros Ghebreyesus – at a foreign policy and security expert submit held in Germany in February 2020 (Union, 2020 , May 19). Tedros argued that as the world continues to grapple with Covid-19 contagion, an ‘infodemic’ is emerging as FN continues to “spread faster and more” than Covid-19 (Africe, 2020 ). Given the speed of the spread of FN, infodemic can hinder the effectiveness of public health response while propagating confusion and distrust in the society.

As shown in Fig. 1 , the hosts interact with those who have similar interests in their SM groups or forums and thus recruit new believers to the environment (Haigh et al., 2018 ; Humprecht, 2019a ; Mettler & Winter, 2016 ; Roozenbeek & van der Linden, 2019 ; Rubin, 2019 ). These communities continue to grow as positive social networks expand. With the power of SM platforms, new groups are created that have a similar agenda, improving social learning and opportunities using SM platforms’ tools (Kwon et al., 2017 ). One of the purposes of these strategies and networks is to clamp down as quickly as possible on people perceived as outsiders that may uncover or expose their content and philosophies.

3 Research Method

3.1 research design and data collection.

This study carried out a longitudinal survey with online participants to test the relationships and associations in the proposed meta-framework. A cross-sectional online survey was conducted in 2019, survey was conducted using stratified sampling, with participants divided into groups based on their demographics, proficiency of using SM platforms, and interest in news and current affairs online. Table 2 shows participants’ profiles in terms of their gender, age, location, SM usage, and SM experience. The questionnaire was designed through the research gap and literature.

This study distributed the questionnaire to 2234 active engaging participants and received 546 surveys which included both partial and completed questionnaire, which accounts for a response rate of 24%, demonstrating that the response rate is consistent with previous studies (Arshad et al., 2014 ; Klashanov, 2018 ; Malik et al., 2020 ). This study sample size consists of participants from across the global, with North America accounting for 29% of the total survey which make up for the largest share in terms of participant size. Experience of using SM platforms show that 28% of the participants engage more than 5 times daily on the platforms while 22.7% accounting for participants with 5 to 6 years working the SM platforms.

3.2 Analytical Technique

According to Ragin ( 2013 ); Ragin and Pennings ( 2005 ), the fuzzy set theoretical approach can be used to evaluate theories, frameworks, and models with a deductive strategy driven by a positivist paradigm. Fuzzy set analysis is an emerging technique for management and social sciences, which has become more popular as the initial problems were overcome by introducing hybrid techniques of fuzzy set logic. This study adopts the relationship and association testing suggested by Ragin ( 2009 ) to test for Boolean expressions in the fuzzy set theoretical approach of the four intersections in Fig. 2 .

Integrated meta-framework

This study proposes an eight-step process flowchart consisting of four loop relationships (represented by the double line diamonds in Fig. 3 ) and three predictive relationships (represented by the single line diamonds) that shows the relationships used to discuss the outcomes of the analysis. The flowchart is described as follows:

Flow chart for the consistency analysis

A loop relationship for an expression that a solution pathway is reliable shows whether the consistency of the sufficiency analysis is greater than 0.7 of the solution pathways as defined in this paper for the consistency threshold analysis. Any relationship that falls below the set threshold is eliminated from further analysis testing, as this means that that relationship does not achieve acceptable reliability.

A loop relationship for an expression that a solution pathway is accepted shows whether the consistency of A1 is greater than 0.7. This statement suggests that any relationship that falls below the acceptable criteria in the solution pathway must be rejected.

A double line diamond relationship for a strongly supported expression shows whether the consistency of A2, A3, and A4 is less than or equal to 0.7. This statement suggests that any relationship that passes the acceptance criteria does not have significant contradictory proofs.

A single line diamond relationship for an expression not supported by itself (however, subsequent relationships can benefit) can be described by the consistency of A3, which is less than or equal to 0.7. Furthermore, A3 represents the type I consistency error, and it is usually below the acceptance threshold.

A loop relationship for an expression that a solution pathway is weakly supported shows whether the consistency of the sufficiency analysis that A1 is greater than A3 of the solution pathways, as defined for the consistency threshold analysis. Any relationship that falls below the set threshold is eliminated from further analysis, as the relationship does not achieve acceptable reliability.

A double line diamond relationship for a supported expression shows whether the consistency of A4 is less than or equal to 0.7. This statement suggests that any relationship that passes the acceptance criteria does not have a significant error during analysis and this supports classification.

A loop relationship for an expression that a solution pathway is not weakly supported shows whether the consistency of A2 is greater than 0.7. This statement suggests that any relationship that falls below the acceptable criteria in the solution pathway can be improved and there is weak support for classification.

A double line diamond relationship for a supported expression shows whether the consistency of A2 is greater than or equal to A4. This statement suggests that any relationship that passes the acceptance criteria and partially supports the conditions for A2 and A4 represents the type II consistency error; this is usually equal to or greater than the acceptance threshold.

4 Data Analysis and Results

According to Deutsch and Malmborg ( 1985 ), complementarity and equifinality, the two underlying features in the fuzzy set theoretic approach, display patterns of attributes and different results depending on the structure of the constructs. In addition, the attributes in the constructs are concerned with the present or absent conditions and associations formed during conceptualization, rather than isolating the attributes from the constructs. Furthermore, complementarity exists if there is proof that causal factors display a match in their attributes and the analysis shows a higher level in the results, while equifinality exists if at least two unidentical pathways known as causal factors show the same results (Herrera-Restrepo et al., 2016 ).

In Table 3 , the attributes of the constructs indicate the relationships that provide empirical evidence to reject or support the model. The results demonstrate that the relationships are mostly rejected. We find that a higher consistency level directly results in a higher reliability of the relationship. The three combinations of attributes in the sufficiency analysis show that the input efficiency either fails or passes the set consistency threshold requirement (consistency and coverage are 0.72 and 0.44, respectively).

In Table 4 , the relationships indicate support for the empirical findings. The results show that the attributes of the constructs have higher combined solution pathways than the attributes in Table 3 . The type II error (or false negative) is one form of contradiction ignored in Fig. 3 . These findings show the least likely attributes of the constructs, indicating the continuation of existing relationships as well as supporting the higher consistency level of the associations and stronger support for further relationships. Hence, this analysis can introduce additional causal conditions of similar attributes not yet shown in the current relationships by retracking to the relationship mapping data and finding common attributes in existing constructs. This may explain the undefined variance in the existing relationships.

Table 5 shows the combined solution pathways for consistency and coverage, indicating support for most of the attributes of the constructs. This indicates a type I error (or false positive) in the form of contradicting the variances in the relationships, while the higher consistency level of the associations supports the higher values that delimit the relationships. Therefore, unconfirmed attributes indicate a restriction of the current relationships.

In Table 6 , this combined solution pathway indicates that neither the predicted relationships nor the coverage by attributes’ definitions of the constructs are strongly supported in terms of societal acceptance and the challenges posed by FN on SM on society. Therefore, alternative variances, as understood by the society, are better-supporting conditions for the relationship’s definitions in A4. Five of the six pathways are equal to or greater than the defined threshold, indicating that the relationships between the constructs can benefit from trade-offs. Furthermore, there are similar results for the unique coverage, signaling a significantly high-efficiency input directly linked to the variance from the causal conditions.

To fully understand the A4 outcomes, it is important to discuss the outcomes from A1, A2, and A3 simultaneously. A1 and A2 are insufficient to support a high input efficiency, indicating that SM will fade-out without a correlation with FN. To have a high input efficiency, the combination of the two constructs is highly significant to the relationships. However, A3, which considers all the attributes in the societal acceptance constructs, rejects the associated attributes from A1, whereas it shows weak support for A2, which indicates that the conditions are peripheral or are unconcerned about the variance. This explains the weak support in the attributes of their relationships. The A4 outcome shows that this study considers the attributes of the relations between A1 and A2, as A3 can explain the outcomes of redefining and reducing the impact of both associations.

5 Discussion

The aim of this research was to carry out an investigation on the impact of FN on the society, the use of SM as a platform for cascading of information and news. Thus, this study further explore the conceptual model of disease triangle (Piccialli et al., 2021 ) which identify FN as infectious pathogen in Fig. 1 (SM platforms host and spread FN), without the societal acceptance, it is difficult to cascade information and news. Furthermore, FN as defined in this study holds three main features which are significant for the perceptions of the society: the contents of the news, the intentions of the news, and the verification of the news. Hence, the use of comparative technique (fsQCA analysis) to outline the findings as shown in this study auggesting that societal acceptance is important in understanding the impact of FN. To better understand FN, SM, and societal acceptance, this study developed a meta-framework and analyzed the relationships among the attributes of the three constructs within. An online survey with 356 participants was carried out with a stratified sample size to test the meta-framework, and the data collected from the survey process were further categorized as the relationships designed in the constructs. This study considered SM platforms and the activities stimuling cascading processes of FN, changing the societal acceptance through the lens of contents management.

In previous studies, SM platforms are increasingly changing business activities and strategies used in positioning new products and brands, also leading to mis-information in the society (Modgil et al., 2021 ; Parra et al., 2021 ; Piccialli et al., 2021 ), also analyzed the SM platforms as the environment for business and social transactions focusing on capturing the largest audiences for information cascading, this further the spread of FN through the use of cascading tools available on SM. According to (Dwivedi et al., 2018 ; Kim & Dennis, 2019 ; Kim et al., 2019 ), cascading of FN through the use of SM platforms is growing faster than anticipated. The results of this study identified focused areas that can reduce the spread of FN on SM.

The results gathered during data analysis of validated questionnaire demonstrated important contributions of this study to minimizing cascading of FN in the society. Thus, the evaluation of the three perspectives; FN, SM, and societal acceptance further enhanced into relationship mapping by considering the entities from each perspectives as shown in Fig. 2 . The results from Table 3 , suggest that the testing of the relationship A1: FN/USˑVA of FN perspective and the entities users and values of the societal perspective is rejected while the relationship A1: FN/USˑNW of FN perspective and the entities users and networks of the societal acceptance is supported. Furthermore, the outcomes in Table 3 concur with the disease triangle theory which discussed the pathology model for disease manifestation, stating that the three triangular elements for infectious pathogen must be present for disease to grow (Humprecht, 2019b ; Rubin, 2019 ; Sommariva et al., 2018 ). Hence, the relationship A1: FN/USˑVA of FN perspective and the entities users and values of the societal perspective lacks the environment (networks) for cascading of contents of FN.

Table 4 shows support for SM and societal acceptance perspectives relationship mapping, with constructs’ consistency and coverage meeting the set requirement in Fig. 3 . However, condition S1 and S2 for A2: SM/USˑVA and S1 for A2: SM/USˑNW were ignored from the result, suggesting that there are other sources of information such as true news, entertainment contents which users are engaging with on SM platforms. According to Kwon et al. ( 2017 ), SM platforms provide positive opportunities such as learning new skills, engaging with experienced individuals and mentors, and finding new friendship, directly impacting positively on the society.

The increase in the level of cascading of FN can be attributed to SM companies drive to upsurge the size of big data, leading to strategic end to end nodes multiplication (Haigh et al., 2018 ). This study demonstrates that the enabling environment for the spreading of FN is attributed to the structure and strategies of SM companies. As shown in Table 6 , when SM companies implement effective fact-checking tools on SM platforms, the traffic of FN is minimized and the impact on the society is reduced. The relevant role of SM companies is to ensure that verification and fact-checking are embedded into the process of retrieving news and information.

In summary, the findings of this study suggest that previous studies (Dwivedi et al., 2018 ; Kim et al., 2019 ; Malik et al., 2020 ; Modgil et al., 2021 ; Roozenbeek & van der Linden, 2019 ) demonstrated the gap for an investigation of the societal acceptance of contents available on SM. Our findings show that the societal acceptance of information and news is highly dependent on the verification and fact-checking features that are available on the SM platforms. Therefore, the research questions in this study outlined the need for fact-checking and verification of information and news most importantly FN on SM. The results of the complementarity assessments show that SM and societal acceptance did significantly influence cascading of contents towards users. Specifically, FN cascading spread faster than any other type of contents on SM as shown in Table 5 . With regards to societal acceptance, users distributions of FN contents unconsciously aid cascasding with the intention of spreading awareness about the situation surrounding FN events.

5.1 Theoretical Implications

This study builds on the theoretical knowledge in literature by making significant contribution to the understanding of the impact of FN and SM platforms on the society. According to studies (Abouzeid et al., 2021 ; Au et al., 2021 ; Dwivedi et al., 2018 ; Kim et al., 2019 ; Parra et al., 2021 ; Tran et al., 2021 ) with combined body of knowledge on misinformation, FN, SM, SM platforms, cascading of FN, and risks of misinformation, this study identifies three main themes in our contribution: FN, SM, and societal acceptance. Previous studies (Orso et al., 2020 ; Pennycook et al., 2020 ) have presented FN and SM concepts, however this study’s introduction of societal acceptance is a novel theoretical contribution. Furthermore, the lack of studies on the societal acceptance of cascading of FN have generated a theoretical gap in understanding FN, misinformation and SM. Therefore, the results in our paper filled the research gap by validating the proposed features of societal acceptance: users, networks, and values.

The findings of this study contribute to theory by using complementarity among FN, SM, and societal acceptance to explain their influence by evaluating all the attributes in the three constructs, building relationships, and presenting findings that identify the significance of each association to reduce the cascading of FN in society. Therefore, this research answers the call of studies (George et al., 2018 ; Miller & Tucker, 2013 ; Miranda et al., 2016 ) that have suggested further work on FN on SM. Further, this study explains the impact of FN on society by exploring the conditions in different scenarios and with different complementarity values. It also shows how SM (i.e., the environment) and users can strategically deploy all resources to tackle the cascading and spread of FN. Most importantly, fuzzy set theory provides a data analysis structure that shows complex causality, enabling this research to present empirical findings.

Theoretically speaking, the outcomes show the importance of fact-checking and managing cascading in reducing the spread of the contents of FN in the society. Also, the role of SM companies in continuance commitment to support the course of minizing the impact of FN. As of date, this is the first of study to develop a meta-framework to examine the impact of FN on the society distributed on the SM. This study argued that exploring fact-checking and managing cascading will provide a platform for SM companies to contributing in the challenging impact of FN on the society. This study finds that SM as a type of environment is equipped with the technological know-how to tackle the spread of FN. This is particularly so for large SM organizations such as Facebook whose main business is SM content. Therefore, investment in technological research and service innovation is becoming a priority. However, more investment is required for fact-checking and analyzing cascading news, meaning that SM organizations with technical research facilities are more likely to initiate rigorous fact-checking campaigns. Hence, profitability and market growth may be more important for implementing fact-checking and news-cascading technologies that benefit society.

