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300+ Social Media Research Topics

Social media has become an integral part of our lives, and it has transformed the way we communicate, share information, and interact with each other. As social media platforms continue to evolve and gain popularity, they have also become a rich source of data for researchers. Social media research is a rapidly growing field that encompasses a wide range of topics , from understanding the psychological and social effects of social media to analyzing patterns of user behavior and identifying trends in online conversations. In this era of data-driven decision-making, social media research is more important than ever, as it provides insights into how we use and are influenced by social media. In this post, we will explore some of the most fascinating and relevant social media research topics that are shaping our understanding of this powerful medium.

Social Media Research Topics

Social Media Research Topics are as follows:

• The effects of social media on mental health
• The role of social media in political polarization
• The impact of social media on relationships
• The use of social media by businesses for marketing
• The effects of social media on body image and self-esteem
• The influence of social media on consumer behavior
• The use of social media for education
• The effects of social media on language use and grammar
• The impact of social media on news consumption
• The role of social media in activism and social change
• The use of social media for job seeking and career development
• The effects of social media on sleep patterns
• The influence of social media on adolescent behavior
• The impact of social media on the spread of misinformation
• The use of social media for personal branding
• The effects of social media on political participation
• The influence of social media on fashion trends
• The impact of social media on sports fandom
• The use of social media for mental health support
• The effects of social media on creativity
• The role of social media in cultural exchange
• The impact of social media on language learning
• The use of social media for crisis communication
• The effects of social media on privacy and security
• The influence of social media on diet and exercise behavior
• The impact of social media on travel behavior
• The use of social media for citizen journalism
• The effects of social media on political accountability
• The role of social media in peer pressure
• The impact of social media on romantic relationships
• The use of social media for community building
• The effects of social media on gender identity
• The influence of social media on music consumption
• The impact of social media on academic performance
• The use of social media for social support
• The effects of social media on social skills
• The role of social media in disaster response
• The impact of social media on nostalgia and memory
• The use of social media for charity and philanthropy
• The effects of social media on political polarization in developing countries
• The influence of social media on literary consumption
• The impact of social media on family relationships
• The use of social media for citizen science
• The effects of social media on cultural identity
• The role of social media in promoting healthy behaviors
• The impact of social media on language diversity
• The use of social media for environmental activism
• The effects of social media on attention span
• The influence of social media on art consumption
• The impact of social media on cultural values and norms.
• The impact of social media on mental health
• The impact of social media on mental health.
• The impact of social media on body image and self-esteem.
• The use of social media for political activism and social justice movements.
• The role of social media in promoting cultural diversity and inclusivity.
• The impact of social media on romantic relationships and dating.
• The use of social media for customer service and support.
• The impact of social media on mental health and well-being among young adults.
• The impact of social media on political polarization and partisanship.
• The use of social media for health communication and behavior change.
• The role of social media in shaping public opinion and attitudes towards vaccination.
• The impact of social media on political participation and civic engagement.
• The impact of social media on political polarization and echo chambers.
• The use of social media for political campaigning and the manipulation of public opinion.
• The role of social media in shaping public attitudes towards vaccination and public health.
• The impact of social media on news consumption and trust in journalism.
• The use of social media for promoting sustainable fashion practices and ethical consumption.
• The role of social media in influencing beauty standards and body image.
• The impact of social media on the music industry and the role of social media influencers.
• The use of social media for promoting mental health and well-being among healthcare professionals.
• The role of social media in shaping public attitudes towards gun violence and gun control policies.
• The impact of social media on social activism and advocacy.
• The use of social media for promoting cross-cultural communication and intercultural understanding.
• The role of social media in shaping public attitudes towards climate change and environmental policies.
• The impact of social media on public health during the COVID-19 pandemic.
• The use of social media for promoting financial literacy and access to financial services for low-income individuals.
• The role of social media in shaping public attitudes towards immigration policies and refugee crises.
• The impact of social media on political activism and social movements.
• The use of social media for promoting digital literacy and technology education in developing countries.
• The role of social media in shaping public attitudes towards gender and sexual orientation.
• The impact of social media on consumer behavior in the food and beverage industry.
• The use of social media for promoting mental health and well-being among first responders.
• The role of social media in shaping public attitudes towards racial justice and police brutality.
• The impact of social media on privacy concerns and data security.
• The use of social media for promoting interfaith dialogue and religious tolerance.
• The role of social media in shaping public attitudes towards income inequality and economic justice.
• The impact of social media on the film and television industry and consumer behavior.
• The use of social media for promoting mental health and well-being among military personnel.
• The role of social media in shaping public attitudes towards privacy and data security.
• The impact of social media on the hospitality industry and consumer behavior.
• The use of social media for promoting intergenerational communication and understanding.
• The role of social media in shaping public attitudes towards animal welfare and animal rights.
• The impact of social media on the gaming industry and gamer behavior.
• The use of social media for promoting digital literacy and technology skills among seniors.
• The role of social media in shaping public attitudes towards renewable energy and sustainability.
• The impact of social media on the advertising industry and consumer behavior.
• The use of social media for promoting mental health and well-being among children and adolescents.
• The role of social media in shaping public attitudes towards online privacy and security.
• The impact of social media on the beauty industry and consumer behavior.
• The use of social media for promoting cultural preservation and heritage tourism.
• The role of social media in shaping public attitudes towards criminal justice reform.
• The impact of social media on the automotive industry and consumer behavior.
• The use of social media for promoting mental health and well-being among marginalized communities.
• The role of social media in shaping public attitudes towards sustainable development goals.
• The impact of social media on the fashion industry and consumer behavior.
• The use of social media for promoting intercultural communication in the workplace.
• The role of social media in shaping public attitudes towards mental health policies.
• The impact of social media on the travel industry and sustainable tourism practices.
• The use of social media for health information seeking and patient empowerment.
• The role of social media in promoting environmental activism and sustainable practices.
• The impact of social media on consumer behavior and brand loyalty.
• The use of social media for promoting education and lifelong learning.
• The role of social media in shaping public opinion and attitudes towards mental health issues.
• The impact of social media on the fashion industry and fast fashion practices.
• The use of social media for promoting social entrepreneurship and social innovation.
• The role of social media in shaping public opinion and attitudes towards gun control.
• The impact of social media on the mental health and well-being of adolescents.
• The use of social media for promoting intercultural exchange and understanding.
• The role of social media in shaping public opinion and attitudes towards climate change.
• The impact of social media on political advertising and campaign strategies.
• The use of social media for promoting healthy relationships and communication skills.
• The role of social media in shaping public opinion and attitudes towards police brutality and racial justice.
• The use of social media for promoting financial literacy and personal finance management.
• The role of social media in shaping public opinion and attitudes towards LGBTQ+ rights.
• The impact of social media on the music industry and fan engagement.
• The use of social media for promoting mental health and well-being among marginalized populations.
• The role of social media in shaping public opinion and attitudes towards immigration and border policies.
• The impact of social media on the professional development and networking of journalists.
• The use of social media for promoting community building and social cohesion.
• The role of social media in shaping public opinion and attitudes towards healthcare policies.
• The impact of social media on the food industry and consumer behavior.
• The role of social media in shaping public opinion and attitudes towards gender equality.
• The impact of social media on the sports industry and athlete-fan interactions.
• The use of social media for promoting financial inclusion and access to banking services.
• The role of social media in shaping public opinion and attitudes towards animal welfare.
• The use of social media for promoting mental health and well-being among college students.
• The role of social media in shaping public opinion and attitudes towards privacy and data security.
• The role of social media in shaping public opinion and attitudes towards income inequality and poverty.
• The use of social media for promoting digital literacy and technology skills.
• The role of social media in shaping public opinion and attitudes towards renewable energy.
• The use of social media for promoting mental health and well-being among elderly populations.
• The role of social media in shaping public opinion and attitudes towards online privacy and security.
• The role of social media in shaping public opinion and attitudes towards criminal justice reform.
• The impact of social media on online activism and social movements.
• The use of social media for business-to-business communication and networking.
• The role of social media in promoting civic education and engagement.
• The impact of social media on the fashion industry and sustainable fashion practices.
• The use of social media for promoting cultural diversity and inclusion.
• The role of social media in shaping public opinion and attitudes towards police reform.
• The impact of social media on the mental health and well-being of frontline healthcare workers.
• The use of social media for promoting financial literacy and investment education.
• The role of social media in promoting environmental sustainability and conservation.
• The impact of social media on body image and self-esteem among adolescent girls.
• The use of social media for promoting intercultural dialogue and understanding.
• The role of social media in shaping public opinion and attitudes towards immigration policies and refugees.
• The impact of social media on the professional development and networking of healthcare professionals.
• The use of social media for promoting community resilience and disaster preparedness.
• The role of social media in shaping public opinion and attitudes towards the Black Lives Matter movement.
• The impact of social media on the music industry and artist-fan interactions.
• The use of social media for promoting healthy eating habits and nutrition education.
• The role of social media in promoting mental health and well-being among college students.
• The impact of social media on the entertainment industry and consumer behavior.
• The use of social media for promoting workplace diversity and inclusion.
• The role of social media in shaping public opinion and attitudes towards climate change policies.
• The impact of social media on the travel industry and consumer behavior.
• The use of social media for promoting mental health and well-being among military veterans.
• The role of social media in promoting intergenerational dialogue and understanding.
• The impact of social media on the professional development and networking of educators.
• The use of social media for promoting animal welfare and advocacy.
• The role of social media in shaping public opinion and attitudes towards reproductive rights.
• The impact of social media on the sports industry and fan behavior.
• The use of social media for promoting financial inclusion and literacy among underprivileged populations.
• The role of social media in promoting mental health and well-being among LGBTQ+ populations.
• The impact of social media on the food and beverage industry and consumer behavior.
• The use of social media for promoting interfaith dialogue and understanding.
• The role of social media in shaping public opinion and attitudes towards gun ownership.
• The use of social media for promoting mental health and well-being among caregivers.
• The role of social media in promoting sustainable tourism practices.
• The impact of social media on the gaming industry and gamer culture.
• The use of social media for promoting cultural heritage tourism and preservation.
• The role of social media in shaping public opinion and attitudes towards public transportation policies.
• The use of social media for promoting mental health and well-being among homeless populations.
• The role of social media in promoting mental health and well-being among immigrants and refugees.
• The use of social media for promoting financial literacy and entrepreneurship among youth.
• The use of social media for political mobilization and participation in authoritarian regimes.
• The role of social media in shaping public opinion and attitudes towards immigration policies.
• The impact of social media on the professional development of teachers and educators.
• The use of social media for emergency communication during public health crises.
• The role of social media in promoting LGBTQ+ rights and advocacy.
• The impact of social media on body positivity and self-acceptance among women.
• The use of social media for public diplomacy and international relations.
• The impact of social media on the mental health and well-being of marginalized communities.
• The use of social media for crisis management and disaster response in the corporate sector.
• The role of social media in promoting environmental activism and conservation.
• The impact of social media on the professional development and networking of entrepreneurs.
• The use of social media for medical education and healthcare communication.
• The role of social media in promoting cultural exchange and understanding.
• The impact of social media on social capital and civic engagement among young adults.
• The use of social media for disaster preparedness and community resilience.
• The role of social media in promoting religious pluralism and tolerance.
• The use of social media for promoting healthy lifestyles and wellness.
• The use of social media for fundraising and philanthropy in the non-profit sector.
• The role of social media in promoting interfaith dialogue and understanding.
• The impact of social media on the travel and tourism industry and consumer behavior.
• The use of social media for customer engagement and brand loyalty in the retail sector.
• The impact of social media on the political attitudes and behaviors of young adults.
• The use of social media for promoting gender equality and women’s empowerment.
• The use of social media for promoting animal welfare and adoption.
• The role of social media in promoting mental health and well-being among the elderly.
• The impact of social media on the art industry and artist-fan interactions.
• The use of social media for promoting healthy food choices and nutrition.
• The role of social media in shaping public opinion and attitudes towards income inequality.
• The use of social media for promoting political satire and humor.
• The role of social media in promoting disability rights and advocacy.
• The use of social media for promoting voter registration and participation.
• The role of social media in promoting entrepreneurship and small business development.
• The use of social media for promoting mental health and well-being among incarcerated populations.
• The role of social media in shaping public opinion and attitudes towards gun violence prevention.
• The use of social media for promoting cultural heritage and preservation.
• The impact of social media on mental health and well-being.
• The relationship between social media use and academic performance.
• The use of social media for emergency communication during natural disasters.
• The impact of social media on traditional news media and journalism.
• The role of social media in shaping public opinion and discourse.
• The use of social media for online learning and education.
• The impact of social media on the fashion and beauty industry.
• The use of social media for brand awareness and marketing.
• The impact of social media on privacy and security.
• The use of social media for job searching and recruitment.
• The impact of social media on political polarization and extremism.
• The use of social media for online harassment and cyberbullying.
• The role of social media in promoting environmental awareness and sustainability.
• The impact of social media on youth culture and identity formation.
• The use of social media for travel and tourism marketing.
• The impact of social media on consumer behavior and decision-making.
• The role of social media in shaping beauty standards and body positivity.
• The use of social media for crisis communication and disaster response.
• The impact of social media on the music industry.
• The use of social media for fundraising and philanthropy.
• The role of social media in promoting healthy lifestyles and wellness.
• The impact of social media on sports fandom and fan behavior.
• The use of social media for political lobbying and advocacy.
• The impact of social media on the entertainment industry.
• The use of social media for healthcare communication and patient engagement.
• The role of social media in promoting gender equality and feminism.
• The impact of social media on the restaurant and food industry.
• The use of social media for volunteerism and community service.
• The role of social media in promoting religious tolerance and interfaith dialogue.
• The impact of social media on the art industry.
• The use of social media for political satire and humor.
• The role of social media in promoting disability awareness and advocacy.
• The impact of social media on the real estate industry.
• The use of social media for legal advocacy and justice reform.
• The role of social media in promoting intercultural communication and understanding.
• The impact of social media on the automotive industry.
• The use of social media for pet adoption and animal welfare advocacy.
• The role of social media in promoting mental health and wellness for marginalized communities.
• The impact of social media on the retail industry.
• The use of social media for promoting civic engagement and voter participation.
• The impact of social media on the film and television industry.
• The use of social media for fashion and style inspiration.
• The role of social media in promoting activism for human rights and social issues.
• The effectiveness of social media for political campaigns.
• The role of social media in promoting fake news and misinformation.
• The impact of social media on self-esteem and body image.
• The impact of social media on romantic relationships.
• The use of social media for online activism and social justice movements.
• The impact of social media on traditional news media.
• The impact of social media on interpersonal communication skills.
• The impact of social media on the fashion industry.
• The use of social media for social support and mental health awareness.
• The use of social media for political lobbying and activism.
• The impact of social media on travel and tourism behavior.
• The use of social media for customer feedback and market research.
• The impact of social media on the restaurant industry.
• The role of social media in political activism
• The effect of social media on interpersonal communication
• The relationship between social media use and body image concerns
• The impact of social media on self-esteem
• The role of social media in shaping cultural norms and values
• The use of social media by celebrities and its impact on their image
• The role of social media in building and maintaining personal relationships
• The use of social media for job searching and recruitment
• The impact of social media on children and adolescents
• The use of social media by political candidates during election campaigns
• The role of social media in education
• The impact of social media on political polarization
• The use of social media for news consumption
• The effect of social media on sleep habits
• The use of social media by non-profit organizations for fundraising
• The role of social media in shaping public opinion
• The influence of social media on language and communication patterns
• The use of social media in crisis communication and emergency management
• The role of social media in promoting environmental awareness
• The influence of social media on music preferences
• The impact of social media on body positivity movements
• The role of social media in shaping beauty standards
• The influence of social media on sports fandom
• The use of social media for health promotion and education
• The impact of social media on political participation
• The role of social media in shaping parenting practices
• The influence of social media on food preferences and eating habits
• The use of social media for peer support and mental health advocacy
• The role of social media in shaping religious beliefs and practices
• The influence of social media on humor and comedy
• The use of social media for online activism and social justice advocacy
• The impact of social media on public health awareness campaigns
• The role of social media in promoting cultural diversity and inclusion
• The influence of social media on travel behavior and decision-making
• The use of social media for international diplomacy and relations
• The impact of social media on job satisfaction and employee engagement
• The role of social media in shaping romantic preferences and dating behavior
• The influence of social media on language learning and language use
• The use of social media for political satire and humor
• The impact of social media on social capital and community building
• The role of social media in shaping gender identity and expression
• The influence of social media on fashion and beauty advertising.