5.2 Practical Implications

Based on the outcomes obtained from the complementarity of the fuzzy set, it is also important for the SM platform providers to continue to invest in the fact-checking and managing contents of FN that are influencing users perceptions. In addition, it is very important to manage the direct impact of FN contents on the society by increasing the amount of fact-checking and verification tools that are available on SM. For instance, vigorous campaigns on the important role of news and information verification across all SM platforms and ensuring that there is educating information about the impact of spreading FN on SM on the society at large. Also, SM organizations should implement safe technology such as real-time deletion of contents of FN to ensure a safer communication environment for the users. Furthermore, the distinguishing real news from fake news using aided technology will boost confidence in the society. The comprehensive theoretical review and in-depth empirical analysis of the complex casualty of FN on SM on society in this study allows SM organizations to consider their organizational strategies to reduce FN cascading and implement sustainable solutions. SM organizations should prioritize the allocation of resources toward measures that tackle the challenges FN poses to society as well as the cost, societal impact, and misinformation linked to regulations to halt the spread of FN.

5.3 Implications for Society

The in-depth empirical analysis conducted concerning the FN on SM and the societal impact, the study provides a platform to the SM users on how far the facts published on SM can be trusted and how to filter the FN from TN on SM. SM organizations such as Facebook and Twitter have invested in large to tackle the publishing of FN on social media while yet the FN has taken on SM drastically during certain urgent situations.

Following the countless challenges that arose around the world due to the FN published on SM and the societal impact, the SM organizations have taken larger steps in minimizing the FN before being published and open to the public. The flowchart for the consistency analysis can be used by SM organizations in analyzing the published news on SM to distinguish FN from TN. Thus, the negative impact caused by FN to users and their lives can be minimized. Despite the fact that steps been taken by the SM organizations, it is also users’ responsibility to filter TN from FN even if they are being posted on verified accounts, by fact-checking or using appropriate verification (Nagi, 2020 ).

6 Conclusions

The results from this study demonstrate that it is important for SM platform providers continue in their efforts to understand the risks of cascading of FN and the influence on the society at large. Hence, the implementation of fact-checking tools is significant in reducing the spread of FN, building of trust and confident in the society. SM platform providers should ensure that there is continuous monitoring of online activities triggered by spread of FN and also ensures periodic upgrade of fact-checking technologies to tackle new tricks and strategies used in cascading FN in the society (Modgil et al., 2021 ; Parra et al., 2021 ). Furthermore, fact-checking information and public awareness on how to verify news can be added to campaigns to support the affected societies in combating the impact of FN. The findings in our study demonstrate that societal acceptance is a powerful tool that can persuade the society to focus on achieving common goal. The role of the society is to adopt the strength in societal acceptance to drive positive cultural change that welcome fact-checking and verification of any form of news.

6.1 Limitations and Future Research Directions

This study, like other studies, has limitations that suggest future research directions. This study analyzed how three constructs, FN, SM, and societal acceptance, impact on society. Other constructs were not included in this study such as SM firms’ power, political strategies, and societal perceptions. In addition, our data collection focused on people who engage most frequently with SM; experts and SM analysts may be relevant for future research to examine. Given that previous researchers focus on cascading FN and fact-checking news content to distinguish TN from FN, the influence of fact-checking and analyzing FN cascading could be tested future research with new datasets. In this vein, this study did not consider the financial impact of FN on SM on society, which is another interesting area for future research.

This cross-sectional research aimed to provide an in-depth understanding of the relationships of the three studied topics by analyzing data from many demographics rather than from one location. Therefore, the findings of this study support generalization to many locations. However, since some studies consider the results from a single location, future research could compare the complementarity, consistency, and coverage of a single location with many locations, which would enrich the findings of this study.

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Olan, F., Jayawickrama, U., Arakpogun, E.O. et al. Fake news on Social Media: the Impact on Society. Inf Syst Front 26 , 443–458 (2024). https://doi.org/10.1007/s10796-022-10242-z

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The Impact of Fake News

Categories: Fake News

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Published: Jan 29, 2024

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The impact of fake news on society, the role of technology and social media in spreading fake news, causes and motivations behind the creation of fake news, combating fake news: solutions and strategies, references:.

Barthel, M., Stocking, G., & Matsa, K. E. (2016). Nearly eight-in-ten Reddit users get news on the site. Pew Research Center. https://www.journalism.org/2016/02/25/reddit-news/
Guess, A. M., Nyhan, B., & Reifler, J. (2020). Exposure to untrustworthy websites in the 2016 US election. Nature Human Behaviour, 4(5), 472-480.
Roozenbeek, J., & Van der Linden, S. (2019). The fake news game: actively inoculating against the risk of misinformation. Journal of Risk Research, 22(5), 570-580.

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Fake news, disinformation and misinformation in social media: a review

Esma aïmeur.

Department of Computer Science and Operations Research (DIRO), University of Montreal, Montreal, Canada

Sabrine Amri

Gilles brassard, associated data.

All the data and material are available in the papers cited in the references.

Online social networks (OSNs) are rapidly growing and have become a huge source of all kinds of global and local news for millions of users. However, OSNs are a double-edged sword. Although the great advantages they offer such as unlimited easy communication and instant news and information, they can also have many disadvantages and issues. One of their major challenging issues is the spread of fake news. Fake news identification is still a complex unresolved issue. Furthermore, fake news detection on OSNs presents unique characteristics and challenges that make finding a solution anything but trivial. On the other hand, artificial intelligence (AI) approaches are still incapable of overcoming this challenging problem. To make matters worse, AI techniques such as machine learning and deep learning are leveraged to deceive people by creating and disseminating fake content. Consequently, automatic fake news detection remains a huge challenge, primarily because the content is designed in a way to closely resemble the truth, and it is often hard to determine its veracity by AI alone without additional information from third parties. This work aims to provide a comprehensive and systematic review of fake news research as well as a fundamental review of existing approaches used to detect and prevent fake news from spreading via OSNs. We present the research problem and the existing challenges, discuss the state of the art in existing approaches for fake news detection, and point out the future research directions in tackling the challenges.

Introduction

Context and motivation.

Fake news, disinformation and misinformation have become such a scourge that Marcia McNutt, president of the National Academy of Sciences of the United States, is quoted to have said (making an implicit reference to the COVID-19 pandemic) “Misinformation is worse than an epidemic: It spreads at the speed of light throughout the globe and can prove deadly when it reinforces misplaced personal bias against all trustworthy evidence” in a joint statement of the National Academies 1 posted on July 15, 2021. Indeed, although online social networks (OSNs), also called social media, have improved the ease with which real-time information is broadcast; its popularity and its massive use have expanded the spread of fake news by increasing the speed and scope at which it can spread. Fake news may refer to the manipulation of information that can be carried out through the production of false information, or the distortion of true information. However, that does not mean that this problem is only created with social media. A long time ago, there were rumors in the traditional media that Elvis was not dead, 2 that the Earth was flat, 3 that aliens had invaded us, 4 , etc.

Therefore, social media has become nowadays a powerful source for fake news dissemination (Sharma et al. 2019 ; Shu et al. 2017 ). According to Pew Research Center’s analysis of the news use across social media platforms, in 2020, about half of American adults get news on social media at least sometimes, 5 while in 2018, only one-fifth of them say they often get news via social media. 6

Hence, fake news can have a significant impact on society as manipulated and false content is easier to generate and harder to detect (Kumar and Shah 2018 ) and as disinformation actors change their tactics (Kumar and Shah 2018 ; Micallef et al. 2020 ). In 2017, Snow predicted in the MIT Technology Review (Snow 2017 ) that most individuals in mature economies will consume more false than valid information by 2022.

Recent news on the COVID-19 pandemic, which has flooded the web and created panic in many countries, has been reported as fake. 7 For example, holding your breath for ten seconds to one minute is not a self-test for COVID-19 8 (see Fig. 1 ). Similarly, online posts claiming to reveal various “cures” for COVID-19 such as eating boiled garlic or drinking chlorine dioxide (which is an industrial bleach), were verified 9 as fake and in some cases as dangerous and will never cure the infection.

An external file that holds a picture, illustration, etc.
Object name is 13278_2023_1028_Fig1_HTML.jpg

Fake news example about a self-test for COVID-19 source: https://cdn.factcheck.org/UploadedFiles/Screenshot031120_false.jpg , last access date: 26-12-2022

Social media outperformed television as the major news source for young people of the UK and the USA. 10 Moreover, as it is easier to generate and disseminate news online than with traditional media or face to face, large volumes of fake news are produced online for many reasons (Shu et al. 2017 ). Furthermore, it has been reported in a previous study about the spread of online news on Twitter (Vosoughi et al. 2018 ) that the spread of false news online is six times faster than truthful content and that 70% of the users could not distinguish real from fake news (Vosoughi et al. 2018 ) due to the attraction of the novelty of the latter (Bovet and Makse 2019 ). It was determined that falsehood spreads significantly farther, faster, deeper and more broadly than the truth in all categories of information, and the effects are more pronounced for false political news than for false news about terrorism, natural disasters, science, urban legends, or financial information (Vosoughi et al. 2018 ).

Over 1 million tweets were estimated to be related to fake news by the end of the 2016 US presidential election. 11 In 2017, in Germany, a government spokesman affirmed: “We are dealing with a phenomenon of a dimension that we have not seen before,” referring to an unprecedented spread of fake news on social networks. 12 Given the strength of this new phenomenon, fake news has been chosen as the word of the year by the Macquarie dictionary both in 2016 13 and in 2018 14 as well as by the Collins dictionary in 2017. 15 , 16 Since 2020, the new term “infodemic” was coined, reflecting widespread researchers’ concern (Gupta et al. 2022 ; Apuke and Omar 2021 ; Sharma et al. 2020 ; Hartley and Vu 2020 ; Micallef et al. 2020 ) about the proliferation of misinformation linked to the COVID-19 pandemic.

The Gartner Group’s top strategic predictions for 2018 and beyond included the need for IT leaders to quickly develop Artificial Intelligence (AI) algorithms to address counterfeit reality and fake news. 17 However, fake news identification is a complex issue. (Snow 2017 ) questioned the ability of AI to win the war against fake news. Similarly, other researchers concurred that even the best AI for spotting fake news is still ineffective. 18 Besides, recent studies have shown that the power of AI algorithms for identifying fake news is lower than its ability to create it Paschen ( 2019 ). Consequently, automatic fake news detection remains a huge challenge, primarily because the content is designed to closely resemble the truth in order to deceive users, and as a result, it is often hard to determine its veracity by AI alone. Therefore, it is crucial to consider more effective approaches to solve the problem of fake news in social media.

Contribution

The fake news problem has been addressed by researchers from various perspectives related to different topics. These topics include, but are not restricted to, social science studies , which investigate why and who falls for fake news (Altay et al. 2022 ; Batailler et al. 2022 ; Sterret et al. 2018 ; Badawy et al. 2019 ; Pennycook and Rand 2020 ; Weiss et al. 2020 ; Guadagno and Guttieri 2021 ), whom to trust and how perceptions of misinformation and disinformation relate to media trust and media consumption patterns (Hameleers et al. 2022 ), how fake news differs from personal lies (Chiu and Oh 2021 ; Escolà-Gascón 2021 ), examine how can the law regulate digital disinformation and how governments can regulate the values of social media companies that themselves regulate disinformation spread on their platforms (Marsden et al. 2020 ; Schuyler 2019 ; Vasu et al. 2018 ; Burshtein 2017 ; Waldman 2017 ; Alemanno 2018 ; Verstraete et al. 2017 ), and argue the challenges to democracy (Jungherr and Schroeder 2021 ); Behavioral interventions studies , which examine what literacy ideas mean in the age of dis/mis- and malinformation (Carmi et al. 2020 ), investigate whether media literacy helps identification of fake news (Jones-Jang et al. 2021 ) and attempt to improve people’s news literacy (Apuke et al. 2022 ; Dame Adjin-Tettey 2022 ; Hameleers 2022 ; Nagel 2022 ; Jones-Jang et al. 2021 ; Mihailidis and Viotty 2017 ; García et al. 2020 ) by encouraging people to pause to assess credibility of headlines (Fazio 2020 ), promote civic online reasoning (McGrew 2020 ; McGrew et al. 2018 ) and critical thinking (Lutzke et al. 2019 ), together with evaluations of credibility indicators (Bhuiyan et al. 2020 ; Nygren et al. 2019 ; Shao et al. 2018a ; Pennycook et al. 2020a , b ; Clayton et al. 2020 ; Ozturk et al. 2015 ; Metzger et al. 2020 ; Sherman et al. 2020 ; Nekmat 2020 ; Brashier et al. 2021 ; Chung and Kim 2021 ; Lanius et al. 2021 ); as well as social media-driven studies , which investigate the effect of signals (e.g., sources) to detect and recognize fake news (Vraga and Bode 2017 ; Jakesch et al. 2019 ; Shen et al. 2019 ; Avram et al. 2020 ; Hameleers et al. 2020 ; Dias et al. 2020 ; Nyhan et al. 2020 ; Bode and Vraga 2015 ; Tsang 2020 ; Vishwakarma et al. 2019 ; Yavary et al. 2020 ) and investigate fake and reliable news sources using complex networks analysis based on search engine optimization metric (Mazzeo and Rapisarda 2022 ).

The impacts of fake news have reached various areas and disciplines beyond online social networks and society (García et al. 2020 ) such as economics (Clarke et al. 2020 ; Kogan et al. 2019 ; Goldstein and Yang 2019 ), psychology (Roozenbeek et al. 2020a ; Van der Linden and Roozenbeek 2020 ; Roozenbeek and van der Linden 2019 ), political science (Valenzuela et al. 2022 ; Bringula et al. 2022 ; Ricard and Medeiros 2020 ; Van der Linden et al. 2020 ; Allcott and Gentzkow 2017 ; Grinberg et al. 2019 ; Guess et al. 2019 ; Baptista and Gradim 2020 ), health science (Alonso-Galbán and Alemañy-Castilla 2022 ; Desai et al. 2022 ; Apuke and Omar 2021 ; Escolà-Gascón 2021 ; Wang et al. 2019c ; Hartley and Vu 2020 ; Micallef et al. 2020 ; Pennycook et al. 2020b ; Sharma et al. 2020 ; Roozenbeek et al. 2020b ), environmental science (e.g., climate change) (Treen et al. 2020 ; Lutzke et al. 2019 ; Lewandowsky 2020 ; Maertens et al. 2020 ), etc.

Interesting research has been carried out to review and study the fake news issue in online social networks. Some focus not only on fake news, but also distinguish between fake news and rumor (Bondielli and Marcelloni 2019 ; Meel and Vishwakarma 2020 ), while others tackle the whole problem, from characterization to processing techniques (Shu et al. 2017 ; Guo et al. 2020 ; Zhou and Zafarani 2020 ). However, they mostly focus on studying approaches from a machine learning perspective (Bondielli and Marcelloni 2019 ), data mining perspective (Shu et al. 2017 ), crowd intelligence perspective (Guo et al. 2020 ), or knowledge-based perspective (Zhou and Zafarani 2020 ). Furthermore, most of these studies ignore at least one of the mentioned perspectives, and in many cases, they do not cover other existing detection approaches using methods such as blockchain and fact-checking, as well as analysis on metrics used for Search Engine Optimization (Mazzeo and Rapisarda 2022 ). However, in our work and to the best of our knowledge, we cover all the approaches used for fake news detection. Indeed, we investigate the proposed solutions from broader perspectives (i.e., the detection techniques that are used, as well as the different aspects and types of the information used).

Therefore, in this paper, we are highly motivated by the following facts. First, fake news detection on social media is still in the early age of development, and many challenging issues remain that require deeper investigation. Hence, it is necessary to discuss potential research directions that can improve fake news detection and mitigation tasks. However, the dynamic nature of fake news propagation through social networks further complicates matters (Sharma et al. 2019 ). False information can easily reach and impact a large number of users in a short time (Friggeri et al. 2014 ; Qian et al. 2018 ). Moreover, fact-checking organizations cannot keep up with the dynamics of propagation as they require human verification, which can hold back a timely and cost-effective response (Kim et al. 2018 ; Ruchansky et al. 2017 ; Shu et al. 2018a ).

Our work focuses primarily on understanding the “fake news” problem, its related challenges and root causes, and reviewing automatic fake news detection and mitigation methods in online social networks as addressed by researchers. The main contributions that differentiate us from other works are summarized below:

We present the general context from which the fake news problem emerged (i.e., online deception)
We review existing definitions of fake news, identify the terms and features most commonly used to define fake news, and categorize related works accordingly.
We propose a fake news typology classification based on the various categorizations of fake news reported in the literature.
We point out the most challenging factors preventing researchers from proposing highly effective solutions for automatic fake news detection in social media.
We highlight and classify representative studies in the domain of automatic fake news detection and mitigation on online social networks including the key methods and techniques used to generate detection models.
We discuss the key shortcomings that may inhibit the effectiveness of the proposed fake news detection methods in online social networks.
We provide recommendations that can help address these shortcomings and improve the quality of research in this domain.

The rest of this article is organized as follows. We explain the methodology with which the studied references are collected and selected in Sect. 2 . We introduce the online deception problem in Sect. 3 . We highlight the modern-day problem of fake news in Sect. 4 , followed by challenges facing fake news detection and mitigation tasks in Sect. 5 . We provide a comprehensive literature review of the most relevant scholarly works on fake news detection in Sect. 6 . We provide a critical discussion and recommendations that may fill some of the gaps we have identified, as well as a classification of the reviewed automatic fake news detection approaches, in Sect. 7 . Finally, we provide a conclusion and propose some future directions in Sect. 8 .

Review methodology

This section introduces the systematic review methodology on which we relied to perform our study. We start with the formulation of the research questions, which allowed us to select the relevant research literature. Then, we provide the different sources of information together with the search and inclusion/exclusion criteria we used to select the final set of papers.

Research questions formulation

The research scope, research questions, and inclusion/exclusion criteria were established following an initial evaluation of the literature and the following research questions were formulated and addressed.

RQ1: what is fake news in social media, how is it defined in the literature, what are its related concepts, and the different types of it?
RQ2: What are the existing challenges and issues related to fake news?
RQ3: What are the available techniques used to perform fake news detection in social media?

Sources of information

We broadly searched for journal and conference research articles, books, and magazines as a source of data to extract relevant articles. We used the main sources of scientific databases and digital libraries in our search, such as Google Scholar, 19 IEEE Xplore, 20 Springer Link, 21 ScienceDirect, 22 Scopus, 23 ACM Digital Library. 24 Also, we screened most of the related high-profile conferences such as WWW, SIGKDD, VLDB, ICDE and so on to find out the recent work.

Search criteria

We focused our research over a period of ten years, but we made sure that about two-thirds of the research papers that we considered were published in or after 2019. Additionally, we defined a set of keywords to search the above-mentioned scientific databases since we concentrated on reviewing the current state of the art in addition to the challenges and the future direction. The set of keywords includes the following terms: fake news, disinformation, misinformation, information disorder, social media, detection techniques, detection methods, survey, literature review.

Study selection, exclusion and inclusion criteria

To retrieve relevant research articles, based on our sources of information and search criteria, a systematic keyword-based search was carried out by posing different search queries, as shown in Table 1 .

List of keywords for searching relevant articles

We discovered a primary list of articles. On the obtained initial list of studies, we applied a set of inclusion/exclusion criteria presented in Table 2 to select the appropriate research papers. The inclusion and exclusion principles are applied to determine whether a study should be included or not.

Inclusion and exclusion criteria

After reading the abstract, we excluded some articles that did not meet our criteria. We chose the most important research to help us understand the field. We reviewed the articles completely and found only 61 research papers that discuss the definition of the term fake news and its related concepts (see Table 4 ). We used the remaining papers to understand the field, reveal the challenges, review the detection techniques, and discuss future directions.

Classification of fake news definitions based on the used term and features

A brief introduction of online deception

The Cambridge Online Dictionary defines Deception as “ the act of hiding the truth, especially to get an advantage .” Deception relies on peoples’ trust, doubt and strong emotions that may prevent them from thinking and acting clearly (Aïmeur et al. 2018 ). We also define it in previous work (Aïmeur et al. 2018 ) as the process that undermines the ability to consciously make decisions and take convenient actions, following personal values and boundaries. In other words, deception gets people to do things they would not otherwise do. In the context of online deception, several factors need to be considered: the deceiver, the purpose or aim of the deception, the social media service, the deception technique and the potential target (Aïmeur et al. 2018 ; Hage et al. 2021 ).

Researchers are working on developing new ways to protect users and prevent online deception (Aïmeur et al. 2018 ). Due to the sophistication of attacks, this is a complex task. Hence, malicious attackers are using more complex tools and strategies to deceive users. Furthermore, the way information is organized and exchanged in social media may lead to exposing OSN users to many risks (Aïmeur et al. 2013 ).

In fact, this field is one of the recent research areas that need collaborative efforts of multidisciplinary practices such as psychology, sociology, journalism, computer science as well as cyber-security and digital marketing (which are not yet well explored in the field of dis/mis/malinformation but relevant for future research). Moreover, Ismailov et al. ( 2020 ) analyzed the main causes that could be responsible for the efficiency gap between laboratory results and real-world implementations.

In this paper, it is not in our scope of work to review online deception state of the art. However, we think it is crucial to note that fake news, misinformation and disinformation are indeed parts of the larger landscape of online deception (Hage et al. 2021 ).

Fake news, the modern-day problem

Fake news has existed for a very long time, much before their wide circulation became facilitated by the invention of the printing press. 25 For instance, Socrates was condemned to death more than twenty-five hundred years ago under the fake news that he was guilty of impiety against the pantheon of Athens and corruption of the youth. 26 A Google Trends Analysis of the term “fake news” reveals an explosion in popularity around the time of the 2016 US presidential election. 27 Fake news detection is a problem that has recently been addressed by numerous organizations, including the European Union 28 and NATO. 29

In this section, we first overview the fake news definitions as they were provided in the literature. We identify the terms and features used in the definitions, and we classify the latter based on them. Then, we provide a fake news typology based on distinct categorizations that we propose, and we define and compare the most cited forms of one specific fake news category (i.e., the intent-based fake news category).

Definitions of fake news

“Fake news” is defined in the Collins English Dictionary as false and often sensational information disseminated under the guise of news reporting, 30 yet the term has evolved over time and has become synonymous with the spread of false information (Cooke 2017 ).

The first definition of the term fake news was provided by Allcott and Gentzkow ( 2017 ) as news articles that are intentionally and verifiably false and could mislead readers. Then, other definitions were provided in the literature, but they all agree on the authenticity of fake news to be false (i.e., being non-factual). However, they disagree on the inclusion and exclusion of some related concepts such as satire , rumors , conspiracy theories , misinformation and hoaxes from the given definition. More recently, Nakov ( 2020 ) reported that the term fake news started to mean different things to different people, and for some politicians, it even means “news that I do not like.”

Hence, there is still no agreed definition of the term “fake news.” Moreover, we can find many terms and concepts in the literature that refer to fake news (Van der Linden et al. 2020 ; Molina et al. 2021 ) (Abu Arqoub et al. 2022 ; Allen et al. 2020 ; Allcott and Gentzkow 2017 ; Shu et al. 2017 ; Sharma et al. 2019 ; Zhou and Zafarani 2020 ; Zhang and Ghorbani 2020 ; Conroy et al. 2015 ; Celliers and Hattingh 2020 ; Nakov 2020 ; Shu et al. 2020c ; Jin et al. 2016 ; Rubin et al. 2016 ; Balmas 2014 ; Brewer et al. 2013 ; Egelhofer and Lecheler 2019 ; Mustafaraj and Metaxas 2017 ; Klein and Wueller 2017 ; Potthast et al. 2017 ; Lazer et al. 2018 ; Weiss et al. 2020 ; Tandoc Jr et al. 2021 ; Guadagno and Guttieri 2021 ), disinformation (Kapantai et al. 2021 ; Shu et al. 2020a , c ; Kumar et al. 2016 ; Bhattacharjee et al. 2020 ; Marsden et al. 2020 ; Jungherr and Schroeder 2021 ; Starbird et al. 2019 ; Ireton and Posetti 2018 ), misinformation (Wu et al. 2019 ; Shu et al. 2020c ; Shao et al. 2016 , 2018b ; Pennycook and Rand 2019 ; Micallef et al. 2020 ), malinformation (Dame Adjin-Tettey 2022 ) (Carmi et al. 2020 ; Shu et al. 2020c ), false information (Kumar and Shah 2018 ; Guo et al. 2020 ; Habib et al. 2019 ), information disorder (Shu et al. 2020c ; Wardle and Derakhshan 2017 ; Wardle 2018 ; Derakhshan and Wardle 2017 ), information warfare (Guadagno and Guttieri 2021 ) and information pollution (Meel and Vishwakarma 2020 ).

There is also a remarkable amount of disagreement over the classification of the term fake news in the research literature, as well as in policy (de Cock Buning 2018 ; ERGA 2018 , 2021 ). Some consider fake news as a type of misinformation (Allen et al. 2020 ; Singh et al. 2021 ; Ha et al. 2021 ; Pennycook and Rand 2019 ; Shao et al. 2018b ; Di Domenico et al. 2021 ; Sharma et al. 2019 ; Celliers and Hattingh 2020 ; Klein and Wueller 2017 ; Potthast et al. 2017 ; Islam et al. 2020 ), others consider it as a type of disinformation (de Cock Buning 2018 ) (Bringula et al. 2022 ; Baptista and Gradim 2022 ; Tsang 2020 ; Tandoc Jr et al. 2021 ; Bastick 2021 ; Khan et al. 2019 ; Shu et al. 2017 ; Nakov 2020 ; Shu et al. 2020c ; Egelhofer and Lecheler 2019 ), while others associate the term with both disinformation and misinformation (Wu et al. 2022 ; Dame Adjin-Tettey 2022 ; Hameleers et al. 2022 ; Carmi et al. 2020 ; Allcott and Gentzkow 2017 ; Zhang and Ghorbani 2020 ; Potthast et al. 2017 ; Weiss et al. 2020 ; Tandoc Jr et al. 2021 ; Guadagno and Guttieri 2021 ). On the other hand, some prefer to differentiate fake news from both terms (ERGA 2018 ; Molina et al. 2021 ; ERGA 2021 ) (Zhou and Zafarani 2020 ; Jin et al. 2016 ; Rubin et al. 2016 ; Balmas 2014 ; Brewer et al. 2013 ).

The existing terms can be separated into two groups. The first group represents the general terms, which are information disorder , false information and fake news , each of which includes a subset of terms from the second group. The second group represents the elementary terms, which are misinformation , disinformation and malinformation . The literature agrees on the definitions of the latter group, but there is still no agreed-upon definition of the first group. In Fig. 2 , we model the relationship between the most used terms in the literature.

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Modeling of the relationship between terms related to fake news

The terms most used in the literature to refer, categorize and classify fake news can be summarized and defined as shown in Table 3 , in which we capture the similarities and show the differences between the different terms based on two common key features, which are the intent and the authenticity of the news content. The intent feature refers to the intention behind the term that is used (i.e., whether or not the purpose is to mislead or cause harm), whereas the authenticity feature refers to its factual aspect. (i.e., whether the content is verifiably false or not, which we label as genuine in the second case). Some of these terms are explicitly used to refer to fake news (i.e., disinformation, misinformation and false information), while others are not (i.e., malinformation). In the comparison table, the empty dash (–) cell denotes that the classification does not apply.

A comparison between used terms based on intent and authenticity

In Fig. 3 , we identify the different features used in the literature to define fake news (i.e., intent, authenticity and knowledge). Hence, some definitions are based on two key features, which are authenticity and intent (i.e., news articles that are intentionally and verifiably false and could mislead readers). However, other definitions are based on either authenticity or intent. Other researchers categorize false information on the web and social media based on its intent and knowledge (i.e., when there is a single ground truth). In Table 4 , we classify the existing fake news definitions based on the used term and the used features . In the classification, the references in the cells refer to the research study in which a fake news definition was provided, while the empty dash (–) cells denote that the classification does not apply.

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The features used for fake news definition

Fake news typology

Various categorizations of fake news have been provided in the literature. We can distinguish two major categories of fake news based on the studied perspective (i.e., intention or content) as shown in Fig. 4 . However, our proposed fake news typology is not about detection methods, and it is not exclusive. Hence, a given category of fake news can be described based on both perspectives (i.e., intention and content) at the same time. For instance, satire (i.e., intent-based fake news) can contain text and/or multimedia content types of data (e.g., headline, body, image, video) (i.e., content-based fake news) and so on.

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Most researchers classify fake news based on the intent (Collins et al. 2020 ; Bondielli and Marcelloni 2019 ; Zannettou et al. 2019 ; Kumar et al. 2016 ; Wardle 2017 ; Shu et al. 2017 ; Kumar and Shah 2018 ) (see Sect. 4.2.2 ). However, other researchers (Parikh and Atrey 2018 ; Fraga-Lamas and Fernández-Caramés 2020 ; Hasan and Salah 2019 ; Masciari et al. 2020 ; Bakdash et al. 2018 ; Elhadad et al. 2019 ; Yang et al. 2019b ) focus on the content to categorize types of fake news through distinguishing the different formats and content types of data in the news (e.g., text and/or multimedia).

Recently, another classification was proposed by Zhang and Ghorbani ( 2020 ). It is based on the combination of content and intent to categorize fake news. They distinguish physical news content and non-physical news content from fake news. Physical content consists of the carriers and format of the news, and non-physical content consists of the opinions, emotions, attitudes and sentiments that the news creators want to express.

Content-based fake news category

According to researchers of this category (Parikh and Atrey 2018 ; Fraga-Lamas and Fernández-Caramés 2020 ; Hasan and Salah 2019 ; Masciari et al. 2020 ; Bakdash et al. 2018 ; Elhadad et al. 2019 ; Yang et al. 2019b ), forms of fake news may include false text such as hyperlinks or embedded content; multimedia such as false videos (Demuyakor and Opata 2022 ), images (Masciari et al. 2020 ; Shen et al. 2019 ), audios (Demuyakor and Opata 2022 ) and so on. Moreover, we can also find multimodal content (Shu et al. 2020a ) that is fake news articles and posts composed of multiple types of data combined together, for example, a fabricated image along with a text related to the image (Shu et al. 2020a ). In this category of fake news forms, we can mention as examples deepfake videos (Yang et al. 2019b ) and GAN-generated fake images (Zhang et al. 2019b ), which are artificial intelligence-based machine-generated fake content that are hard for unsophisticated social network users to identify.