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Muhammad Hassan

Researcher, Academic Writer, Web developer

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• Open access
• Published: 01 July 2020

The effect of social media on well-being differs from adolescent to adolescent

• Ine Beyens   ORCID: orcid.org/0000-0001-7023-867X 1 ,
• J. Loes Pouwels   ORCID: orcid.org/0000-0002-9586-392X 1 ,
• Irene I. van Driel   ORCID: orcid.org/0000-0002-7810-9677 1 ,
• Loes Keijsers   ORCID: orcid.org/0000-0001-8580-6000 2 &
• Patti M. Valkenburg   ORCID: orcid.org/0000-0003-0477-8429 1  

Scientific Reports volume  10 , Article number:  10763 ( 2020 ) Cite this article

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• Human behaviour

The question whether social media use benefits or undermines adolescents’ well-being is an important societal concern. Previous empirical studies have mostly established across-the-board effects among (sub)populations of adolescents. As a result, it is still an open question whether the effects are unique for each individual adolescent. We sampled adolescents’ experiences six times per day for one week to quantify differences in their susceptibility to the effects of social media on their momentary affective well-being. Rigorous analyses of 2,155 real-time assessments showed that the association between social media use and affective well-being differs strongly across adolescents: While 44% did not feel better or worse after passive social media use, 46% felt better, and 10% felt worse. Our results imply that person-specific effects can no longer be ignored in research, as well as in prevention and intervention programs.

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Associations between youth’s daily social media use and well-being are mediated by upward comparisons

Variation in social media sensitivity across people and contexts

Introduction.

Ever since the introduction of social media, such as Facebook and Instagram, researchers have been studying whether the use of such media may affect adolescents’ well-being. These studies have typically reported mixed findings, yielding either small negative, small positive, or no effects of the time spent using social media on different indicators of well-being, such as life satisfaction and depressive symptoms (for recent reviews, see for example 1 , 2 , 3 , 4 , 5 ). Most of these studies have focused on between-person associations, examining whether adolescents who use social media more (or less) often than their peers experience lower (or higher) levels of well-being than these peers. While such between-person studies are valuable in their own right, several scholars 6 , 7 have recently called for studies that investigate within-person associations to understand whether an increase in an adolescent’s social media use is associated with an increase or decrease in that adolescent’s well-being. The current study aims to respond to this call by investigating associations between social media use and well-being within single adolescents across multiple points in time 8 , 9 , 10 .

Person-specific effects

To our knowledge, four recent studies have investigated within-person associations of social media use with different indicators of adolescent well-being (i.e., life satisfaction, depression), again with mixed results 6 , 11 , 12 , 13 . Orben and colleagues 6 found a small negative reciprocal within-person association between the time spent using social media and life satisfaction. Likewise, Boers and colleagues 12 found a small within-person association between social media use and increased depressive symptoms. Finally, Coyne and colleagues 11 and Jensen and colleagues 13 did not find any evidence for within-person associations between social media use and depression.

Earlier studies that investigated within-person associations of social media use with indicators of well-being have all only reported average effect sizes. However, it is possible, or even plausible, that these average within-person effects may have been small and nonsignificant because they result from sizeable heterogeneity in adolescents’ susceptibility to the effects of social media use on well-being (see 14 , 15 ). After all, an average within-person effect size can be considered an aggregate of numerous individual within-person effect sizes that range from highly positive to highly negative.

Some within-person studies have sought to understand adolescents’ differential susceptibility to the effects of social media by investigating differences between subgroups. For instance, they have investigated the moderating role of sex to compare the effects of social media on boys versus girls 6 , 11 . However, such a group-differential approach, in which potential differences in susceptibility are conceptualized by group-level moderators (e.g., gender, age) does not provide insights into more fine-grained differences at the level of the single individual 16 . After all, while girls and boys each represent a homogenous group in terms of sex, they may each differ on a wide array of other factors.