The effects of these forms of fake news content vary on the credibility assessment, as well as sharing intentions which influences the spread of fake news on OSNs. For instance, people with little knowledge about the issue compared to those who are strongly concerned about the key issue of fake news tend to be easier to convince that the misleading or fake news is real, especially when shared via a video modality as compared to the text or the audio modality (Demuyakor and Opata 2022 ).

Intent-based Fake News Category

The most often mentioned and discussed forms of fake news according to researchers in this category include but are not restricted to clickbait , hoax , rumor , satire , propaganda , framing , conspiracy theories and others. In the following subsections, we explain these types of fake news as they were defined in the literature and undertake a brief comparison between them as depicted in Table 5 . The following are the most cited forms of intent-based types of fake news, and their comparison is based on what we suspect are the most common criteria mentioned by researchers.

A comparison between the different types of intent-based fake news

Clickbait refers to misleading headlines and thumbnails of content on the web (Zannettou et al. 2019 ) that tend to be fake stories with catchy headlines aimed at enticing the reader to click on a link (Collins et al. 2020 ). This type of fake news is considered to be the least severe type of false information because if a user reads/views the whole content, it is possible to distinguish if the headline and/or the thumbnail was misleading (Zannettou et al. 2019 ). However, the goal behind using clickbait is to increase the traffic to a website (Zannettou et al. 2019 ).

A hoax is a false (Zubiaga et al. 2018 ) or inaccurate (Zannettou et al. 2019 ) intentionally fabricated (Collins et al. 2020 ) news story used to masquerade the truth (Zubiaga et al. 2018 ) and is presented as factual (Zannettou et al. 2019 ) to deceive the public or audiences (Collins et al. 2020 ). This category is also known either as half-truth or factoid stories (Zannettou et al. 2019 ). Popular examples of hoaxes are stories that report the false death of celebrities (Zannettou et al. 2019 ) and public figures (Collins et al. 2020 ). Recently, hoaxes about the COVID-19 have been circulating through social media.

The term rumor refers to ambiguous or never confirmed claims (Zannettou et al. 2019 ) that are disseminated with a lack of evidence to support them (Sharma et al. 2019 ). This kind of information is widely propagated on OSNs (Zannettou et al. 2019 ). However, they are not necessarily false and may turn out to be true (Zubiaga et al. 2018 ). Rumors originate from unverified sources but may be true or false or remain unresolved (Zubiaga et al. 2018 ).

Satire refers to stories that contain a lot of irony and humor (Zannettou et al. 2019 ). It presents stories as news that might be factually incorrect, but the intent is not to deceive but rather to call out, ridicule, or to expose behavior that is shameful, corrupt, or otherwise “bad” (Golbeck et al. 2018 ). This is done with a fabricated story or by exaggerating the truth reported in mainstream media in the form of comedy (Collins et al. 2020 ). The intent behind satire seems kind of legitimate and many authors (such as Wardle (Wardle 2017 )) do include satire as a type of fake news as there is no intention to cause harm but it has the potential to mislead or fool people.

Also, Golbeck et al. ( 2018 ) mention that there is a spectrum from fake to satirical news that they found to be exploited by many fake news sites. These sites used disclaimers at the bottom of their webpages to suggest they were “satirical” even when there was nothing satirical about their articles, to protect them from accusations about being fake. The difference with a satirical form of fake news is that the authors or the host present themselves as a comedian or as an entertainer rather than a journalist informing the public (Collins et al. 2020 ). However, most audiences believed the information passed in this satirical form because the comedian usually projects news from mainstream media and frames them to suit their program (Collins et al. 2020 ).

Propaganda refers to news stories created by political entities to mislead people. It is a special instance of fabricated stories that aim to harm the interests of a particular party and, typically, has a political context (Zannettou et al. 2019 ). Propaganda was widely used during both World Wars (Collins et al. 2020 ) and during the Cold War (Zannettou et al. 2019 ). It is a consequential type of false information as it can change the course of human history (e.g., by changing the outcome of an election) (Zannettou et al. 2019 ). States are the main actors of propaganda. Recently, propaganda has been used by politicians and media organizations to support a certain position or view (Collins et al. 2020 ). Online astroturfing can be an example of the tools used for the dissemination of propaganda. It is a covert manipulation of public opinion (Peng et al. 2017 ) that aims to make it seem that many people share the same opinion about something. Astroturfing can affect different domains of interest, based on which online astroturfing can be mainly divided into political astroturfing, corporate astroturfing and astroturfing in e-commerce or online services (Mahbub et al. 2019 ). Propaganda types of fake news can be debunked with manual fact-based detection models such as the use of expert-based fact-checkers (Collins et al. 2020 ).

Framing refers to employing some aspect of reality to make content more visible, while the truth is concealed (Collins et al. 2020 ) to deceive and misguide readers. People will understand certain concepts based on the way they are coined and invented. An example of framing was provided by Collins et al. ( 2020 ): “suppose a leader X says “I will neutralize my opponent” simply meaning he will beat his opponent in a given election. Such a statement will be framed such as “leader X threatens to kill Y” and this framed statement provides a total misrepresentation of the original meaning.

Conspiracy Theories

Conspiracy theories refer to the belief that an event is the result of secret plots generated by powerful conspirators. Conspiracy belief refers to people’s adoption and belief of conspiracy theories, and it is associated with psychological, political and social factors (Douglas et al. 2019 ). Conspiracy theories are widespread in contemporary democracies (Sutton and Douglas 2020 ), and they have major consequences. For instance, lately and during the COVID-19 pandemic, conspiracy theories have been discussed from a public health perspective (Meese et al. 2020 ; Allington et al. 2020 ; Freeman et al. 2020 ).

Comparison Between Most Popular Intent-based Types of Fake News

Following a review of the most popular intent-based types of fake news, we compare them as shown in Table 5 based on the most common criteria mentioned by researchers in their definitions as listed below.

the intent behind the news, which refers to whether a given news type was mainly created to intentionally deceive people or not (e.g., humor, irony, entertainment, etc.);
the way that the news propagates through OSN, which determines the nature of the propagation of each type of fake news and this can be either fast or slow propagation;
the severity of the impact of the news on OSN users, which refers to whether the public has been highly impacted by the given type of fake news; the mentioned impact of each fake news type is mainly the proportion of the negative impact;
and the goal behind disseminating the news, which can be to gain popularity for a particular entity (e.g., political party), for profit (e.g., lucrative business), or other reasons such as humor and irony in the case of satire, spreading panic or anger, and manipulating the public in the case of hoaxes, made-up stories about a particular person or entity in the case of rumors, and misguiding readers in the case of framing.

However, the comparison provided in Table 5 is deduced from the studied research papers; it is our point of view, which is not based on empirical data.

We suspect that the most dangerous types of fake news are the ones with high intention to deceive the public, fast propagation through social media, high negative impact on OSN users, and complicated hidden goals and agendas. However, while the other types of fake news are less dangerous, they should not be ignored.

Moreover, it is important to highlight that the existence of the overlap in the types of fake news mentioned above has been proven, thus it is possible to observe false information that may fall within multiple categories (Zannettou et al. 2019 ). Here, we provide two examples by Zannettou et al. ( 2019 ) to better understand possible overlaps: (1) a rumor may also use clickbait techniques to increase the audience that will read the story; and (2) propaganda stories, as a special instance of a framing story.

Challenges related to fake news detection and mitigation

To alleviate fake news and its threats, it is crucial to first identify and understand the factors involved that continue to challenge researchers. Thus, the main question is to explore and investigate the factors that make it easier to fall for manipulated information. Despite the tremendous progress made in alleviating some of the challenges in fake news detection (Sharma et al. 2019 ; Zhou and Zafarani 2020 ; Zhang and Ghorbani 2020 ; Shu et al. 2020a ), much more work needs to be accomplished to address the problem effectively.

In this section, we discuss several open issues that have been making fake news detection in social media a challenging problem. These issues can be summarized as follows: content-based issues (i.e., deceptive content that resembles the truth very closely), contextual issues (i.e., lack of user awareness, social bots spreaders of fake content, and OSN’s dynamic natures that leads to the fast propagation), as well as the issue of existing datasets (i.e., there still no one size fits all benchmark dataset for fake news detection). These various aspects have proven (Shu et al. 2017 ) to have a great impact on the accuracy of fake news detection approaches.

Content-based issue, deceptive content

Automatic fake news detection remains a huge challenge, primarily because the content is designed in a way that it closely resembles the truth. Besides, most deceivers choose their words carefully and use their language strategically to avoid being caught. Therefore, it is often hard to determine its veracity by AI without the reliance on additional information from third parties such as fact-checkers.

Abdullah-All-Tanvir et al. ( 2020 ) reported that fake news tends to have more complicated stories and hardly ever make any references. It is more likely to contain a greater number of words that express negative emotions. This makes it so complicated that it becomes impossible for a human to manually detect the credibility of this content. Therefore, detecting fake news on social media is quite challenging. Moreover, fake news appears in multiple types and forms, which makes it hard and challenging to define a single global solution able to capture and deal with the disseminated content. Consequently, detecting false information is not a straightforward task due to its various types and forms Zannettou et al. ( 2019 ).

Contextual issues

Contextual issues are challenges that we suspect may not be related to the content of the news but rather they are inferred from the context of the online news post (i.e., humans are the weakest factor due to lack of user awareness, social bots spreaders, dynamic nature of online social platforms and fast propagation of fake news).

Humans are the weakest factor due to the lack of awareness

Recent statistics 31 show that the percentage of unintentional fake news spreaders (people who share fake news without the intention to mislead) over social media is five times higher than intentional spreaders. Moreover, another recent statistic 32 shows that the percentage of people who were confident about their ability to discern fact from fiction is ten times higher than those who were not confident about the truthfulness of what they are sharing. As a result, we can deduce the lack of human awareness about the ascent of fake news.

Public susceptibility and lack of user awareness (Sharma et al. 2019 ) have always been the most challenging problem when dealing with fake news and misinformation. This is a complex issue because many people believe almost everything on the Internet and the ones who are new to digital technology or have less expertise may be easily fooled (Edgerly et al. 2020 ).

Moreover, it has been widely proven (Metzger et al. 2020 ; Edgerly et al. 2020 ) that people are often motivated to support and accept information that goes with their preexisting viewpoints and beliefs, and reject information that does not fit in as well. Hence, Shu et al. ( 2017 ) illustrate an interesting correlation between fake news spread and psychological and cognitive theories. They further suggest that humans are more likely to believe information that confirms their existing views and ideological beliefs. Consequently, they deduce that humans are naturally not very good at differentiating real information from fake information.

Recent research by Giachanou et al. ( 2020 ) studies the role of personality and linguistic patterns in discriminating between fake news spreaders and fact-checkers. They classify a user as a potential fact-checker or a potential fake news spreader based on features that represent users’ personality traits and linguistic patterns used in their tweets. They show that leveraging personality traits and linguistic patterns can improve the performance in differentiating between checkers and spreaders.

Furthermore, several researchers studied the prevalence of fake news on social networks during (Allcott and Gentzkow 2017 ; Grinberg et al. 2019 ; Guess et al. 2019 ; Baptista and Gradim 2020 ) and after (Garrett and Bond 2021 ) the 2016 US presidential election and found that individuals most likely to engage with fake news sources were generally conservative-leaning, older, and highly engaged with political news.

Metzger et al. ( 2020 ) examine how individuals evaluate the credibility of biased news sources and stories. They investigate the role of both cognitive dissonance and credibility perceptions in selective exposure to attitude-consistent news information. They found that online news consumers tend to perceive attitude-consistent news stories as more accurate and more credible than attitude-inconsistent stories.

Similarly, Edgerly et al. ( 2020 ) explore the impact of news headlines on the audience’s intent to verify whether given news is true or false. They concluded that participants exhibit higher intent to verify the news only when they believe the headline to be true, which is predicted by perceived congruence with preexisting ideological tendencies.

Luo et al. ( 2022 ) evaluate the effects of endorsement cues in social media on message credibility and detection accuracy. Results showed that headlines associated with a high number of likes increased credibility, thereby enhancing detection accuracy for real news but undermining accuracy for fake news. Consequently, they highlight the urgency of empowering individuals to assess both news veracity and endorsement cues appropriately on social media.

Moreover, misinformed people are a greater problem than uninformed people (Kuklinski et al. 2000 ), because the former hold inaccurate opinions (which may concern politics, climate change, medicine) that are harder to correct. Indeed, people find it difficult to update their misinformation-based beliefs even after they have been proved to be false (Flynn et al. 2017 ). Moreover, even if a person has accepted the corrected information, his/her belief may still affect their opinion (Nyhan and Reifler 2015 ).

Falling for disinformation may also be explained by a lack of critical thinking and of the need for evidence that supports information (Vilmer et al. 2018 ; Badawy et al. 2019 ). However, it is also possible that people choose misinformation because they engage in directionally motivated reasoning (Badawy et al. 2019 ; Flynn et al. 2017 ). Online clients are normally vulnerable and will, in general, perceive web-based networking media as reliable, as reported by Abdullah-All-Tanvir et al. ( 2019 ), who propose to mechanize fake news recognition.

It is worth noting that in addition to bots causing the outpouring of the majority of the misrepresentations, specific individuals are also contributing a large share of this issue (Abdullah-All-Tanvir et al. 2019 ). Furthermore, Vosoughi et al. (Vosoughi et al. 2018 ) found that contrary to conventional wisdom, robots have accelerated the spread of real and fake news at the same rate, implying that fake news spreads more than the truth because humans, not robots, are more likely to spread it.

In this case, verified users and those with numerous followers were not necessarily responsible for spreading misinformation of the corrupted posts (Abdullah-All-Tanvir et al. 2019 ).

Viral fake news can cause much havoc to our society. Therefore, to mitigate the negative impact of fake news, it is important to analyze the factors that lead people to fall for misinformation and to further understand why people spread fake news (Cheng et al. 2020 ). Measuring the accuracy, credibility, veracity and validity of news contents can also be a key countermeasure to consider.

Social bots spreaders

Several authors (Shu et al. 2018b , 2017 ; Shi et al. 2019 ; Bessi and Ferrara 2016 ; Shao et al. 2018a ) have also shown that fake news is likely to be created and spread by non-human accounts with similar attributes and structure in the network, such as social bots (Ferrara et al. 2016 ). Bots (short for software robots) exist since the early days of computers. A social bot is a computer algorithm that automatically produces content and interacts with humans on social media, trying to emulate and possibly alter their behavior (Ferrara et al. 2016 ). Although they are designed to provide a useful service, they can be harmful, for example when they contribute to the spread of unverified information or rumors (Ferrara et al. 2016 ). However, it is important to note that bots are simply tools created and maintained by humans for some specific hidden agendas.

Social bots tend to connect with legitimate users instead of other bots. They try to act like a human with fewer words and fewer followers on social media. This contributes to the forwarding of fake news (Jiang et al. 2019 ). Moreover, there is a difference between bot-generated and human-written clickbait (Le et al. 2019 ).

Many researchers have addressed ways of identifying and analyzing possible sources of fake news spread in social media. Recent research by Shu et al. ( 2020a ) describes social bots use of two strategies to spread low-credibility content. First, they amplify interactions with content as soon as it is created to make it look legitimate and to facilitate its spread across social networks. Next, they try to increase public exposure to the created content and thus boost its perceived credibility by targeting influential users that are more likely to believe disinformation in the hope of getting them to “repost” the fabricated content. They further discuss the social bot detection systems taxonomy proposed by Ferrara et al. ( 2016 ) which divides bot detection methods into three classes: (1) graph-based, (2) crowdsourcing and (3) feature-based social bot detection methods.

Similarly, Shao et al. ( 2018a ) examine social bots and how they promote the spread of misinformation through millions of Twitter posts during and following the 2016 US presidential campaign. They found that social bots played a disproportionate role in spreading articles from low-credibility sources by amplifying such content in the early spreading moments and targeting users with many followers through replies and mentions to expose them to this content and induce them to share it.

Ismailov et al. ( 2020 ) assert that the techniques used to detect bots depend on the social platform and the objective. They note that a malicious bot designed to make friends with as many accounts as possible will require a different detection approach than a bot designed to repeatedly post links to malicious websites. Therefore, they identify two models for detecting malicious accounts, each using a different set of features. Social context models achieve detection by examining features related to an account’s social presence including features such as relationships to other accounts, similarities to other users’ behaviors, and a variety of graph-based features. User behavior models primarily focus on features related to an individual user’s behavior, such as frequency of activities (e.g., number of tweets or posts per time interval), patterns of activity and clickstream sequences.

Therefore, it is crucial to consider bot detection techniques to distinguish bots from normal users to better leverage user profile features to detect fake news.

However, there is also another “bot-like” strategy that aims to massively promote disinformation and fake content in social platforms, which is called bot farms or also troll farms. It is not social bots, but it is a group of organized individuals engaging in trolling or bot-like promotion of narratives in a coordinated fashion (Wardle 2018 ) hired to massively spread fake news or any other harmful content. A prominent troll farm example is the Russia-based Internet Research Agency (IRA), which disseminated inflammatory content online to influence the outcome of the 2016 U.S. presidential election. 33 As a result, Twitter suspended accounts connected to the IRA and deleted 200,000 tweets from Russian trolls (Jamieson 2020 ). Another example to mention in this category is review bombing (Moro and Birt 2022 ). Review bombing refers to coordinated groups of people massively performing the same negative actions online (e.g., dislike, negative review/comment) on an online video, game, post, product, etc., in order to reduce its aggregate review score. The review bombers can be both humans and bots coordinated in order to cause harm and mislead people by falsifying facts.

Dynamic nature of online social platforms and fast propagation of fake news

Sharma et al. ( 2019 ) affirm that the fast proliferation of fake news through social networks makes it hard and challenging to assess the information’s credibility on social media. Similarly, Qian et al. ( 2018 ) assert that fake news and fabricated content propagate exponentially at the early stage of its creation and can cause a significant loss in a short amount of time (Friggeri et al. 2014 ) including manipulating the outcome of political events (Liu and Wu 2018 ; Bessi and Ferrara 2016 ).

Moreover, while analyzing the way source and promoters of fake news operate over the web through multiple online platforms, Zannettou et al. ( 2019 ) discovered that false information is more likely to spread across platforms (18% appearing on multiple platforms) compared to real information (11%).

Furthermore, recently, Shu et al. ( 2020c ) attempted to understand the propagation of disinformation and fake news in social media and found that such content is produced and disseminated faster and easier through social media because of the low barriers that prevent doing so. Similarly, Shu et al. ( 2020b ) studied hierarchical propagation networks for fake news detection. They performed a comparative analysis between fake and real news from structural, temporal and linguistic perspectives. They demonstrated the potential of using these features to detect fake news and they showed their effectiveness for fake news detection as well.

Lastly, Abdullah-All-Tanvir et al. ( 2020 ) note that it is almost impossible to manually detect the sources and authenticity of fake news effectively and efficiently, due to its fast circulation in such a small amount of time. Therefore, it is crucial to note that the dynamic nature of the various online social platforms, which results in the continued rapid and exponential propagation of such fake content, remains a major challenge that requires further investigation while defining innovative solutions for fake news detection.

Datasets issue

The existing approaches lack an inclusive dataset with derived multidimensional information to detect fake news characteristics to achieve higher accuracy of machine learning classification model performance (Nyow and Chua 2019 ). These datasets are primarily dedicated to validating the machine learning model and are the ultimate frame of reference to train the model and analyze its performance. Therefore, if a researcher evaluates their model based on an unrepresentative dataset, the validity and the efficiency of the model become questionable when it comes to applying the fake news detection approach in a real-world scenario.

Moreover, several researchers (Shu et al. 2020d ; Wang et al. 2020 ; Pathak and Srihari 2019 ; Przybyla 2020 ) believe that fake news is diverse and dynamic in terms of content, topics, publishing methods and media platforms, and sophisticated linguistic styles geared to emulate true news. Consequently, training machine learning models on such sophisticated content requires large-scale annotated fake news data that are difficult to obtain (Shu et al. 2020d ).

Therefore, datasets are also a great topic to work on to enhance data quality and have better results while defining our solutions. Adversarial learning techniques (e.g., GAN, SeqGAN) can be used to provide machine-generated data that can be used to train deeper models and build robust systems to detect fake examples from the real ones. This approach can be used to counter the lack of datasets and the scarcity of data available to train models.

Fake news detection literature review

Fake news detection in social networks is still in the early stage of development and there are still challenging issues that need further investigation. This has become an emerging research area that is attracting huge attention.

There are various research studies on fake news detection in online social networks. Few of them have focused on the automatic detection of fake news using artificial intelligence techniques. In this section, we review the existing approaches used in automatic fake news detection, as well as the techniques that have been adopted. Then, a critical discussion built on a primary classification scheme based on a specific set of criteria is also emphasized.

Categories of fake news detection

In this section, we give an overview of most of the existing automatic fake news detection solutions adopted in the literature. A recent classification by Sharma et al. ( 2019 ) uses three categories of fake news identification methods. Each category is further divided based on the type of existing methods (i.e., content-based, feedback-based and intervention-based methods). However, a review of the literature for fake news detection in online social networks shows that the existing studies can be classified into broader categories based on two major aspects that most authors inspect and make use of to define an adequate solution. These aspects can be considered as major sources of extracted information used for fake news detection and can be summarized as follows: the content-based (i.e., related to the content of the news post) and the contextual aspect (i.e., related to the context of the news post).

Consequently, the studies we reviewed can be classified into three different categories based on the two aspects mentioned above (the third category is hybrid). As depicted in Fig. 5 , fake news detection solutions can be categorized as news content-based approaches, the social context-based approaches that can be divided into network and user-based approaches, and hybrid approaches. The latter combines both content-based and contextual approaches to define the solution.

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Classification of fake news detection approaches

News Content-based Category

News content-based approaches are fake news detection approaches that use content information (i.e., information extracted from the content of the news post) and that focus on studying and exploiting the news content in their proposed solutions. Content refers to the body of the news, including source, headline, text and image-video, which can reflect subtle differences.

Researchers of this category rely on content-based detection cues (i.e., text and multimedia-based cues), which are features extracted from the content of the news post. Text-based cues are features extracted from the text of the news, whereas multimedia-based cues are features extracted from the images and videos attached to the news. Figure 6 summarizes the most widely used news content representation (i.e., text and multimedia/images) and detection techniques (i.e., machine learning (ML), deep Learning (DL), natural language processing (NLP), fact-checking, crowdsourcing (CDS) and blockchain (BKC)) in news content-based category of fake news detection approaches. Most of the reviewed research works based on news content for fake news detection rely on the text-based cues (Kapusta et al. 2019 ; Kaur et al. 2020 ; Vereshchaka et al. 2020 ; Ozbay and Alatas 2020 ; Wang 2017 ; Nyow and Chua 2019 ; Hosseinimotlagh and Papalexakis 2018 ; Abdullah-All-Tanvir et al. 2019 , 2020 ; Mahabub 2020 ; Bahad et al. 2019 ; Hiriyannaiah et al. 2020 ) extracted from the text of the news content including the body of the news and its headline. However, a few researchers such as Vishwakarma et al. ( 2019 ) and Amri et al. ( 2022 ) try to recognize text from the associated image.

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News content-based category: news content representation and detection techniques

Most researchers of this category rely on artificial intelligence (AI) techniques (such as ML, DL and NLP models) to improve performance in terms of prediction accuracy. Others use different techniques such as fact-checking, crowdsourcing and blockchain. Specifically, the AI- and ML-based approaches in this category are trying to extract features from the news content, which they use later for content analysis and training tasks. In this particular case, the extracted features are the different types of information considered to be relevant for the analysis. Feature extraction is considered as one of the best techniques to reduce data size in automatic fake news detection. This technique aims to choose a subset of features from the original set to improve classification performance (Yazdi et al. 2020 ).

Table 6 lists the distinct features and metadata, as well as the used datasets in the news content-based category of fake news detection approaches.

The features and datasets used in the news content-based approaches

a https://www.kaggle.com/anthonyc1/gathering-real-news-for-oct-dec-2016 , last access date: 26-12-2022

b https://mediabiasfactcheck.com/ , last access date: 26-12-2022

c https://github.com/KaiDMML/FakeNewsNet , last access date: 26-12-2022

d https://www.kaggle.com/anthonyc1/gathering-real-news-for-oct-dec-2016 , last access date: 26-12-2022

e https://www.cs.ucsb.edu/~william/data/liar_dataset.zip , last access date: 26-12-2022

f https://www.kaggle.com/mrisdal/fake-news , last access date: 26-12-2022

g https://github.com/BuzzFeedNews/2016-10-facebook-fact-check , last access date: 26-12-2022

h https://www.politifact.com/subjects/fake-news/ , last access date: 26-12-2022

i https://www.kaggle.com/rchitic17/real-or-fake , last access date: 26-12-2022

j https://www.kaggle.com/jruvika/fake-news-detection , last access date: 26-12-2022

k https://github.com/MKLab-ITI/image-verification-corpus , last access date: 26-12-2022

l https://drive.google.com/file/d/14VQ7EWPiFeGzxp3XC2DeEHi-BEisDINn/view , last access date: 26-12-2022

Social Context-based Category

Unlike news content-based solutions, the social context-based approaches capture the skeptical social context of the online news (Zhang and Ghorbani 2020 ) rather than focusing on the news content. The social context-based category contains fake news detection approaches that use the contextual aspects (i.e., information related to the context of the news post). These aspects are based on social context and they offer additional information to help detect fake news. They are the surrounding data outside of the fake news article itself, where they can be an essential part of automatic fake news detection. Some useful examples of contextual information may include checking if the news itself and the source that published it are credible, checking the date of the news or the supporting resources, and checking if any other online news platforms are reporting the same or similar stories (Zhang and Ghorbani 2020 ).

Social context-based aspects can be classified into two subcategories, user-based and network-based, and they can be used for context analysis and training tasks in the case of AI- and ML-based approaches. User-based aspects refer to information captured from OSN users such as user profile information (Shu et al. 2019b ; Wang et al. 2019c ; Hamdi et al. 2020 ; Nyow and Chua 2019 ; Jiang et al. 2019 ) and user behavior (Cardaioli et al. 2020 ) such as user engagement (Uppada et al. 2022 ; Jiang et al. 2019 ; Shu et al. 2018b ; Nyow and Chua 2019 ) and response (Zhang et al. 2019a ; Qian et al. 2018 ). Meanwhile, network-based aspects refer to information captured from the properties of the social network where the fake content is shared and disseminated such as news propagation path (Liu and Wu 2018 ; Wu and Liu 2018 ) (e.g., propagation times and temporal characteristics of propagation), diffusion patterns (Shu et al. 2019a ) (e.g., number of retweets, shares), as well as user relationships (Mishra 2020 ; Hamdi et al. 2020 ; Jiang et al. 2019 ) (e.g., friendship status among users).

Figure 7 summarizes some of the most widely adopted social context representations, as well as the most used detection techniques (i.e., AI, ML, DL, fact-checking and blockchain), in the social context-based category of approaches.

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Social context-based category: social context representation and detection techniques

Table 7 lists the distinct features and metadata, the adopted detection cues, as well as the used datasets, in the context-based category of fake news detection approaches.

The features, detection cues and datasets used int the social context-based approaches

a https://www.dropbox.com/s/7ewzdrbelpmrnxu/rumdetect2017.zip , last access date: 26-12-2022 b https://snap.stanford.edu/data/ego-Twitter.html , last access date: 26-12-2022

Hybrid approaches

Most researchers are focusing on employing a specific method rather than a combination of both content- and context-based methods. This is because some of them (Wu and Rao 2020 ) believe that there still some challenging limitations in the traditional fusion strategies due to existing feature correlations and semantic conflicts. For this reason, some researchers focus on extracting content-based information, while others are capturing some social context-based information for their proposed approaches.

However, it has proven challenging to successfully automate fake news detection based on just a single type of feature (Ruchansky et al. 2017 ). Therefore, recent directions tend to do a mixture by using both news content-based and social context-based approaches for fake news detection.

Table 8 lists the distinct features and metadata, as well as the used datasets, in the hybrid category of fake news detection approaches.

The features and datasets used in the hybrid approaches

Fake news detection techniques

Another vision for classifying automatic fake news detection is to look at techniques used in the literature. Hence, we classify the detection methods based on the techniques into three groups:

Human-based techniques: This category mainly includes the use of crowdsourcing and fact-checking techniques, which rely on human knowledge to check and validate the veracity of news content.
Artificial Intelligence-based techniques: This category includes the most used AI approaches for fake news detection in the literature. Specifically, these are the approaches in which researchers use classical ML, deep learning techniques such as convolutional neural network (CNN), recurrent neural network (RNN), as well as natural language processing (NLP).
Blockchain-based techniques: This category includes solutions using blockchain technology to detect and mitigate fake news in social media by checking source reliability and establishing the traceability of the news content.

Human-based Techniques

One specific research direction for fake news detection consists of using human-based techniques such as crowdsourcing (Pennycook and Rand 2019 ; Micallef et al. 2020 ) and fact-checking (Vlachos and Riedel 2014 ; Chung and Kim 2021 ; Nyhan et al. 2020 ) techniques.

These approaches can be considered as low computational requirement techniques since both rely on human knowledge and expertise for fake news detection. However, fake news identification cannot be addressed solely through human force since it demands a lot of effort in terms of time and cost, and it is ineffective in terms of preventing the fast spread of fake content.

Crowdsourcing. Crowdsourcing approaches (Kim et al. 2018 ) are based on the “wisdom of the crowds” (Collins et al. 2020 ) for fake content detection. These approaches rely on the collective contributions and crowd signals (Tschiatschek et al. 2018 ) of a group of people for the aggregation of crowd intelligence to detect fake news (Tchakounté et al. 2020 ) and to reduce the spread of misinformation on social media (Pennycook and Rand 2019 ; Micallef et al. 2020 ).