As such, although worthwhile, the average within-person effects of social media on well-being obtained in previous studies may have been small or non-significant because they are diluted across a highly heterogeneous population (or sub-population) of adolescents 14 , 15 . In line with the proposition of media effects theories that each adolescent may have a unique susceptibility to the effects of social media 17 , a viable explanation for the small and inconsistent findings in earlier studies may be that the effect of social media differs from adolescent to adolescent. The aim of the current study is to investigate this hypothesis and to obtain a better understanding of adolescents’ unique susceptibility to the effects of social media on their affective well-being.

Social media and affective well-being

Within-person studies have provided important insights into the associations of social media use with cognitive well-being (e.g., life satisfaction 6 ), which refers to adolescents’ cognitive judgment of how satisfied they are with their life 18 . However, the associations of social media use with adolescents’ affective well-being (i.e., adolescents’ affective evaluations of their moods and emotions 18 ) are still unknown. In addition, while earlier within-person studies have focused on associations with trait-like conceptualizations of well-being 11 , 12 , 13 , that is, adolescents’ average well-being across specific time periods 18 , there is a lack of studies that focus on well-being as a momentary affective state. Therefore, we extend previous research by examining the association between adolescents’ social media use and their momentary affective well-being. Like earlier experience sampling (ESM) studies among adults 19 , 20 , we measured adolescents’ momentary affective well-being with a single item. Adolescents’ momentary affective well-being was defined as their current feelings of happiness, a commonly used question to measure well-being 21 , 22 , which has high convergent validity, as evidenced by the strong correlations with the presence of positive affect and absence of negative affect.

To assess adolescents’ momentary affective well-being (henceforth referred to as well-being), we conducted a week-long ESM study among 63 middle adolescents ages 14 and 15. Six times a day, adolescents were asked to complete a survey using their own mobile phone, covering 42 assessments per adolescent, assessing their affective well-being and social media use. In total, adolescents completed 2,155 assessments (83.2% average compliance).

We focused on middle adolescence, since this is the period in life characterized by most significant fluctuations in well-being 23 , 24 . Also, in comparison to early and late adolescents, middle adolescents are more sensitive to reactions from peers and have a strong tendency to compare themselves with others on social media and beyond. Because middle adolescents typically use different social media platforms, in a complementary way 25 , 26 , 27 , each adolescent reported on his/her use of the three social media platforms that s/he used most frequently out of the five most popular social media platforms among adolescents: WhatsApp, followed by Instagram, Snapchat, YouTube, and, finally, the chat function of games 28 . In addition to investigating the association between overall social media use and well-being (i.e., the summed use of adolescents’ three most frequently used platforms), we examined the unique associations of the two most popular platforms, WhatsApp and Instagram 28 .

Like previous studies on social media use and well-being, we distinguished between active social media use (i.e., “activities that facilitate direct exchanges with others” 29 ) and passive social media use (i.e., “consuming information without direct exchanges” 29 ). Within-person studies among young adults have shown that passive but not active social media use predicts decreases in well-being 29 . Therefore, we examined the unique associations of adolescents’ overall active and passive social media use with their well-being, as well as active and passive use of Instagram and WhatsApp, specifically. We investigated categorical associations, that is, whether adolescents would feel better or worse if they had actively or passively used social media. And we investigated dose–response associations to understand whether adolescents’ well-being would change as a function of the time they had spent actively or passively using social media.

The hypotheses and the design, sampling and analysis plan were preregistered prior to data collection and are available on the Open Science Framework, along with the code used in the analyses ( https://osf.io/nhks2 ). For details about the design of the study and analysis approach, see Methods.

In more than half of all assessments (68.17%), adolescents had used social media (i.e., one or more of their three favorite social media platforms), either in an active or passive way. Instagram (50.90%) and WhatsApp (53.52%) were used in half of all assessments. Passive use of social media (66.21% of all assessments) was more common than active use (50.86%), both on Instagram (48.48% vs. 20.79%) and WhatsApp (51.25% vs. 40.07%).

Strong positive between-person correlations were found between the duration of active and passive social media use (overall: r  = 0.69, p  < 0.001; Instagram: r  = 0.38, p  < 0.01; WhatsApp: r  = 0.85, p  < 0.001): Adolescents who had spent more time actively using social media than their peers, had also spent more time passively using social media than their peers. Likewise, strong positive within-person correlations were found between the duration of active and passive social media use (overall: r  = 0.63, p  < 0.001; Instagram: r  = 0.37, p  < 0.001; WhatsApp: r  = 0.57, p  < 0.001): The more time an adolescent had spent actively using social media at a certain moment, the more time s/he had also spent passively using social media at that moment.

Table 1 displays the average number of minutes that adolescents had spent using social media in the past hour at each assessment, and the zero-order between- and within-person correlations between the duration of social media use and well-being. At the between-person level, the duration of active and passive social media use was not associated with well-being: Adolescents who had spent more time actively or passively using social media than their peers did not report significantly higher or lower levels of well-being than their peers. At the within-person level, significant but weak positive correlations were found between the duration of active and passive overall social media use and well-being. This indicates that adolescents felt somewhat better at moments when they had spent more time actively or passively using social media (overall), compared to moments when they had spent less time actively or passively using social media. When looking at specific platforms, a positive correlation was only found for passive WhatsApp use, but not for active WhatsApp use, and not for active and passive Instagram use.

Average and person-specific effects

The within-person associations of social media use with well-being and differences in these associations were tested in a series of multilevel models. We ran separate models for overall social media use (i.e., active use and passive use of adolescents’ three favorite social media platforms, see Table 2 ), Instagram use (see Table 3 ), and WhatsApp use (see Table 4 ). In a first step we examined the average categorical associations for each of these three social media uses using fixed effects models (Models 1A, 3A, and 5A) to investigate whether, on average, adolescents would feel better or worse at moments when they had used social media compared to moments when they had not (i.e., categorical predictors: active use versus no active use, and passive use versus no passive use). In a second step, we examined heterogeneity in the within-person categorical associations by adding random slopes to the fixed effects models (Models 1B, 3B, and 5B). Next, we examined the average dose–response associations using fixed effects models (Models 2A, 4A, and 6A), to investigate whether, on average, adolescents would feel better or worse when they had spent more time using social media (i.e., continuous predictors: duration of active use and duration of passive use). Finally, we examined heterogeneity in the within-person dose–response associations by adding random slopes to the fixed effects models (Models 2B, 4B, and 6B).

Overall social media use.

The model with the categorical predictors (see Table 2 ; Model 1A) showed that, on average, there was no association between overall use and well-being: Adolescents’ well-being did not increase or decrease at moments when they had used social media, either in a passive or active way. However, evidence was found that the association of passive (but not active) social media use with well-being differed from adolescent to adolescent (Model 1B), with effect sizes ranging from − 0.24 to 0.68. For 44.26% of the adolescents the association was non-existent to small (− 0.10 <  r  < 0.10). However, for 45.90% of the adolescents there was a weak (0.10 <  r  < 0.20; 8.20%), moderate (0.20 <  r  < 0.30; 22.95%) or even strong positive ( r  ≥ 0.30; 14.75%) association between overall passive social media use and well-being, and for almost one in ten (9.84%) adolescents there was a weak (− 0.20 <  r  < − 0.10; 6.56%) or moderate negative (− 0.30 <  r  < − 0.20; 3.28%) association.

The model with continuous predictors (Model 2A) showed that, on average, there was a significant dose–response association for active use. At moments when adolescents had used social media, the time they spent actively (but not passively) using social media was positively associated with well-being: Adolescents felt better at moments when they had spent more time sending messages, posting, or sharing something on social media. The associations of the time spent actively and passively using social media with well-being did not differ across adolescents (Model 2B).

Instagram use

As shown in Model 3A in Table 3 , on average, there was a significant categorical association between passive (but not active) Instagram use and well-being: Adolescents experienced an increase in well-being at moments when they had passively used Instagram (i.e., viewing posts/stories of others). Adolescents did not experience an increase or decrease in well-being when they had actively used Instagram. The associations of passive and active Instagram use with well-being did not differ across adolescents (Model 3B).

On average, no significant dose–response association was found for Instagram use (Model 4A): At moments when adolescents had used Instagram, the time adolescents spent using Instagram (either actively or passively) was not associated with their well-being. However, evidence was found that the association of the time spent passively using Instagram differed from adolescent to adolescent (Model 4B), with effect sizes ranging from − 0.48 to 0.27. For most adolescents (73.91%) the association was non-existent to small (− 0.10 <  r  < 0.10), but for almost one in five adolescents (17.39%) there was a weak (0.10 <  r  < 0.20; 10.87%) or moderate (0.20 <  r  < 0.30; 6.52%) positive association, and for almost one in ten adolescents (8.70%) there was a weak (− 0.20 <  r  < − 0.10; 2.17%), moderate (− 0.30 <  r  < − 0.20; 4.35%), or strong ( r  ≤ − 0.30; 2.17%) negative association. Figure  1 illustrates these differences in the dose–response associations.

The dose–response association between passive Instagram use (in minutes per hour) and affective well-being for each individual adolescent (n = 46). Red lines represent significant negative within-person associations, green lines represent significant positive within-person associations, and gray lines represent non-significant within-person associations. A graph was created for each participant who had completed at least 10 assessments. A total of 13 participants were excluded because they had completed less than 10 assessments of passive Instagram use. In addition, one participant was excluded because no graph could be computed, since this participant's passive Instagram use was constant across assessments.

WhatsApp use

As shown in Model 5A in Table 4 , just as for Instagram, we found that, on average, there was a significant categorical association between passive (but not active) WhatsApp use and well-being: Adolescents reported that they felt better at moments when they had passively used WhatsApp (i.e., read WhatsApp messages). For active WhatsApp use, no significant association was found. Also, in line with the results for Instagram use, no differences were found regarding the associations of active and passive WhatsApp use (Model 5B).

In addition, a significant dose–response association was found for passive (but not active) use (Model 6A). At moments when adolescents had used WhatsApp, we found that, on average, the time adolescents spent passively using WhatsApp was positively associated with well-being: Adolescents felt better at moments when they had spent more time reading WhatsApp messages. The time spent actively using WhatsApp was not associated with well-being. No differences were found in the dose–response associations of active and passive WhatsApp use (Model 6B).

This preregistered study investigated adolescents’ unique susceptibility to the effects of social media. We found that the associations of passive (but not active) social media use with well-being differed substantially from adolescent to adolescent, with effect sizes ranging from moderately negative (− 0.24) to strongly positive (0.68). While 44.26% of adolescents did not feel better or worse if they had passively used social media, 45.90% felt better, and a small group felt worse (9.84%). In addition, for Instagram the majority of adolescents (73.91%) did not feel better or worse when they had spent more time viewing post or stories of others, whereas some felt better (17.39%), and others (8.70%) felt worse.

These findings have important implications for social media effects research, and media effects research more generally. For decades, researchers have argued that people differ in their susceptibility to the effects of media 17 , leading to numerous investigations of such differential susceptibility. These investigations have typically focused on moderators, based on variables such as sex, age, or personality. Yet, over the years, studies have shown that such moderators appear to have little power to explain how individuals differ in their susceptibility to media effects, probably because a group-differential approach does not account for the possibility that media users may differ across a range of factors, that are not captured by only one (or a few) investigated moderator variables.

By providing insights into each individual’s unique susceptibility, the findings of this study provide an explanation as to why, up until now, most media effects research has only found small effects. We found that the majority of adolescents do not experience any short-term changes in well-being related to their social media use. And if they do experience any changes, these are more often positive than negative. Because only small subsets of adolescents experience small to moderate changes in well-being, the true effects of social media reported in previous studies have probably been diluted across heterogeneous samples of individuals that differ in their susceptibility to media effects (also see 30 ). Several scholars have noted that overall effect sizes may mask more subtle individual differences 14 , 15 , which may explain why previous studies have typically reported small or no effects of social media on well-being or indicators of well-being 6 , 11 , 12 , 13 . The current study seems to confirm this assumption, by showing that while the overall effect sizes are small at best, the person-specific effect sizes vary considerably, from tiny and small to moderate and strong.

As called upon by other scholars 5 , 31 , we disentangled the associations of active and passive use of social media. Research among young adults found that passive (but not active) social media use is associated with lower levels of affective well-being 29 . In line with these findings, the current study shows that active and passive use yielded different associations with adolescents’ affective well-being. Interestingly though, in contrast to previous findings among adults, our study showed that, on average, passive use of Instagram and WhatsApp seemed to enhance rather than decrease adolescents’ well-being. This discrepancy in findings may be attributed to the fact that different mechanisms might be involved. Verduyn and colleagues 29 found that passive use of Facebook undermines adults’ well-being by enhancing envy, which may also explain the decreases in well-being found in our study among a small group of adolescents. Yet, adolescents who felt better by passively using Instagram and WhatsApp, might have felt so because they experienced enjoyment. After all, adolescents often seek positive content on social media, such as humorous posts or memes 32 . Also, research has shown that adolescents mainly receive positive feedback on social media 33 . Hence, their passive Instagram and WhatsApp use may involve the reading of positive feedback, which may explain the increases in well-being.

Overall, the time spent passively using WhatsApp improved adolescents’ well-being. This did not differ from adolescent to adolescent. However, the associations of the time spent passively using Instagram with well-being did differ from adolescent to adolescent. This discrepancy suggests that not all social media uses yield person-specific effects on well-being. A possible explanation may be that adolescents’ responses to WhatsApp are more homogenous than those to Instagram. WhatsApp is a more private platform, which is mostly used for one-to-one communication with friends and acquaintances 26 . Instagram, in contrast, is a more public platform, which allows its users to follow a diverse set of people, ranging from best friends to singers, actors, and influencers 28 , and to engage in intimate communication as well as self-presentation and social comparison. Such diverse uses could lead to more varied, or even opposing responses, such as envy versus inspiration.

Limitations and directions for future research

The current study extends our understanding of differential susceptibility to media effects, by revealing that the effect of social media use on well-being differs from adolescent to adolescent. The findings confirm our assumption that among the great majority of adolescents, social media use is unrelated to well-being, but that among a small subset, social media use is either related to decreases or increases in well-being. It must be noted, however, that participants in this study felt relatively happy, overall. Studies with more vulnerable samples, consisting of clinical samples or youth with lower social-emotional well-being may elicit different patterns of effects 27 . Also, the current study focused on affective well-being, operationalized as happiness. It is plausible that social media use relates differently with other types of well-being, such as cognitive well-being. An important next step is to identify which adolescents are particularly susceptible to experience declines in well-being. It is conceivable, for instance, that the few adolescents who feel worse when they use social media are the ones who receive negative feedback on social media 33 .

In addition, future ESM studies into the effects of social media should attempt to include one or more follow-up measures to improve our knowledge of the longer-term influence of social media use on affective well-being. While a week-long ESM is very common and applied in most earlier ESM studies 34 , a week is only a snapshot of adolescent development. Research is needed that investigates whether the associations of social media use with adolescents’ momentary affective well-being may cumulate into long-lasting consequences. Such investigations could help clarify whether adolescents who feel bad in the short term would experience more negative consequences in the long term, and whether adolescents who feel better would be more resistant to developing long-term negative consequences. And while most adolescents do not seem to experience any short-term increases or decreases in well-being, more research is needed to investigate whether these adolescents may experience a longer-term impact of social media.

While the use of different platforms may be differently associated with well-being, different types of use may also yield different effects. Although the current study distinguished between active and passive use of social media, future research should further differentiate between different activities. For instance, because passive use entails many different activities, from reading private messages (e.g., WhatsApp messages, direct messages on Instagram) to browsing a public feed (e.g., scrolling through posts on Instagram), research is needed that explores the unique effects of passive public use and passive private use. Research that seeks to explore the nuances in adolescents’ susceptibility as well as the nuances in their social media use may truly improve our understanding of the effects of social media use.

Participants

Participants were recruited via a secondary school in the south of the Netherlands. Our preregistered sampling plan set a target sample size of 100 adolescents. We invited adolescents from six classrooms to participate in the study. The final sample consisted of 63 adolescents (i.e., 42% consent rate, which is comparable to other ESM studies among adolescents; see, for instance 35 , 36 ). Informed consent was obtained from all participants and their parents. On average, participants were 15 years old ( M  = 15.12 years, SD  = 0.51) and 54% were girls. All participants self-identified as Dutch, and 41.3% were enrolled in the prevocational secondary education track, 25.4% in the intermediate general secondary education track, and 33.3% in the academic preparatory education track.

The study was approved by the Ethics Review Board of the Faculty of Social and Behavioral Sciences at the University of Amsterdam and was performed in accordance with the guidelines formulated by the Ethics Review Board. The study consisted of two phases: A baseline survey and a personalized week-long experience sampling (ESM) study. In phase 1, researchers visited the school during school hours. Researchers informed the participants of the objective and procedure of the study and assured them that their responses would be treated confidentially. Participants were asked to sign the consent form. Next, participants completed a 15-min baseline survey. The baseline survey included questions about demographics and assessed which social media each adolescent used most frequently, allowing to personalize the social media questions presented during the ESM study in phase 2. After completing the baseline survey, participants were provided detailed instructions about phase 2.

In phase 2, which took place two and a half weeks after the baseline survey, a 7-day ESM study was conducted, following the guidelines for ESM studies provided by van Roekel and colleagues 34 . Aiming for at least 30 assessments per participant and based on an average compliance rate of 70 to 80% reported in earlier ESM studies among adolescents 34 , we asked each participant to complete a total of 42 ESM surveys (i.e., six 2-min surveys per day). Participants completed the surveys using their own mobile phone, on which the ESM software application Ethica Data was installed during the instruction session with the researchers (phase 1). Each 2-min survey consisted of 22 questions, which assessed adolescents’ well-being and social media use. Two open-ended questions were added to the final survey of the day, which asked about adolescents’ most pleasant and most unpleasant events of the day.

The ESM sampling scheme was semi-random, to allow for randomization and avoid structural patterns in well-being, while taking into account that adolescents were not allowed to use their phone during school time. The Ethica Data app was programmed to generate six beep notifications per day at random time points within a fixed time interval that was tailored to the school’s schedule: before school time (1 beep), during school breaks (2 beeps), and after school time (3 beeps). During the weekend, the beeps were generated during the morning (1 beep), afternoon (3 beeps), and evening (2 beeps). To maximize compliance, a 30-min time window was provided to complete each survey. This time window was extended to one hour for the first survey (morning) and two hours for the final survey (evening) to account for travel time to school and time spent on evening activities. The average compliance rate was 83.2%. A total of 2,155 ESM assessments were collected: Participants completed an average of 34.83 surveys ( SD  = 4.91) on a total of 42 surveys, which is high compared to previous ESM studies among adolescents 34 .

The questions of the ESM study were personalized based on the responses to the baseline survey. During the ESM study, each participant reported on his/her use of three different social media platforms: WhatsApp and either Instagram, Snapchat, YouTube, and/or the chat function of games (i.e., the most popular social media platforms among adolescents 28 ). Questions about Instagram and WhatsApp use were only included if the participant had indicated in the baseline survey that s/he used these platforms at least once a week. If a participant had indicated that s/he used Instagram or WhatsApp (or both) less than once a week, s/he was asked to report on the use of Snapchat, YouTube, or the chat function of games, depending on what platform s/he used at least once a week. In addition to Instagram and WhatsApp, questions were asked about a third platform, that was selected based on how frequently the participant used Snapchat, YouTube, or the chat function of games (i.e., at least once a week). This resulted in five different combinations of three platforms: Instagram, WhatsApp, and Snapchat (47 participants); Instagram, WhatsApp, and YouTube (11 participants); Instagram, WhatsApp, and chatting via games (2 participants); WhatsApp, Snapchat, and YouTube (1 participant); and WhatsApp, YouTube, and chatting via games (2 participants).

Frequency of social media use

In the baseline survey, participants were asked to indicate how often they used and checked Instagram, WhatsApp, Snapchat, YouTube, and the chat function of games, using response options ranging from 1 ( never ) to 7 ( more than 12 times per day ). These platforms are the five most popular platforms among Dutch 14- and 15-year-olds 28 . Participants’ responses were used to select the three social media platforms that were assessed in the personalized ESM study.