Micallef et al. ( 2020 ) highlight the role of the crowd in countering misinformation. They suspect that concerned citizens (i.e., the crowd), who use platforms where disinformation appears, can play a crucial role in spreading fact-checking information and in combating the spread of misinformation.

Recently Tchakounté et al. ( 2020 ) proposed a voting system as a new method of binary aggregation of opinions of the crowd and the knowledge of a third-party expert. The aggregator is based on majority voting on the crowd side and weighted averaging on the third-party site.

Similarly, Huffaker et al. ( 2020 ) propose a crowdsourced detection of emotionally manipulative language. They introduce an approach that transforms classification problems into a comparison task to mitigate conflation content by allowing the crowd to detect text that uses manipulative emotional language to sway users toward positions or actions. The proposed system leverages anchor comparison to distinguish between intrinsically emotional content and emotionally manipulative language.

La Barbera et al. ( 2020 ) try to understand how people perceive the truthfulness of information presented to them. They collect data from US-based crowd workers, build a dataset of crowdsourced truthfulness judgments for political statements, and compare it with expert annotation data generated by fact-checkers such as PolitiFact.

Coscia and Rossi ( 2020 ) introduce a crowdsourced flagging system that consists of online news flagging. The bipolar model of news flagging attempts to capture the main ingredients that they observe in empirical research on fake news and disinformation.

Unlike the previously mentioned researchers who focus on news content in their approaches, Pennycook and Rand ( 2019 ) focus on using crowdsourced judgments of the quality of news sources to combat social media disinformation.

Fact-Checking. The fact-checking task is commonly manually performed by journalists to verify the truthfulness of a given claim. Indeed, fact-checking features are being adopted by multiple online social network platforms. For instance, Facebook 34 started addressing false information through independent fact-checkers in 2017, followed by Google 35 the same year. Two years later, Instagram 36 followed suit. However, the usefulness of fact-checking initiatives is questioned by journalists 37 , as well as by researchers such as Andersen and Søe ( 2020 ). On the other hand, work is being conducted to boost the effectiveness of these initiatives to reduce misinformation (Chung and Kim 2021 ; Clayton et al. 2020 ; Nyhan et al. 2020 ).

Most researchers use fact-checking websites (e.g., politifact.com, 38 snopes.com, 39 Reuters, 40 , etc.) as data sources to build their datasets and train their models. Therefore, in the following, we specifically review examples of solutions that use fact-checking (Vlachos and Riedel 2014 ) to help build datasets that can be further used in the automatic detection of fake content.

Yang et al. ( 2019a ) use PolitiFact fact-checking website as a data source to train, tune, and evaluate their model named XFake, on political data. The XFake system is an explainable fake news detector that assists end users to identify news credibility. The fakeness of news items is detected and interpreted considering both content and contextual (e.g., statements) information (e.g., speaker).

Based on the idea that fact-checkers cannot clean all data, and it must be a selection of what “matters the most” to clean while checking a claim, Sintos et al. ( 2019 ) propose a solution to help fact-checkers combat problems related to data quality (where inaccurate data lead to incorrect conclusions) and data phishing. The proposed solution is a combination of data cleaning and perturbation analysis to avoid uncertainties and errors in data and the possibility that data can be phished.

Tchechmedjiev et al. ( 2019 ) propose a system named “ClaimsKG” as a knowledge graph of fact-checked claims aiming to facilitate structured queries about their truth values, authors, dates, journalistic reviews and other kinds of metadata. “ClaimsKG” designs the relationship between vocabularies. To gather vocabularies, a semi-automated pipeline periodically gathers data from popular fact-checking websites regularly.

AI-based Techniques

Previous work by Yaqub et al. ( 2020 ) has shown that people lack trust in automated solutions for fake news detection However, work is already being undertaken to increase this trust, for instance by von der Weth et al. ( 2020 ).

Most researchers consider fake news detection as a classification problem and use artificial intelligence techniques, as shown in Fig. 8 . The adopted AI techniques may include machine learning ML (e.g., Naïve Bayes, logistic regression, support vector machine SVM), deep learning DL (e.g., convolutional neural networks CNN, recurrent neural networks RNN, long short-term memory LSTM) and natural language processing NLP (e.g., Count vectorizer, TF-IDF Vectorizer). Most of them combine many AI techniques in their solutions rather than relying on one specific approach.

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Examples of the most widely used AI techniques for fake news detection

Many researchers are developing machine learning models in their solutions for fake news detection. Recently, deep neural network techniques are also being employed as they are generating promising results (Islam et al. 2020 ). A neural network is a massively parallel distributed processor with simple units that can store important information and make it available for use (Hiriyannaiah et al. 2020 ). Moreover, it has been proven (Cardoso Durier da Silva et al. 2019 ) that the most widely used method for automatic detection of fake news is not simply a classical machine learning technique, but rather a fusion of classical techniques coordinated by a neural network.

Some researchers define purely machine learning models (Del Vicario et al. 2019 ; Elhadad et al. 2019 ; Aswani et al. 2017 ; Hakak et al. 2021 ; Singh et al. 2021 ) in their fake news detection approaches. The more commonly used machine learning algorithms (Abdullah-All-Tanvir et al. 2019 ) for classification problems are Naïve Bayes, logistic regression and SVM.

Other researchers (Wang et al. 2019c ; Wang 2017 ; Liu and Wu 2018 ; Mishra 2020 ; Qian et al. 2018 ; Zhang et al. 2020 ; Goldani et al. 2021 ) prefer to do a mixture of different deep learning models, without combining them with classical machine learning techniques. Some even prove that deep learning techniques outperform traditional machine learning techniques (Mishra et al. 2022 ). Deep learning is one of the most widely popular research topics in machine learning. Unlike traditional machine learning approaches, which are based on manually crafted features, deep learning approaches can learn hidden representations from simpler inputs both in context and content variations (Bondielli and Marcelloni 2019 ). Moreover, traditional machine learning algorithms almost always require structured data and are designed to “learn” to act by understanding labeled data and then use it to produce new results with more datasets, which requires human intervention to “teach them” when the result is incorrect (Parrish 2018 ), while deep learning networks rely on layers of artificial neural networks (ANN) and do not require human intervention, as multilevel layers in neural networks place data in a hierarchy of different concepts, which ultimately learn from their own mistakes (Parrish 2018 ). The two most widely implemented paradigms in deep neural networks are recurrent neural networks (RNN) and convolutional neural networks (CNN).

Still other researchers (Abdullah-All-Tanvir et al. 2019 ; Kaliyar et al. 2020 ; Zhang et al. 2019a ; Deepak and Chitturi 2020 ; Shu et al. 2018a ; Wang et al. 2019c ) prefer to combine traditional machine learning and deep learning classification, models. Others combine machine learning and natural language processing techniques. A few combine deep learning models with natural language processing (Vereshchaka et al. 2020 ). Some other researchers (Kapusta et al. 2019 ; Ozbay and Alatas 2020 ; Ahmed et al. 2020 ) combine natural language processing with machine learning models. Furthermore, others (Abdullah-All-Tanvir et al. 2019 ; Kaur et al. 2020 ; Kaliyar 2018 ; Abdullah-All-Tanvir et al. 2020 ; Bahad et al. 2019 ) prefer to combine all the previously mentioned techniques (i.e., ML, DL and NLP) in their approaches.

Table 11 , which is relegated to the Appendix (after the bibliography) because of its size, shows a comparison of the fake news detection solutions that we have reviewed based on their main approaches, the methodology that was used and the models.

Comparison of AI-based fake news detection techniques

Blockchain-based Techniques for Source Reliability and Traceability

Another research direction for detecting and mitigating fake news in social media focuses on using blockchain solutions. Blockchain technology is recently attracting researchers’ attention due to the interesting features it offers. Immutability, decentralization, tamperproof, consensus, record keeping and non-repudiation of transactions are some of the key features that make blockchain technology exploitable, not just for cryptocurrencies, but also to prove the authenticity and integrity of digital assets.

However, the proposed blockchain approaches are few in number and they are fundamental and theoretical approaches. Specifically, the solutions that are currently available are still in research, prototype, and beta testing stages (DiCicco and Agarwal 2020 ; Tchechmedjiev et al. 2019 ). Furthermore, most researchers (Ochoa et al. 2019 ; Song et al. 2019 ; Shang et al. 2018 ; Qayyum et al. 2019 ; Jing and Murugesan 2018 ; Buccafurri et al. 2017 ; Chen et al. 2018 ) do not specify which fake news type they are mitigating in their studies. They mention news content in general, which is not adequate for innovative solutions. For that, serious implementations should be provided to prove the usefulness and feasibility of this newly developing research vision.

Table 9 shows a classification of the reviewed blockchain-based approaches. In the classification, we listed the following:

The type of fake news that authors are trying to mitigate, which can be multimedia-based or text-based fake news.
The techniques used for fake news mitigation, which can be either blockchain only, or blockchain combined with other techniques such as AI, Data mining, Truth-discovery, Preservation metadata, Semantic similarity, Crowdsourcing, Graph theory and SIR model (Susceptible, Infected, Recovered).
The feature that is offered as an advantage of the given solution (e.g., Reliability, Authenticity and Traceability). Reliability is the credibility and truthfulness of the news content, which consists of proving the trustworthiness of the content. Traceability aims to trace and archive the contents. Authenticity consists of checking whether the content is real and authentic.

A checkmark ( ✓ ) in Table 9 denotes that the mentioned criterion is explicitly mentioned in the proposed solution, while the empty dash (–) cell for fake news type denotes that it depends on the case: The criterion was either not explicitly mentioned (e.g., fake news type) in the work or the classification does not apply (e.g., techniques/other).

A classification of popular blockchain-based approaches for fake news detection in social media

After reviewing the most relevant state of the art for automatic fake news detection, we classify them as shown in Table 10 based on the detection aspects (i.e., content-based, contextual, or hybrid aspects) and the techniques used (i.e., AI, crowdsourcing, fact-checking, blockchain or hybrid techniques). Hybrid techniques refer to solutions that simultaneously combine different techniques from previously mentioned categories (i.e., inter-hybrid methods), as well as techniques within the same class of methods (i.e., intra-hybrid methods), in order to define innovative solutions for fake news detection. A hybrid method should bring the best of both worlds. Then, we provide a discussion based on different axes.

Fake news detection approaches classification

News content-based methods

Most of the news content-based approaches consider fake news detection as a classification problem and they use AI techniques such as classical machine learning (e.g., regression, Bayesian) as well as deep learning (i.e., neural methods such as CNN and RNN). More specifically, classification of social media content is a fundamental task for social media mining, so that most existing methods regard it as a text categorization problem and mainly focus on using content features, such as words and hashtags (Wu and Liu 2018 ). The main challenge facing these approaches is how to extract features in a way to reduce the data used to train their models and what features are the most suitable for accurate results.

Researchers using such approaches are motivated by the fact that the news content is the main entity in the deception process, and it is a straightforward factor to analyze and use while looking for predictive clues of deception. However, detecting fake news only from the content of the news is not enough because the news is created in a strategic intentional way to mimic the truth (i.e., the content can be intentionally manipulated by the spreader to make it look like real news). Therefore, it is considered to be challenging, if not impossible, to identify useful features (Wu and Liu 2018 ) and consequently tell the nature of such news solely from the content.

Moreover, works that utilize only the news content for fake news detection ignore the rich information and latent user intelligence (Qian et al. 2018 ) stored in user responses toward previously disseminated articles. Therefore, the auxiliary information is deemed crucial for an effective fake news detection approach.

Social context-based methods

The context-based approaches explore the surrounding data outside of the news content, which can be an effective direction and has some advantages in areas where the content approaches based on text classification can run into issues. However, most existing studies implementing contextual methods mainly focus on additional information coming from users and network diffusion patterns. Moreover, from a technical perspective, they are limited to the use of sophisticated machine learning techniques for feature extraction, and they ignore the usefulness of results coming from techniques such as web search and crowdsourcing which may save much time and help in the early detection and identification of fake content.

Hybrid approaches can simultaneously model different aspects of fake news such as the content-based aspects, as well as the contextual aspect based on both the OSN user and the OSN network patterns. However, these approaches are deemed more complex in terms of models (Bondielli and Marcelloni 2019 ), data availability, and the number of features. Furthermore, it remains difficult to decide which information among each category (i.e., content-based and context-based information) is most suitable and appropriate to be used to achieve accurate and precise results. Therefore, there are still very few studies belonging to this category of hybrid approaches.

Early detection

As fake news usually evolves and spreads very fast on social media, it is critical and urgent to consider early detection directions. Yet, this is a challenging task to do especially in highly dynamic platforms such as social networks. Both news content- and social context-based approaches suffer from this challenging early detection of fake news.

Although approaches that detect fake news based on content analysis face this issue less, they are still limited by the lack of information required for verification when the news is in its early stage of spread. However, approaches that detect fake news based on contextual analysis are most likely to suffer from the lack of early detection since most of them rely on information that is mostly available after the spread of fake content such as social engagement, user response, and propagation patterns. Therefore, it is crucial to consider both trusted human verification and historical data as an attempt to detect fake content during its early stage of propagation.

Conclusion and future directions

In this paper, we introduced the general context of the fake news problem as one of the major issues of the online deception problem in online social networks. Based on reviewing the most relevant state of the art, we summarized and classified existing definitions of fake news, as well as its related terms. We also listed various typologies and existing categorizations of fake news such as intent-based fake news including clickbait, hoax, rumor, satire, propaganda, conspiracy theories, framing as well as content-based fake news including text and multimedia-based fake news, and in the latter, we can tackle deepfake videos and GAN-generated fake images. We discussed the major challenges related to fake news detection and mitigation in social media including the deceptiveness nature of the fabricated content, the lack of human awareness in the field of fake news, the non-human spreaders issue (e.g., social bots), the dynamicity of such online platforms, which results in a fast propagation of fake content and the quality of existing datasets, which still limits the efficiency of the proposed solutions. We reviewed existing researchers’ visions regarding the automatic detection of fake news based on the adopted approaches (i.e., news content-based approaches, social context-based approaches, or hybrid approaches) and the techniques that are used (i.e., artificial intelligence-based methods; crowdsourcing, fact-checking, and blockchain-based methods; and hybrid methods), then we showed a comparative study between the reviewed works. We also provided a critical discussion of the reviewed approaches based on different axes such as the adopted aspect for fake news detection (i.e., content-based, contextual, and hybrid aspects) and the early detection perspective.

To conclude, we present the main issues for combating the fake news problem that needs to be further investigated while proposing new detection approaches. We believe that to define an efficient fake news detection approach, we need to consider the following:

Our choice of sources of information and search criteria may have introduced biases in our research. If so, it would be desirable to identify those biases and mitigate them.
News content is the fundamental source to find clues to distinguish fake from real content. However, contextual information derived from social media users and from the network can provide useful auxiliary information to increase detection accuracy. Specifically, capturing users’ characteristics and users’ behavior toward shared content can be a key task for fake news detection.
Moreover, capturing users’ historical behavior, including their emotions and/or opinions toward news content, can help in the early detection and mitigation of fake news.
Furthermore, adversarial learning techniques (e.g., GAN, SeqGAN) can be considered as a promising direction for mitigating the lack and scarcity of available datasets by providing machine-generated data that can be used to train and build robust systems to detect the fake examples from the real ones.
Lastly, analyzing how sources and promoters of fake news operate over the web through multiple online platforms is crucial; Zannettou et al. ( 2019 ) discovered that false information is more likely to spread across platforms (18% appearing on multiple platforms) compared to valid information (11%).