Duration of social media use

In the ESM study, duration of active and passive social media use was measured by asking participants how much time in the past hour they had spent actively and passively using each of the three platforms that were included in the personalized ESM surveys. Response options ranged from 0 to 60 min , with 5-min intervals. To measure active Instagram use, participants indicated how much time in the past hour they had spent (a) “posting on your feed or sharing something in your story on Instagram” and (b) “sending direct messages/chatting on Instagram.” These two items were summed to create the variable duration of active Instagram use. Sum scores exceeding 60 min (only 0.52% of all assessments) were recoded to 60 min. To measure duration of passive Instagram use, participants indicated how much time in the past hour they had spent “viewing posts/stories of others on Instagram.” To measure the use of WhatsApp, Snapchat, YouTube and game-based chatting, we asked participants how much time they had spent “sending WhatsApp messages” (active use) and “reading WhatsApp messages” (passive use); “sending snaps/messages or sharing something in your story on Snapchat” (active use) and “viewing snaps/stories/messages from others on Snapchat” (passive use); “posting YouTube clips” (active use) and “watching YouTube clips” (passive use); “sending messages via the chat function of a game/games” (active use) and “reading messages via the chat function of a game/games” (passive use). Duration of active and passive overall social media use were created by summing the responses across the three social media platforms for active and passive use, respectively. Sum scores exceeding 60 min (2.13% of all assessments for active overall use; 2.90% for passive overall use) were recoded to 60 min. The duration variables were used to investigate whether the time spent actively or passively using social media was associated with well-being (dose–response associations).

Use/no use of social media

Based on the duration variables, we created six dummy variables, one for active and one for passive overall social media use, one for active and one for passive Instagram use, and one for active and one for passive WhatsApp use (0 =  no active use and 1 =  active use , and 0 =  no passive use and 1 =  passive use , respectively). These dummy variables were used to investigate whether the use of social media, irrespective of the duration of use, was associated with well-being (categorical associations).

Consistent with previous ESM studies 19 , 20 , we measured affective well-being using one item, asking “How happy do you feel right now?” at each assessment. Adolescents indicated their response to the question using a 7-point scale ranging from 1 ( not at all ) to 7 ( completely ), with 4 ( a little ) as the midpoint. Convergent validity of this item was established in a separate pilot ESM study among 30 adolescents conducted by the research team of the fourth author: The affective well-being item was strongly correlated with the presence of positive affect and absence of negative affect (assessed by a 10-item positive and negative affect schedule for children; PANAS-C) at both the between-person (positive affect: r  = 0.88, p < 0.001; negative affect: r  = − 0.62, p < 0.001) and within-person level (positive affect: r  = 0.74, p < 0.001; negative affect: r  = − 0.58, p < 0.001).

Statistical analyses

Before conducting the analyses, several validation checks were performed (see 34 ). First, we aimed to only include participants in the analyses who had completed more than 33% of all ESM assessments (i.e., at least 14 assessments). Next, we screened participants’ responses to the open questions for unserious responses (e.g., gross comments, jokes). And finally, we inspected time series plots for patterns in answering tendencies. Since all participants completed more than 33% of all ESM assessments, and no inappropriate responses or low-quality data patterns were detected, all participants were included in the analyses.

Following our preregistered analysis plan, we tested the proposed associations in a series of multilevel models. Before doing so, we tested the homoscedasticity and linearity assumptions for multilevel analyses 37 . Inspection of standardized residual plots indicated that the data met these assumptions (plots are available on OSF at  https://osf.io/nhks2 ). We specified separate models for overall social media use, use of Instagram, and use of WhatsApp. To investigate to what extent adolescents’ well-being would vary depending on whether they had actively or passively used social media/Instagram/WhatsApp or not during the past hour (categorical associations), we tested models including the dummy variables as predictors (active use versus no active use, and passive use versus no passive use; models 1, 3, and 5). To investigate whether, at moments when adolescents had used social media/Instagram/WhatsApp during the past hour, their well-being would vary depending on the duration of social media/Instagram/WhatsApp use (dose–response associations), we tested models including the duration variables as predictors (duration of active use and duration of passive use; models 2, 4, and 6). In order to avoid negative skew in the duration variables, we only included assessments during which adolescents had used social media in the past hour (overall, Instagram, or WhatsApp, respectively), either actively or passively. All models included well-being as outcome variable. Since multilevel analyses allow to include all available data for each individual, no missing data were imputed and no data points were excluded.

We used a model building approach that involved three steps. In the first step, we estimated an intercept-only model to assess the relative amount of between- and within-person variance in affective well-being. We estimated a three-level model in which repeated momentary assessments (level 1) were nested within adolescents (level 2), who, in turn, were nested within classrooms (level 3). However, because the between-classroom variance in affective well-being was small (i.e., 0.4% of the variance was explained by differences between classes), we proceeded with estimating two-level (instead of three-level) models, with repeated momentary assessments (level 1) nested within adolescents (level 2).

In the second step, we assessed the within-person associations of well-being with (a) overall active and passive social media use (i.e., the total of the three platforms), (b) active and passive use of Instagram, and (c) active and passive use of WhatsApp, by adding fixed effects to the model (Models 1A-6A). To facilitate the interpretation of the associations and control for the effects of time, a covariate was added that controlled for the n th assessment of the study week (instead of the n th assessment of the day, as preregistered). This so-called detrending is helpful to interpret within-person associations as correlated fluctuations beyond other changes in social media use and well-being 38 . In order to obtain within-person estimates, we person-mean centered all predictors 38 . Significance of the fixed effects was determined using the Wald test.

In the third and final step, we assessed heterogeneity in the within-person associations by adding random slopes to the models (Models 1B-6B). Significance of the random slopes was determined by comparing the fit of the fixed effects model with the fit of the random effects model, by performing the Satorra-Bentler scaled chi-square test 39 and by comparing the Bayesian information criterion (BIC 40 ) and Akaike information criterion (AIC 41 ) of the models. When the random effects model had a significantly better fit than the fixed effects model (i.e., pointing at significant heterogeneity), variance components were inspected to investigate whether heterogeneity existed in the association of either active or passive use. Next, when evidence was found for significant heterogeneity, we computed person-specific effect sizes, based on the random effect models, to investigate what percentages of adolescents experienced better well-being, worse well-being, and no changes in well-being. In line with Keijsers and colleagues 42 we only included participants who had completed at least 10 assessments. In addition, for the dose–response associations, we constructed graphical representations of the person-specific slopes, based on the person-specific effect sizes, using the xyplot function from the lattice package in R 43 .

Three improvements were made to our original preregistered plan. First, rather than estimating the models with multilevel modelling in R 43 , we ran the preregistered models in Mplus 44 . Mplus provides standardized estimates for the fixed effects models, which offers insight into the effect sizes. This allowed us to compare the relative strength of the associations of passive versus active use with well-being. Second, instead of using the maximum likelihood estimator, we used the maximum likelihood estimator with robust standard errors (MLR), which are robust to non-normality. Sensitivity tests, uploaded on OSF ( https://osf.io/nhks2 ), indicated that the results were almost identical across the two software packages and estimation approaches. Third, to improve the interpretation of the results and make the scales of the duration measures of social media use and well-being more comparable, we transformed the social media duration scores (0 to 60 min) into scales running from 0 to 6, so that an increase of 1 unit reflects 10 min of social media use. The model estimates were unaffected by this transformation.

Reporting summary

Further information on the research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

The dataset generated and analysed during the current study is available in Figshare 45 . The preregistration of the design, sampling and analysis plan, and the analysis scripts used to analyse the data for this paper are available online on the Open Science Framework website ( https://osf.io/nhks2 ).

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Acknowledgements

This study was funded by the NWO Spinoza Prize and the Gravitation grant (NWO Grant 024.001.003; Consortium on Individual Development) awarded to P.M.V. by the Dutch Research Council (NWO). Additional funding was received from the VIDI grant (NWO VIDI Grant 452.17.011) awarded to L.K. by the Dutch Research Council (NWO). The authors would like to thank Savannah Boele (Tilburg University) for providing her pilot ESM results.

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I.B., J.L.P., I.I.v.D., L.K., and P.M.V. designed the study; I.B., J.L.P., and I.I.v.D. collected the data; I.B., J.L.P., and L.K. analyzed the data; and I.B., J.L.P., I.I.v.D., L.K., and P.M.V. contributed to writing and reviewing the manuscript.

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Beyens, I., Pouwels, J.L., van Driel, I.I. et al. The effect of social media on well-being differs from adolescent to adolescent. Sci Rep 10 , 10763 (2020). https://doi.org/10.1038/s41598-020-67727-7

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Social media research: Step-by-step tutorial with examples

• Introduction: What is social media research?
• Step 1: Develop a research design
• Step 2: Collect & import your social media data
• Step 3: Data preparation

Step 4: Get an overview

• Step 5: Categorize your data
• Step 6: Aggregate & present your results

Further learning materials

Friday, January 5, 2024

How to conduct social media research with MAXQDA?

Social Media has drastically changed the way we communicate. Nowadays it’s a lot easier for an individual to communicate with a large audience or with strangers living on the other side of the planet, and to find and communicate with others researching similar topics. Companies, organizations, and political parties can target a specific group of people for their campaign and receive immediate feedback. So, it’s not a surprise that online communication has become more prevalent, which in turn has increased the significance of social media platforms.

Researchers and marketers alike benefit from the wealth of data available on social media platforms, gaining insights into the public’s opinions, communication patterns, and more. Social media research describes the process of collecting and analyzing social media data, such as posts, comments, and likes in order to understand communication patterns, public opinions, and trends.

Who conducts social media research?

Compared to other data collection instruments, such as focus group discussions, collecting social media data is less resource-intensive as the data is easily accessible. However, researchers are confronted with extensive data when performing social media research. Depending on the topic thousands and thousands of posts and comments exist. Consequently, social media researchers need QDA software that is well-equipped for challenges like these, such as MAXQDA. MAXQDA can facilitate your social media research with its numerous data organization and analysis tools. MAXQDA’s auto-coding and sentiment analysis are particularly useful tools, allowing you to explore many posts without reading each one individually. Furthermore, AI Assist, MAXQDA’s AI-based features, are well-suited to handle big data. In the present guide we aim to explain how you can perform social media research with MAXQDA.

Step 1: Develop a research design for social media research

As for any other research project, we advise you to develop a research design before starting your social media research. A research design serves as a structured plan outlining how a researcher intends to answer a specific research question. Determine the specific social media data you wish to analyze and define the precise methodology for your analysis. Among other considerations, ask yourself which social media platform(s) you want to consider, whether there is a time frame of interest; and if you plan to exclusively focus on social media posts containing particular hashtags or keywords. You must address these questions to develop a well-designed study that ensures reliable and valid results. We recommend reading our Research Design guide if you need clarification on what a research design entails.

Please note that the order of the steps presented here is flexible and depends on your research design and research question.

Step 2: Collect & import your social media data

With MAXQDA, you have several options for importing your social media data. On the one side, MAXQDA provides specialized import tools for YouTube comments and specialized analysis tools for YouTube data and X (formerly known as Twitter) data. Suppose you want to import and analyze data from a different social media platform. Then, you can either use MAXQDA’s WebCollector to collect and import entire webpages into MAXQDA or another social media data collection service, saving the data in a MAXQDA-compatible format, like an Excel file. There are several online tools for exporting social media data.

MAXQDA’s WebCollector

You can use MAXQDA’s WebCollector – a free Chrome Browser extension – to export entire websites in a format that can be imported into MAXQDA. The free MAXQDA WebCollector is availale on the Chrome WebStore.

Get the MAXQDA WebCollector

After installing the extension, export the webpage from your social media platform of interest. In the case of X (formerly known as Twitter) you have two options. You can either export only top-level posts or a specific top-level post, including all its replies. Search for a hashtag and export the search results, i.e., all posts containing this hashtag, by opening the WebCollector extension and clicking “Collect.” If you want, you can add notes in the Document Memo section, such as the time frame or other parameters of your search. Upon import into MAXQDA, these notes will be imported as a Document Memo.

Use the MAXQDA WebCollector to export social media data

In the case you are specifically interested in specific posts, e.g., posts from a certain account or posts with a lot of replies, click on the post so that the original post and all comments are displayed. Now, export the website with MAXQDA’s WebCollector to compile the original posts, including all replies.

Step 3: Social media research data preparation

Before starting the actual analysis, you might want to clean and organize your data in a meaningful way. For example, you could remove irrelevant and duplicate posts. You could also organize your data in document groups, e.g., based on the social media platform, a time range, a hashtag, or whatever category is important to your social media research.

Organize your data in Document groups

You may add variables to the imported social media data depending on your research design. For example, when investigating social media trends over time, it can be handy for further analysis to add variables such as the date and timing of the post. To do so, simply go to the “Variables” tab and click “List of Document Variables.” By clicking “New variable,” you can add new variables, specify their type, and define missing values.

Add document variables to improve your social media research

Depending on your research approach, you might benefit from an overview of the data before creating and applying codes, e.g., when following an inductive approach. In other cases, you might already have codes in mind and use them prior to summarizing the data, e.g., in deductive approaches.

When following an inductive approach, you might want to get a basic understanding of the collected social media data and base your codes on the actual content. MAXQDA offers numerous tools, allowing you to get a quick overview. Especially useful when working with big data, such as in social media research, are MAXQDA’s auto-coding and AI-based tools.

Summarize social media data with AI Assist

We acknowledge that AI can assist researchers in qualitative data analysis as well as in other areas of life. Therefore, we developed the AI Assist add-on – your virtual research assistant. AI Assist features several tools that can facilitate your social media research. AI Assist’s Summarize Document function is handy for a quick content overview. This feature creates a summary of entire documents, e.g., of a post and its replies, which it stores in Document Memos. To let AI Assist summarize your document, right-click on it in the Documents window, and choose AI Assist > Summarize Document. You can edit and refine the summary within the Document Memo. These summaries might help you get an idea about the key points discussed and develop codes accordingly.

With AI-generated summaries you can speed up your social media research

Automatically analyze the public’s sentiment

Often, people performing social media research are not interested in every single opinion of every single individual but in the general sentiment towards a topic, politician, issue, or product. With MAXQDA, you can perform a sentiment analysis in no time. To perform a sentiment analysis, open the Smart Coding Tool in the Codes tab. Since the Smart Coding tool works on the level of coded segments, you need to dummy-code your data prior to the sentiment analysis. When importing YouTube comments, comments and replies are automatically coded. However, when importing data through other means, such as via the WebCollector, you may want to manually create the codes ‘post’ and ‘reply’ to quickly code your data. Subsequently, you can perform automatic sentiment analysis by clicking on the button “Analyze Sentiments.” To autocode your social media data with the respective sentiment, click “Autocode Segments with Sentiment.” Then, MAXQDA creates the code ‘Sentiment’ with the identified sentiments as subcodes. By looking at the code frequencies you get a first impression of the general public sentiment.

Autocode the sentiment of your social media data

Subsequently, you can use MAXQDA’s retrieval function to, e.g., focus your social media research on negative posts. To do so, simply activate the documents of interest and the code ‘Sentiment’ > ‘Negative’. All text segments coded with this code will be displayed in the Retrieved Segments window. If you plan to create subcodes, for example to divide the negative sentiment into reasons why people dislike your product, you can again use the Smart Coding tool. Select the code ‘Negative’ from the Code tree on the left site and MAXQDA will display only the segments with a negative sentiment to which you can apply additional codes.

Summarize coded segments with AI Assist

Rather than going through the ‘Negative’ posts individually, you can again use the power of artificial intelligence to create a summary of the coded segments. To do so, right-click the code ‘Negative’ in the Codes window and select AI Assist > Summarize coded segments. Similarly, to the Summarize Document feature, AI Assist will add the summary in a memo.

Step 5: Categorize your social media research data

In many qualitative research projects, including social media research, coding/categorizing your data is an important step. When working inductively, the AI-generated summaries might provide initial ideas for codes. When working deductively, you probably already have codes in mind. With MAXQDA you can easily create codes, assign code colors, and define rules for coding in the New Code window regardless of your approach. Furthermore, you can organize your codes hierarchically. But there is more – MAXQDA allows you to create emoticodes which might come handy when analyzing social media data. For more information on various coding methods, you can refer to:

Learn more about coding with MAXQDA

Autocode your social media data

Especially useful for big data is MAXQDA’s Text Search & Autocode feature, which is located in the Analysis tab. This feature allows you to search for keywords and automatically code them. You can also use logical operators, such as OR, to search for a list of keywords simultaneously e.g., to find all synonyms of a word with just one search. If you are interested in certain concepts, you can create dictionaries of keywords defining the concept and search for multiple concepts at once (search for the whole dictionary). To do so, you first need to create a dictionary. Therefore, go to the MAXDictio tab and select Dictionaries.

Search & autocode important keywords for your social media research

Generate subcode suggestions with AI Assist

In the coding of qualitative data, researchers often start with broad codes, intending to refine them in a later step of the social media research. Alongside the Smart Coding Tool, which is ideal for code refinement, as explained earlier, AI Assist’s “Suggest Subcodes” is another valuable tool. You can use this feature to to get subcode suggestions. Simply, right-click on a code and select AI Assist > Subcode Suggestions.

Step 6: Aggregate & present your results

A crucial step involves consolidating your social media research results into a format that is easily understandable for others. For example, charts and visualisations can aggregate huge amount of data in an easily comprehensible graph that answers your research question. Of course, MAXQDA has integrated visualisation and charting tools. Some tools that might be especially useful for presenting social media data are presented in the following sections.

Word Cloud for visualizing the most frequent words

MAXQDA’s World Cloud, which can also be used with data that hasn’t been coded, is one of the most appropriate visualization tools in social media research. Select the document(s) that serve as the basis for your word cloud and generate a visual representation of the most recurring words. To exclude frequent, yet non-informative words such as ‘the’ or ‘a,’ you can apply a stop word list to the data, effectively filtering out these ubiquitous terms. We offer several Stop Word Lists in several languages on our website, so you don’t have to create one yourself.

Get Stop Word Lists

Word Cloud displaying the most frequent words of YouTube comments

Visualize trends

If your social media research analyzes a topic over time, the Trends function might also interest you. Currently, MAXQDA offers Word Trends, Code Trends, and Dictionary Categories Trends. To explore how code or word frequencies change across time, you should store your social media data in distinct documents – one document per time range. While Word Trends can be used even when the data is not coded, Code Trends requires coded data. No matter whether you are using Code or Word trends, select Trends for multiple documents. Then, select the documents (and codes) of interest and MAXQDA will visualize them. For example, you can use the Code Trends tool on auto-generated sentiment codes to investigate how sentiments towards a topic change over time.

Aside from analyzing trends across time, you can also use MAXQDA’s Trends tool to compare reactions, e.g., between different social media plattforms. To do this, you need to organize your data as follows: create a separate document for each social media plattform containing all posts of interest. Next, choose your preferred Trends tool and again choose trends for multiple documents. In case you are interested in how a discussion evolves in a comment section, given that the data is stored in one document, you can opt for the single document trends feature. MAXQDA splits the document in 10 segments, allowing you to see how word/code frequencies look across them.

Visualizing sentiment trends for #maxqda across weeks

Write your report with QTT

Questions-Themes-Theories (QTT) provides an innovative workspace for gathering important visualizations, notes, segments, and other analytical results. It is an excellent tool for organizing your thoughts and crafting your social media research report. To get started, create a dedicated worksheet for your topics and research questions, and populate it with pertinent analysis elements extracted from other MAXQDA functions. For example, you can incorporate your Trends visualization to a QTT worksheet by clicking on the button, as shown in the screenshot below. Exploratory coded segments related to a set of social media posts can be added to the QTT worksheet via the context menu. For each imported element you can add insights. Furthermore, you have the option to add your conclusions and theories, as well as your research design. Subsequently, you can view all analysis elements and insights to write your final conclusion. The new Questions-Themes-Theories tool is designed to assist you finalize your social media research. With just one click, you can export your worksheet and use it as a starting point for your social media research report.