Appendix: A Comparison of AI-based fake news detection techniques

This Appendix consists only in the rather long Table 11 . It shows a comparison of the fake news detection solutions based on artificial intelligence that we have reviewed according to their main approaches, the methodology that was used, and the models, as explained in Sect. 6.2.2 .

Author Contributions

The order of authors is alphabetic as is customary in the third author’s field. The lead author was Sabrine Amri, who collected and analyzed the data and wrote a first draft of the paper, all along under the supervision and tight guidance of Esma Aïmeur. Gilles Brassard reviewed, criticized and polished the work into its final form.

This work is supported in part by Canada’s Natural Sciences and Engineering Research Council.

Availability of data and material

Declarations.

On behalf of all authors, the corresponding author states that there is no conflict of interest.

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Contributor Information

Esma Aïmeur, Email: ac.laertnomu.ori@ruemia .

Sabrine Amri, Email: [email protected] .

Gilles Brassard, Email: ac.laertnomu.ori@drassarb .

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Misleading COVID-19 headlines from mainstream sources did more harm on Facebook than fake news

MIT Sloan Office of Communications

May 30, 2024

New MIT Sloan research shows that unflagged but misleading content on Facebook was less persuasive, but much more widely seen, and thus generated more COVID-19 vaccine skepticism than flagged misinformation.

CAMBRIDGE, Mass., May 30, 2024 – Since the rollout of the COVID-19 vaccine in 2021, fake news on social media has been widely blamed for low vaccine uptake in the United States — but research by MIT Sloan School of Management PhD candidate Jennifer Allen and Professor David Rand finds that the blame lies elsewhere.

In a new paper published in Science and co-authored by Duncan J. Watts of the University of Pennsylvania, the researchers introduce a new methodology for measuring social media content’s causal impact at scale. They show that misleading content from mainstream news sources — rather than outright misinformation or “fake news” — was the primary driver of vaccine hesitancy on Facebook.

A new approach to estimating impact

“Misinformation has been correlated with many societal challenges, but there’s not a lot of research showing that exposure to misinformation actually causes harm,” explained Allen.

During the COVID-19 pandemic, for example, the spread of misinformation related to the virus and vaccine received significant public attention. However, existing research has, for the most part, only established correlations between vaccine refusal and factors such as sharing misinformation online — and largely overlooked the role of “vaccine-skeptical” content, which was potentially misleading but not flagged as misinformation by Facebook fact-checkers.

To address that gap, the researchers first asked a key question: What would be necessary for misinformation or any other type of content to have far-reaching impacts?

“To change behavior at scale, content has to not only be persuasive enough to convince people not to get the vaccine, but also widely seen,” Allen said. “Potential harm results from the combination of persuasion and exposure.”

To quantify content’s persuasive ability, the researchers conducted randomized experiments in which they showed thousands of survey participants the headlines from 130 vaccine-related stories — including both mainstream content and known misinformation — and tested how those headlines impacted their intentions to get vaccinated against COVID-19. Researchers also asked a separate group of respondents to rate the headlines across various attributes, including plausibility and political leaning. One factor reliably predicted impacts on vaccination intentions: the extent to which a headline suggested that the vaccine was harmful to a person’s health.

Using the “ wisdom of crowds ” and natural language processing AI tools, Allen and her co-authors extrapolated those survey results to predict the persuasive power of all 13,206 vaccine-related URLs that were widely viewed on Facebook in the first three months of the vaccine rollout. By combining these predictions with data from Facebook showing the number of users who viewed each URL, the researchers could predict each headline’s overall impact — the number of people it might have persuaded not to get the vaccine. The results were surprising.

The underestimated power of exposure

Contrary to popular perceptions, the researchers estimated that vaccine-skeptical content reduced vaccination intentions 46 times more than misinformation flagged by fact-checkers.

The reason? Even though flagged misinformation was more harmful when seen, it had relatively low reach. In total, the vaccine-related headlines in the Facebook data set received 2.7 billion views — but content flagged as misinformation received just 0.3% of those views, and content from domains rated as low-credibility received 5.1%.

“Even though the outright false content reduced vaccination intentions the most when viewed, comparatively few people saw it,” explained Rand. “Essentially, that means there’s this class of gray-area content that is less harmful per exposure but is seen far more often —and thus more impactful overall — that has been largely overlooked by both academics and social media companies.”

Notably, several of the most impactful URLs within the data set were articles from mainstream sources that cast doubt on the vaccine’s safety. For instance, the most-viewed was an article — from a well-regarded mainstream news source — suggesting that a medical doctor died two weeks after receiving the COVID-19 vaccine. This single headline received 54.9 million views — more than six times the combined views of all flagged misinformation.

While the body of this article did acknowledge the uncertainty of the doctor’s cause of death, its “clickbait” headline was highly suggestive and implied that the vaccine was likely responsible. That’s significant since the vast majority of viewers on social media likely never click out to read past the headline.

How journalists and social media platforms can help

According to Rand, one implication of this work is that media outlets need to take more care with their headlines, even if that means they aren’t as attention-grabbing.

“When you are writing a headline, you should not just be asking yourself if it’s false or not,” he said. “You should be asking yourself if the headline is likely to cause inaccurate perceptions.”

For platforms, added Allen, the research also points to the need for more nuanced moderation — across all subjects, not just public health.

“Content moderation focuses on identifying the most egregiously false information — but that may not be an effective way of identifying the most overall harmful content,” she says. “Platforms should also prioritize reviewing content from the people or organizations with the largest numbers of followers while balancing freedom of expression. We need to invest in more research and creative solutions in this space – for example, crowdsourced moderation tools like X’s Community Notes .”

“Content moderation decisions can be really difficult because of the inherent tension between wanting to mitigate harm and allowing people to express themselves,” Rand said. “Our paper introduces a framework to help balance that trade-off by allowing tech companies to actually quantify potential harm.”

And the trade-offs could be large. An exploratory analysis by the authors found that if Facebook users hadn’t been exposed to this vaccine-skeptical content, as many as 3 million more Americans could have been vaccinated.

“We can’t just ignore this gray area-content,” Allen concluded. “Lives could have been saved.”

About the MIT Sloan School of Management

The MIT Sloan School of Management is where smart, independent leaders come together to solve problems, create new organizations, and improve the world. Learn more at mitsloan.mit.edu .

Graphic image of cellphone with bullhorn announcing "Fake News."

Misleading COVID-19 headlines from mainstream sources did more harm on Facebook than fake news, study finds

S ince the rollout of the COVID-19 vaccine in 2021, fake news on social media has been widely blamed for low vaccine uptake in the United States—but research by MIT Sloan School of Management Ph.D. candidate Jennifer Allen and Professor David Rand finds that the blame lies elsewhere.

In a new paper published in Science and co-authored by Duncan J. Watts of the University of Pennsylvania, the researchers introduce a new methodology for measuring social media content's causal impact at scale. They show that misleading content from mainstream news sources—rather than outright misinformation or "fake news"—was the primary driver of vaccine hesitancy on Facebook.

A new approach to estimating impact

"Misinformation has been correlated with many societal challenges, but there's not a lot of research showing that exposure to misinformation actually causes harm," explained Allen.

During the COVID-19 pandemic, for example, the spread of misinformation related to the virus and vaccine received significant public attention. However, existing research has, for the most part, only established correlations between vaccine refusal and factors such as sharing misinformation online—and largely overlooked the role of "vaccine-skeptical" content, which was potentially misleading but not flagged as misinformation by Facebook fact-checkers.

To address that gap, the researchers first asked a key question: What would be necessary for misinformation or any other type of content to have far-reaching impacts?

"To change behavior at scale, content has to not only be persuasive enough to convince people not to get the vaccine, but also widely seen," Allen said. "Potential harm results from the combination of persuasion and exposure."

To quantify content's persuasive ability, the researchers conducted randomized experiments in which they showed thousands of survey participants the headlines from 130 vaccine-related stories—including both mainstream content and known misinformation—and tested how those headlines impacted their intentions to get vaccinated against COVID-19.

Researchers also asked a separate group of respondents to rate the headlines across various attributes, including plausibility and political leaning. One factor reliably predicted impacts on vaccination intentions: the extent to which a headline suggested that the vaccine was harmful to a person's health.

Using the "wisdom of crowds" and natural language processing AI tools, Allen and her co-authors extrapolated those survey results to predict the persuasive power of all 13,206 vaccine-related URLs that were widely viewed on Facebook in the first three months of the vaccine rollout.

By combining these predictions with data from Facebook showing the number of users who viewed each URL, the researchers could predict each headline's overall impact—the number of people it might have persuaded not to get the vaccine. The results were surprising.

The underestimated power of exposure

Contrary to popular perceptions, the researchers estimated that vaccine-skeptical content reduced vaccination intentions 46 times more than misinformation flagged by fact-checkers.

The reason? Even though flagged misinformation was more harmful when seen, it had relatively low reach. In total, the vaccine-related headlines in the Facebook data set received 2.7 billion views—but content flagged as misinformation received just 0.3% of those views, and content from domains rated as low-credibility received 5.1%.

"Even though the outright false content reduced vaccination intentions the most when viewed, comparatively few people saw it," explained Rand. "Essentially, that means there's this class of gray-area content that is less harmful per exposure but is seen far more often —and thus more impactful overall—that has been largely overlooked by both academics and social media companies."

Notably, several of the most impactful URLs within the data set were articles from mainstream sources that cast doubt on the vaccine's safety. For instance, the most-viewed was an article—from a well-regarded mainstream news source—suggesting that a medical doctor died two weeks after receiving the COVID-19 vaccine. This single headline received 54.9 million views—more than six times the combined views of all flagged misinformation.

While the body of this article did acknowledge the uncertainty of the doctor's cause of death, its "clickbait" headline was highly suggestive and implied that the vaccine was likely responsible. That's significant since the vast majority of viewers on social media likely never click out to read past the headline.

How journalists and social media platforms can help

According to Rand, one implication of this work is that media outlets need to take more care with their headlines, even if that means they aren't as attention-grabbing.

"When you are writing a headline, you should not just be asking yourself if it's false or not," he said. "You should be asking yourself if the headline is likely to cause inaccurate perceptions."

For platforms, added Allen, the research also points to the need for more nuanced moderation—across all subjects, not just public health.

"Content moderation focuses on identifying the most egregiously false information—but that may not be an effective way of identifying the most overall harmful content," she says. "Platforms should also prioritize reviewing content from the people or organizations with the largest numbers of followers while balancing freedom of expression. We need to invest in more research and creative solutions in this space—for example, crowdsourced moderation tools like X's Community Notes."

"Content moderation decisions can be really difficult because of the inherent tension between wanting to mitigate harm and allowing people to express themselves," Rand said. "Our paper introduces a framework to help balance that trade-off by allowing tech companies to actually quantify potential harm."

And the trade-offs could be large. An exploratory analysis by the authors found that if Facebook users hadn't been exposed to this vaccine-skeptical content, as many as 3 million more Americans could have been vaccinated.

"We can't just ignore this gray area-content," Allen concluded. "Lives could have been saved."

More information: Jennifer Allen, Quantifying the impact of misinformation and vaccine-skeptical content on Facebook, Science (2024). DOI: 10.1126/science.adk3451 . www.science.org/doi/10.1126/science.adk3451

Provided by MIT Sloan School of Management

Despite the greater potency of "fake news" on Facebook to discourage Americans from taking the COVID-19 vaccine, users' greater exposure to unflagged, vaccine-skeptical content meant the latter had a much greater negative effect on vaccine uptake. Credit: Jennifer Allen, Duncan Watts, David G. Rand

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A man wearing a black suit and tie walks with and a gloomy countenance down a tree-lined European street.

By Mike Hale

Hitler’s project: “Making Germany great again.” The Nazis’ characterization of criticism from the media: “Fake news.” Hitler’s mountain retreat in Berchtesgaden: “It’s sort of like Hitler’s Mar-a-Lago, if you will.”

Donald Trump’s name is not mentioned in the six episodes of “Hitler and the Nazis: Evil on Trial,” a new historical documentary series on Netflix. But it dances just beneath the surface, and occasionally, as in the examples above, the production’s cadre of scholars, popular historians and biographers can barely stop themselves from giving the game away.

The series was directed by the veteran documentarian Joe Berlinger (“Paradise Lost,” “Metallica: Some Kind of Monster”), who has a production deal with Netflix and has given it popular true-crime shows like “Jeffrey Epstein: Filthy Rich” and the “Conversations With a Killer” series.

In promotional material, Berlinger explains his decision to step up from true crime to total war and genocide: “This is the right time to retell this story for a younger generation as a cautionary tale,” he says, adding, “In America, we are in the midst of our own reckoning with democracy, with authoritarianism knocking at the door and a rise in antisemitism.” In other words, you can’t make a documentary about Germany in the 1930s and ’40s without holding the United States of the 2010s and ’20s in your mind.

To that end, Berlinger has made a deluxe version of the sort of history of Hitler, the Third Reich and the Holocaust that for years has been a staple of American cable television. The information is not new, but the resources available to Berlinger are reflected in the abundance of material he deploys across nearly six and a half hours: archival film, most of it meticulously colorized for the series, and audio; staged recreations with a sprawling cast of actors; and the copious roster of interviewees.

A new telling of an old story requires a twist, of course, and Berlinger has several. The American journalist William L. Shirer serves as the series’s unofficial narrator, despite having died in 1993 — an A.I. recreation of his voice recites passages from his many books about the period, and occasionally his actual voice is heard in excerpts from radio broadcasts. He is also represented onscreen by an actor in scenes recreating the series’s other primary framing device, the first Nuremberg trials in 1945.

Testimony from the trials is used to fill in the show’s accounts of political machinations, war making and mass killing. And the presentation of the trials is the most striking example of a visual style Berlinger employs throughout the series: sliding smoothly back and forth between elaborately staged recreations and real colorized footage, so that you need to pay attention to know whether you are looking at Hermann Goering or the actor playing Hermann Goering (Gabor Sotonyi). Berlinger is going for a seamless dramatic effect, and if it doesn’t always work as drama, it holds your attention.

Even the interviews are theatrical, shot on a darkened stage with blood-red curtains framing a ladder and what looks like a rough brick wall. It is unclear what the set dressing is meant to represent, but it might reflect Berlinger’s demonstrated tendency toward a kind of hushed sensationalism in the service of storytelling. That impulse comes through more clearly in some of the recreation, such as a scene of Jewish captives being shot at Babi Yar, or in the way the actor silently playing Hitler, Karoly Kozma, has been directed to play many of his scenes as if he were mid-seizure.

Much of the familiar material of a World War II documentary is missing or mentioned in passing, with events on the western front getting cursory attention. Berlinger is concerned with the development of Hitler’s psychology and worldview, and that takes the series on a track from the frustrations of his youth in Austria to his rise in 1930s Germany, and from there to the eastern front, the Soviet Union and the concentration camps in Germany and Poland.