Add a visualization to a QTT worksheet

We offer a variety of complimentary learning materials to help you get started with your social media research. Check out the recording of a spotlight session on analyzing social media data with MAXQDA which was held at the MAXDAYS conference in 2023. In addition, the free book “The Practice of Qualitative Data Analysis,” provides ten case studies with brief real-world examples, demonstrating MAXQDA’s practical applications.

Spotlight Session: Analyzing Social Media Data with MAXQDA

The Practice of Qualitative Data Analysis

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Social Media Use and Impact on Interpersonal Communication

• Conference paper
• First Online: 01 January 2015
• Cite this conference paper

• Yerika Jimenez 2 &
• Patricia Morreale 3  

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 529))

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• International Conference on Human-Computer Interaction

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This research paper presents the findings of a research project that investigated how young adult interpersonal communications have changed since using social media. Specifically, the research focused on determining if using social media had a beneficial or an adverse effect on the development of interaction and communication skills of young adults. Results from interviews reveal a negative impact in young adult communications and social skills. In this paper young adult preferences in social media are also explored, to answer the question: Does social media usage affect the development of interaction and communication skills for young adults and set a basis for future adult communication behaviors?

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Weibo or Weixin? Gratifications for Using Different Social Media

Social Media Dependency: The Implications of Technological Communication Use Among University Students

Investigating the Usage Patterns and the Implications of Young Adults’ Social Media Usage in South Africa

• Social media
• Social interaction
• Interpersonal communications
• Young adults

1 Introduction

Human interaction has changed drastically in the last 20 years, not only due to the introduction of the Internet, but also from social media and online communities. These social media options and communities have grown from being simply used to communicate on a private network into a strong culture that almost all individuals are using to communicate with others all over the world. We will concentrate on the impact that social media has on human communication and interaction among young adults, primarily college students. In today’s society, powerful social media platforms such as Myspace, Facebook, Twitter, Instagram (IG), and Pinterest have been the result of an evolution that is changing how humans communicate with each other. The big question we asked ourselves was how much has social media really impacted the way that humans communicate and interact with each other, and if so, how significant is the change of interpersonal interaction among young adults in the United States today?

The motivation behind this research has been personal experience with interaction and communication with friends and family; it had become difficult, sometimes even rare, to have a one-on-one conversation with them, without having them glancing at or interacting with their phone. Has social interaction changed since the introduction of advanced technology and primarily social media? In correlation with the research data collected in this study, it was concluded that many participants’ personal communication has decreased due social media influence encouraging them to have online conversations, as opposed to face-to-face, in-person conversations.

2 Related Work

The question of how social media affects social and human interaction in our society is being actively researched and studied. A literature review highlights the positive and negative aspects of social media interaction, as researchers battle to understand the current and future effects of social media interaction. A study done by Keith Oatley, an emeritus professor of cognitive psychology at the University of Toronto, suggests that the brain may interpret digital interaction in the same manner as in-person interaction, while others maintain that differences are growing between how we perceive one another online as opposed to in reality [ 1 ]. This means that young adults can interpret online communication as being real one-on-one communication because the brain will process that information as a reality. Another study revealed that online interaction helps with the ability to relate to others, tolerate differing viewpoints, and express thoughts and feeling in a healthy way [ 2 , 3 ]. Moreover a study executed by the National Institutes of Health found that youths with strong, positive face-to-face relationships may be those most frequently using social media as an additional venue to interact with their peers [ 4 ].

In contrast, research reveals that individuals with many friends may appear to be focusing too much on Facebook, making friends out of desperation rather than popularity, spending a great deal of time on their computer ostensibly trying to make connections in a computer-mediated environment where they feel more comfortable rather than in face-to-face social interaction [ 5 ]. Moreover, a study among college freshman revealed that social media prevents people from being social and networking in person [ 6 ].

3 Experimental Design

This research study was divided into two parts during the academic year 2013–2014. Part one, conducted during fall semester 2013, had the purpose of understanding how and why young adults use their mobile devices, as well as how the students describe and identify with their mobile devices. This was done by distributing an online survey to several Kean University student communities: various majors, fraternity and sorority groups, sports groups, etc. The data revealed that users primarily used their mobile devices for social media and entertainment purposes. The surveyed individuals indicated that they mainly accessed mobile apps like Facebook, Pinterest, Twitter, and Instagram, to communicate, interact, and share many parts of their daily life with their friends and peers.

Based on the data collected during part one, a different approach and purpose was used for part two, with the goal being to understand how social media activities shape the communication skills of individuals and reflects their attitudes, attention, interests, and activities. Additionally, research included how young adult communication needs change through the use of different social media platforms, and if a pattern can be predicted from the users’ behavior on the social media platforms. Part two of this research was conducted by having 30 one-on-one interviews with young adults who are college students. During this interview key questions were asked in order to understand if there is a significant amount of interpersonal interaction between users and their peers. Interpersonal interaction is a communication process that involves the exchange of information, feelings and meaning by means of verbal or non-verbal messages. For the purposes of this paper, only the data collected during spring 2014 is presented.

4 Data Collection

Through interviews, accurate results of the interaction of young adults with social media were collected. These interviews involved 30 one-on-one conversations with Kean University students. Having one-on-one interviews with participants allowed for individual results, first responses from the participant, without permitting responses being skewed or influenced by other participants, such as might occur in group interviews. It also allows users to give truthful answers, in contrast to an online or paper survey, as they might have second thoughts about an answer and change it. The one-on-one interviews consisted of ten open-ended questions, which were aimed to answer, and ultimately determine, how social media interaction involuntarily influences, positively or negatively, an individual’s attitude, attention, interests, and social/personal activities. The largest motive behind the questions was to determine how individual communication skills, formally and informally, have changed from interacting with various social media platforms. The interviews, along with being recorded on paper, were also video and audio-recorded. The average time for each interview was between two to ten minutes. These interviews were held in quiet labs and during off-times, so that the responses could be given and recorded clearly and without distraction (Fig.  1 ). A total of 19 females and 11 males participated, with ages ranging from 19 to 28 years old.

Female participant during one-on-one interview

After conducting the interviews and analyzing the data collected, it was determined that the age when participants, both male and female, first began to use social media ranged between 9 to 17 years. It was found that, generally, males began to use social media around the age of 13, whereas females started around the age of 12. The average age for males starting to use social media is about 12.909 with a standard deviation of 2.343. For females, the average age is 12.263 with a standard deviation of 1.627. From this, we can determine that males generally begin to use social media around the age of 13, whereas females begin around the age of 12.

After determining the average age of when participants started using social media, it was necessary to find which social media platforms they had as a basis; meaning which social media platform they first used. MySpace was the first social media used by twenty-three participants, followed by Facebook with three users, and Mi Gente by only one user, with two participants not using social media at all. It was interesting to find that all of the participants who started using Myspace migrated to Facebook. The reasoning provided was that “everyone [they knew] started to use Facebook.” According to the participants, Facebook was “more interactive” and was “extremely easy to use.” The participants also stated that Myspace was becoming suitable for a younger user base, and it got boring because they needed to keep changing their profile backgrounds and modifying their top friends, which caused rifts or “popularity issues” between friends. After finding out which platform they started from, it was also essential to find out which platform they currently use. However, one platform that seemed to be used by all participants to keep up-to-date with their friends and acquaintances was Instagram, a picture and video-based social media platform. Another surprising finding was that many users did not use Pinterest at all, or had not even heard of the platform. After determining which social media platforms the users migrated to, it was essential to identify what caused the users to move from one platform to another. What are the merits of a certain platform that caused the users to migrate to it, and what are the drawbacks of another platform that caused users to migrate from it or simply not use it all?

4.1 Social Interaction Changes

For some participants social interaction had a chance for a positive outcome, while others viewed it in a more negative aspect. The participants were asked if their social interactions have changed since they were first exposed to social media (Table  1 ). One participant stated that “it is easier to just look at a social media page to see how friends and family are doing rather than have a one-on-one interaction.” As for people’s attitudes, they would rather comment or “like” a picture than stop and have a quick conversation. On the other hand, another participant felt that social media helped them when talking and expressing opinions on topics that they generally would not have discussed in person. Moreover, the participants are aware of the actions and thing that they are doing but continue to do it because they feel comfortable and did not desire to have one-on-one interactions with people.

The participants were also asked to explain how social media changed their communication and interactions during the years of using social media (Table  2 ). The data shows that participants interact less in person because they are relating more via online pictures and status. For other participants, it made them more cautious and even afraid of putting any personal information online because it might cause problems or rifts in their life. On the contrary, some participants stated that their communication and interaction is the same; however, they were able to see how it had changed for the people that are around them. A participant stated that “internet/social media is a power tool that allows people to be whatever they want and in a way it creates popularity, but once again they walk around acting like they do not know you and ‘like’ your pictures the next day.”

5 Discussion

The data illustrated in this paper shows how much the introduction and usage of social media has impacted the interaction and communication of young adults. The future of interaction and communication was also presented as a possibility, if the current trend continues with young adults and social media or online communities. This raises the notion of possibly not having any social, in-person interaction and having all communication or interaction online and virtually with all family and friends.

6 Conclusion

Referring back to the question asked during the introduction: how much has social media impacted the way we communicate and interact with each other? After reviewing all the findings, seeing the relationship individuals have with their mobile phones, and comparing social media platforms, it is clear that many young adults have an emotional attachment with their mobile device and want interaction that is quick and to the point, with minimal “in-person” contact. Many young adults prefer to use their mobile device to send a text message or interact via social media. This is due to their comfort level being higher while posting via social media applications, as opposed to in-person interaction. To successfully and accurately answer the question: yes, social media has had a very positive and negative effect on the way we communicate and interact with each other. However, how effective is this method of “virtual” communication and interaction in the real world?

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Jimenez, Y., Morreale, P. (2015). Social Media Use and Impact on Interpersonal Communication. In: Stephanidis, C. (eds) HCI International 2015 - Posters’ Extended Abstracts. HCI 2015. Communications in Computer and Information Science, vol 529. Springer, Cham. https://doi.org/10.1007/978-3-319-21383-5_15

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Adolescence

More research questions the “social media hypothesis” of mental health, a new study shows that social media does not lead to anxiety or depression..