The focus is on how the personal drives the political, and you can’t watch “Evil on Trial” without considering how Berlinger’s and his colleagues’ feelings about Trump and the hard right in the contemporary United States might have affected what they chose to emphasize in their portrait of Hitler and Nazi Germany.

But the unspoken case they build is comprehensive. We are shown Hitler tapping into the emotions stirred by a nation’s loss of power; playing to people who feel economically exploited and alienated from a liberal, urban culture; and uniting moderate and radical conservatives in fear of the far left. We see him demanding absolute loyalty and pitting subordinates against one another in battles for his favor. We see an absence of empathy and an inability to admit defeat. Shirer chimes in: “I began to comprehend it did not matter so much what he said, but how he said it. In such an atmosphere, every lie pronounced is accepted as high truth itself.”

Whether you find the case persuasive or not is probably beside the point, since the most salient feature of our current political landscape is that most Americans appear to have already made up their minds about he who — in the case of “Evil on Trial,” anyway — must not be named.

Mike Hale is a television critic for The Times. He also writes about online video, film and media. More about Mike Hale

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The Most Consequential TV Show in History

A new book about The Apprentice reveals how the 45th president was shaped by tawdry reality-TV culture.

Donald Trump in front of a sign for "The Apprentice"

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Produced by ElevenLabs and News Over Audio (NOA) using AI narration.

In a CNN interview shortly after launching his presidential campaign in 2015, Donald Trump told a skeptical Jake Tapper that he was “in it to win it” and boasted, “I’m giving up hundreds of millions of dollars to do this. I’m giving up a prime-time television show.” In fact, according to a new book, Trump wasn’t quite as confident as he claimed. For at least six months after he entered the race, he insisted on keeping the set for The Apprentice intact on the 14th floor of Trump Tower—if the whole presidential-campaign thing didn’t work out, at least it would generate good publicity for the next season of The Celebrity Apprentice . “There was a cognizant decision to leave the boardroom,” Trump’s son Eric told the book’s author, “and there was a possibility of it coming back.” When the set was eventually torn down, campaign staffers took over the floor.

This almost-too-perfect metaphor for the melding of Trump’s reality-TV and political careers appears in Apprentice in Wonderland , by the entertainment journalist Ramin Setoodeh. The book comes out later this month; I obtained an early copy.

It is by now a truism of the Trump era that the 45th president rose to power in large part thanks to the persona he popularized on The Apprentice , which he hosted from 2004 to 2015. Few readers will be surprised to learn that the character he played on the show—the tough-but-fair executive who doles out savvy business advice and decisively fires underperforming employees—was more reality-TV invention than reality. But the book’s peek behind the scenes of what is arguably the most consequential television show in history is still revealing. In Setoodeh’s look back at the series, Trump, a man who has now served in the most powerful office in the world, shows himself to be thoroughly steeped in the tawdry, lowbrow celebrity culture of the aughts—a culture that remains influential on his politics.

That the former president cooperated so extensively for a book about his reality-TV career is telling. According to an author’s note at the end of the book, Trump granted Setoodeh six interviews, four of them in person. That’s more than Trump has given to most of the people writing books about his presidency. Setoodeh writes that the interviews sometimes went on for hours, and that his subject seemed to thrill at watching old clips of the show. On the day Trump’s sister died in November 2023, Setoodeh assumed their scheduled interview would be canceled. But Trump proceeded as planned, alternating between taking personal phone calls and recounting old episodes of The Celebrity Apprentice to Setoodeh in the Mar-a-Lago living room. “In our days together,” Setoodeh writes, “Trump is happiest when he talks about The Apprentice and crankiest when he relives his years as the commander in chief.”

McKay Coppins: Why Republican politicians do whatever Trump says

The premise of The Apprentice was straightforward. On each episode, a cast of aspiring “employees,” who were divided into teams, competed in business-oriented challenges, after which Trump summoned the losing team to a boardroom and grilled them on their failures. At the end, he’d send a contestant home with his famous catchphrase: “You’re fired.”

The boardroom scenes became known for high drama and vitriolic sniping, and according to Setoodeh, Trump thrived on pitting the contestants against one another. The author reports that the dynamic was built into the set design, which placed Trump’s chair on a platform, allowing him to lord over the contestants competing for his approval. He hectored, humiliated, and bullied them—and only a small fraction of the interactions wound up on air. With Trump in charge, the filming of the boardroom scenes sometimes stretched on for hours, Setoodeh writes, leaving contestants exhausted and disoriented.

Trump also casually deployed racial division for entertainment, according to several contestants. In 2005, he publicly floated a segregated season of The Apprentice , in which “a team of successful African Americans” would compete against “a team of successful whites.” He argued at the time, “Whether people like that idea or not, it is somewhat reflective of our very vicious world.” The idea never came to fruition. But Setoodeh quotes Black contestants who say the show’s racial politics were already retrograde enough, and that they were rooted in Trump’s personal views.

Tara Dowdell, who appeared on Season 3, recalls producers trying to goad her during interviews into acting angry: “They wanted me to be a stereotype of a Black woman,” she told Setoodeh. Randall Pinkett, a Rhodes Scholar and the first Black winner of The Apprentice , is quoted as saying, “I think Donald’s a racist. And I think he consciously and unconsciously and deliberately cast Black people in a negative light.” In the show’s first season, Omarosa Manigault, who was the lone Black woman in the cast and later went on to serve in the Trump White House, was depicted as so cartoonishly dishonest and manipulative that her name became shorthand in the reality-TV industry for “villain.”

In response to an email detailing several of the claims in Setoodeh’s book, Steven Cheung, the communications director for Trump’s 2024 campaign, wrote, “These completely fabricated accusations and bullshit story was already peddled in 2016 and thoroughly debunked. Nobody took it seriously then, and they won’t now, because it’s fake news. Now that Crooked Joe Biden and the Democrats are losing the election, and President Trump continues to dominate, they are bringing up old fake stories from the past because they are desperate.”

The accusation of racism that has proved most persistent is the rumor that Trump was caught on a hot mic using the N-word during a taping of The Apprentice . Manigault said in 2018 that she’d heard a tape of Trump using the slur. Mark Burnett, the series creator, told Setoodeh it wasn’t true. Last week, Bill Pruitt, a former producer on the series, revived the allegation with an essay in Slate , writing that Trump, while discussing the contestant Kwame Jackson, asked aloud, “I mean, would America buy a n— winning?” In an interview with Setoodeh, Trump repeatedly denies that any tapes exist of him using what he calls “the race word.”

“Number one, it’s a word that I’ve never used. I’ve never used it in my life!” Trump says, before adding, “Would I use it when the mics are all hot? The mics were always hot.”

Megan Garber: Trump’s smoking gun is a dream that will never die

Apprentice in Wonderland also offers new details about the experience of being a woman on the set. It is perhaps not shocking that Trump—who brags in the book that he made the Miss Universe swimsuit competition skimpier by introducing bikinis—objectified female Apprentice contestants. One challenge that involved creating a customized shopping experience at Home Depot, Setoodeh writes, spawned a rumor among contestants that Trump had told one of them, Erin Elmore, “I’ll show you my nine-inch power tool.” (Elmore, who later became a Republican strategist and Trump-campaign surrogate, tells Setoodeh it didn’t happen.) And when Trump was alone with the male contestants in Season 4, Pinkett says, the host talked about how much he wanted to have sex with Jennifer Murphy, a 26-year-old beauty queen who was another cast member.

Murphy herself offers a detailed description of her various encounters with Trump. At first, she tells Setoodeh, the relationship was like that of a mentor and protégée. “I think he looked at me in a way like he does his daughter,” Murphy says. “But also, I did think he had the hots for me a bit.” She says that Trump unexpectedly kissed her one day while she was waiting for an elevator, and that on another occasion he invited her to his room at the Beverly Hills Hotel. She declined the invitation because he was married to his current wife, Melania. “I have a conscience,” Murphy tells Setoodeh. “I have integrity. I made up a reason I was busy.”

Murphy says she that wasn’t offended by Trump’s advances, and that she didn’t consider him a predator: “I think, if anything, he likes beautiful women too much—if that’s a flaw.” The two remained friends. When she got engaged to a celebrity dentist in 2006, Murphy recounts, Trump let her hold the wedding at one of his properties at a discount. He also joined her in filming an Access Hollywood segment about the nuptials. But at one point during the filming, she says, Trump pulled her aside and asked her why she was marrying her fiancé. “He put his arm around me,” Murphy tells Setoodeh. “It was off camera. I think he smacked my butt a little. I was like, ‘Goodness gracious!’”

Trump’s vulgar behavior wasn’t limited to backstage. During a Season 4 boardroom scene that made it to air, Setoodeh writes, Trump asked the 22-year-old contestant Adam Israelov if he’d ever had sex. Israelov said he wasn’t comfortable answering the question, but Trump wouldn’t let it go. “How can you be afraid to talk about sex? Sex is, like, not a big deal. How can you be afraid?” Trump kept pushing. “Listen, Adam isn’t good with sex. He might be in ten years, but right now you don’t feel comfortable with sex. Do you agree with it? Someday, you will. It’s gotten me into a lot of trouble, Adam. It’s cost me a lot of money.” (This was nearly two decades before Trump would be convicted on 34 felony counts related to a hush-money payment to an adult-film actor.)

Another moment of candor came during a meal in 2004 with the publishing executive Steve Forbes, who made a cameo on the show. Alex Thomason, a contestant, tells Setoodeh that he heard Trump critique Forbes’s failed presidential bids in 1996 and 2000. “You went overboard on this pro-life nonsense,” Thomason recalls Trump telling him.

By 2008, ratings for The Apprentice had fallen off dramatically enough that NBC needed a new gimmick, and The Celebrity Apprentice was born. According to Setoodeh, Trump wasn’t wild at first about surrounding himself with other famous people—he wanted to be the only celebrity on the show—but a network executive eventually warmed him up to the idea of lording over a boardroom full of C-listers. As Trump reflects on those seasons, though, he seems consumed primarily by how many of his celebrity friends have since abandoned him.

Speaking with Setoodeh, Trump neatly divides all of Hollywood into two categories—pro-Trump and anti-Trump—and shifts his assessments accordingly. (If this sounds familiar, that’s because it’s also how he talks about politicians.)

Tom Brady? When they were friends, Trump hailed the star quarterback as “ a great winner ” on the campaign trail. But after Brady visited the Biden White House and made a joke about election deniers, Trump was done with him. “He recommended crypto. That’s bad!” Trump tells Setoodeh. “Because he lost like $200 million in them. He was friends with this guy, [Sam] Bankman-Fried, and that’s not a good guy to be friends with right now.” (Brady was a paid “ambassador” for Bankman-Fried’s crypto company and reportedly lost tens of millions of dollars when it went bankrupt.)

Debra Messing? When the actor was (according to Trump, at least) effusively thanking him for saving NBC with his show’s massive ratings, he found her “quite attractive.” But once she became an outspoken critic of his politics, the attraction disappeared: “I watch her today, and it’s like she’s a raving mess.”

Trump seems to reserve special disdain for the Kardashians. He once happily advertised his coziness with reality TV’s most famous family. Kim Kardashian made a guest appearance on The Apprentice , and her sister Khloé was a contestant on The Celebrity Apprentice . Years later, when Trump was president, he hosted Kim at the White House and granted clemency to a federal prisoner for whom she’d advocated. But after Biden won the 2020 election, Kim celebrated by posting three blue heart emoji on Twitter—and that was apparently enough for Trump to turn on the whole family.

When Setoodeh mentions Kim, he rants: “She went for Sleepy Joe! Which is incredible to me. Incredible, because I did something that was perhaps important to her.” He dismisses her criminal-justice-reform activism: “Maybe it was just publicity for her. I don’t know.” When Khloé comes up, he says, “I never got along great with Khloé,” and then offers, unprompted, “Khloé was arrested for drunk driving. Did you know that?” (The arrest took place in 2007.) “I think it’s a terrible thing—so many people die with drunk driving. You don’t hear about it, but they do.” Trump even seems to disavow the Kardashians’ parent Caitlyn Jenner, who voted for him in 2016 but later spoke out against what she considered his administration’s transphobic policies. When Setoodeh asks Trump about Jenner, he says blankly, “I don’t know her. I knew Bruce. But I don’t know Caitlyn.”

Trump tells Setoodeh that he seriously considered leaving the show in 2012 to run for president, but that Burnett talked him out of it. “You don’t understand,” Trump recalls Burnett saying. “They’re offering you millions of dollars to be on a show, to be on primetime television.” That this argument won out suggests an answer to the question of which job— Apprentice host or president—Trump considered more prestigious, at least at the time. Still, he says he would have easily beaten Mitt Romney in the Republican primaries and done a better job running against Barack Obama. “He ran a horrible race,” Trump says of the 2012 GOP nominee, who’s since become a vocal Trump critic. “Do you know why? Because he was intimidated by African Americans … He’s a total asshole anyway. He’s a total schmuck.”

From the November 2023 issue: What Mitt Romney saw in the Senate

Four years later, when Trump finally left, he tried to get his daughter Ivanka installed as the host. Instead, NBC tapped Arnold Schwarzenegger to host The New Celebrity Apprentice , which debuted weeks before Trump was sworn in as president. Speaking with Setoodeh, Trump is gleeful that the show was canceled after one season. He claims that Schwarzenegger was incapable of saying Trump’s catchphrase properly during rehearsals, and so had to come up with his own pale imitation: “You’re terminated.”

“He didn’t have it,” Trump tells Setoodeh with a grin. “The whole thing was, like, ponderous. And I view that as a great compliment to myself.” He adds, “Arnold was a guy, he supported Crooked Hillary, so I didn’t give a shit. He was a [John] Kasich supporter too, which made it even worse. So between Kasich and Hillary, I said, ‘I hope he bombs like a dog,’ and he did.” (A Schwarzenegger spokesperson told me in a statement: “We aren’t going to get into this because we understand that 90% of what he says is untrue,” but added that Schwarzenegger used the phrase “You’re fired” in the 1994 movie True Lies , “years before Donald Trump was a reality star.”)

Setoodeh’s book contains so many anecdotes like this that one can’t help but marvel at how Trump manages to keep his catalog of petty celebrity snubs straight. He might struggle to define nuclear triad , but he can tell you which Apprentice contestants sided with Rosie O’Donnell over him in their 2006 feud. As unsavory as this world might be to some readers, the lessons Trump took from his reality-TV era permeated his presidency. Recall those early scenes from his White House: the boss enthroned behind the Resolute desk, pitting advisers against one another, firing Cabinet officials at will, nursing his grudges and grievances. Many presidential libraries feature replicas of the Oval Office; by the end of Setoodeh’s book, I wondered if Trump’s would include a model of the Apprentice boardroom.

“The show would be a big part of history,” Eric Trump tells Setoodeh. “It’s going to be a big part of his legacy. I hope it will remain a big part of his legacy.”

When you buy a book using a link on this page, we receive a commission. Thank you for supporting The Atlantic.

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