Posted August 10, 2023 | Reviewed by Gary Drevitch

• What Changes During Adolescence?
• Find a therapist to support kids and teens
• Many believe that social media causes teens to experience depression and anxiety, despite lacking evidence.
• A new study found that when teenagers used social media more, their mental health did not change over time.
• Mainstream media should devote more coverage to studies like this one.

As I’ve discussed previously , conventional wisdom suggests that using social media promotes poor mental health, especially in teenagers . But there is good reason to question this idea. As more high-quality research becomes available, we can see room for nuance and see that social media is not consistently detrimental to everyone’s well-being.

A critical limitation in many existing studies on this topic is that they are cross-sectional. This means all variables are assessed only once, and at the same time. This isn’t necessarily a bad thing; it just means we don’t know how behavioral changes over time might be associated with changes in emotional variables. Longitudinal research helps us to better understand how change happens by measuring these variables repeatedly over a period of months or even years.

Longitudinal research is especially valuable in this case because some young people may use social media to alleviate distress , so we might observe that increases in depression or anxiety will predict increases in social media use , rather than the reverse. On the other hand, if the social media hypothesis is correct, then as teenagers spend more and more time online, this should be followed by decreased mental health (i.e., greater anxiety/depression). But that’s not what the data reveal.

What Researchers Found

A research team in Norway recently published a study in which they tracked young people aged 10-16, and assessed them every 2 years. Each time, the researchers interviewed participants about their behaviors online (e.g., posting photos, “liking,” or commenting on others' posts), and they conducted clinical assessments of depression and anxiety with standardized psychiatric measures. The researchers found no evidence that increased social media use was followed by elevated anxiety or depression. This means that as these teenagers used more social media, their mental health did not change. These findings directly contradict the idea that social media use leads to poor psychological well-being.

The authors are careful to note that even though social media did not make teenagers feel worse, on average, it also did not make them feel better. So, social media use may not have an overall negative or positive effect for the average teenager. This idea is consistent with what I have argued previously , which is that social media use may have differential effects depending on the user’s initial motivations. When people are motivated to use social media because they find it interesting or rewarding, then it’s likelier to make them happy, whereas when they feel compelled or obligated to use it, then it’s likelier to make them feel worse. Motivations matter more than the technology itself.

The researchers also suggest that perhaps subgroups of teenagers may experience different outcomes following social media use, such as those who are bullied or have low self-esteem . The specific content that people view on social media may also play a role. It is also true that digital technologies change rapidly and we cannot assume that all future forms of social media will operate the same way psychologically. New applications have the potential to be better or worse than what people currently use.

Time Trend Data Are Inconclusive

Those who hold with the “social media hypothesis” of mental health will often point to time trend data as evidence. They argue that because social media use has risen in teenagers over the past 15 years, and that teen depression and anxiety has also risen over the same period of time, then those two trends are likely connected.

But if that were true, we ought to be able to observe this trend happening during teenagers’ lives. The fact is, we do not observe this pattern, and these null findings should make us skeptical about such claims. When researchers track teenagers’ mental health over a span of years, there is no link between their social media use and their experiences of depression or anxiety. In the words of the authors , “ the frequency with which adolescents engage in behaviors like posting, liking, and commenting on others’ posts does not influence their risk for symptoms of depression and anxiety .”

It would be great to see more mainstream media coverage of studies like this, especially considering the widespread belief that if young people are permitted to use social media, their mental health will deteriorate. Perhaps parents of teenagers can take some comfort in the fact that for the average user, there is little risk of this.

Cauberghe, V., Van Wesenbeeck, I., De Jans, S., Hudders, L., & Ponnet, K. (2021). How Adolescents Use Social Media to Cope with Feelings of Loneliness and Anxiety During COVID-19 Lockdown. Cyberpsychology, behavior and social networking , 24 (4), 250–257. https://doi.org/10.1089/cyber.2020.0478

Puukko, K., Hietajärvi, L., Maksniemi, E., Alho, K., & Salmela-Aro, K. (2020). Social Media Use and Depressive Symptoms—A Longitudinal Study from Early to Late Adolescence. International Journal of Environmental Research and Public Health , 17 (16), 5921. MDPI AG. Retrieved from http://dx.doi.org/10.3390/ijerph17165921

Steinsbekk, S., Nesi, J., & Wichstrøm, L. (2023). Social media behaviors and symptoms of anxiety and depression. A four-wave cohort study from age 10–16 years. Computers in Human Behavior , 147 , 107859.

Dylan Selterman, Ph.D., is an Associate Teaching Professor at Johns Hopkins University in the Department of Psychological and Brain Sciences. He teaches courses and conducts research on personality traits, happiness, relationships, morality/ethics, game theory, political psychology, and more.

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74 Best Social Media Research Paper Topics

Whether in college or high school, you will come across research writing as a student. In most cases, the topic of research is assigned by your teacher/professor. Other times, students have to come up with their topic. Research writing in school is inescapable. It’s a task you are bound to undertake to fulfill your academic requirements. If you are in college, there are several topics for research depending on your discipline. For high school students, the topic is usually given. In this article, we focus on social media and topics about social media.

A social media paper is a research paper about social media that studies social media generally or an aspect of it. To write research papers on social media, you’ll need to conduct thorough research for materials and scholarly materials that’ll assist you. For social media, most of the scholarly works will be media-focused.

Sometimes, Professors or teachers ask students to write an essay or research a topic without narrowing it down. In that case, students will have to develop specific research topics. If you’re writing a paper on social media, we’ve provided you with helpful topics to consider for research.

How to Start a Social Media Research Paper

Social media topics to write about, social media research topics for college students, interesting topics to research for fun, research questions about social media, social media essay topics for high school students, narrow research topic ideas students can consider, research paper on social media marketing, good social topics for research papers, easy social issues to write about, social science research topics for college students, interesting research topics for high school students, comprehensive social networking research papers, final words about social media topics.

Before giving a research writing, Professors and teachers believe students already know how to write one. Not every student knows how to write a research paper in most cases.

Research writing follows a systematic pattern, which applies to research on social media. Below is the pattern of a research paper to use;

• Paper title
• Introduction
• Statement of problem
• Research methodology
• Research objective
• Critical analysis
• Results and discussion

Every research follows this basic pattern, and it also applies to your research paper on social media.

Social media has become a powerful tool for engagement of various kinds. Before now, social media was merely apps used for interpersonal affairs. Today, with the modification of digital technology, social media encompasses a lot more. Below are some social media topics to write about.

• The impact of social media in promoting interpersonal relationships
• A study on how social media is a vital tool for social change
• Social media censorship: A new form of restriction on freedom of speech
• The constantly growing oversharing nature of social media
• Social media is a vital tool for political campaign
• The proliferation of social media platforms into a buying space
• The juxtaposition of personal engagement and business on social media platforms

There is a wide range of topics to coin from social media for college students because social media is a platform with diverse issues that can form into topics. Here are some research topics about social media to consider.

• Breach of Privacy: A study on the ability of the government to monitor personal affairs on social media
• A study of the toxicity brewing within social media
• The increased cyberbullying perpetrated on social media platforms
• The evolution of Twitter into a space for diverse conversations
• A study of the emergence and growth of social media over the years
• Effects of social media: How social media is breeding laziness amongst children
• Social media as a distraction tool for students

If you are searching for interesting topics, there are many interesting research topics on social media. Examples of research paper topics that sound fun to choose from include;

• A study on how the emergence of social media and social media advertising has infiltrated its primary purpose
• An evaluation of how social media has created employment opportunities for people
• Social media influence and its negative impact on society
• Advertising on social media: Will influencer businesses take over advertising agencies?
• A study on ways to improve advertisement for social media engagement
• A look into how social media creates a distorted view of real life
• Social media and real-life: Does social media obscure reality?

Research questions are helpful when carrying out research in a particular field. To know more about your thesis on social media, you will need to create research questions on social media to help inform your writing. Some social media research questions to ask are;

• Are social media platforms designed to be addictive?
• What is a social media Algorithm, and how to navigate it?
• To what extent are personal data stored on social app databases protected?
• Can social media owners avoid government monitoring?
• Should parents allow their children to navigate social media before they are 15?
• Have social media jobs come to stay, or are they temporary?
• Is social media influencer culture overtaking celebrity culture?
• To what extent can social media help to curb racism and homophobia?
• Does social media exacerbate or curb discriminatory practices?
• Is social media an effective tool for learning?

Everyone has access to social media apps until they’ve reached a certain age. There are several social media essay topics for high school students to write about. Some social media titles for essays include;

• How social media affects the academic performance of students
• Why the use of social media is prohibited during school hours
• Why students are obsessed with Tiktok
• Running a profitable social media business while in high school and the challenges
• The dangers of overusing editing apps
• A critical essay on how editing apps and filters promote an unrealistic idea of beauty
• The death of TV: how social media has stolen student’s interest

The challenge students have with their topic ideas for research papers is that they’re broad. A good social media thesis topic should be narrowed down. Narrowing a topic down helps you during research to focus on an issue.

Some narrow social media topics for the research paper include;

• A study of how social media is overtaking Television in entertainment
• A study of how social media has overtaken traditional journalism
• An evaluation of the rise of influencer culture on Instagram
• YouTube and how it has created sustainable income for black content creators
• A comparative study of social media managers and content creators
• A study of the decline of Instagram since the emergence of Tiktok
• How Twitter breeds transphobic conversations

There are several areas of social media to focus your research on. If you are looking for some social media marketing topics, below are some social media research paper topics to consider;

• Influencer culture and a modified model of mouth-to-mouth marketing
• The growth of video marketing on Instagram
• Social media managers as an essential part of online marketing
• A study on how social media stories are optimized for marketing
• An analysis of social media marketing and its impact on customer behavior
• An evaluation of target marketing on social media

There are so many topics to choose from in this aspect. Some social issues research paper topics to explore are;

• The growth of cyberattacks and cyberstalking in social media
• Social media and how it promotes an unrealistic idea of life
• Social media and the many impacts it has on users and businesses
• Social media detox: Importance of taking scheduled social media breaks
• How social media enable conversation on social challenges

Writing a research paper on social issues touches on various areas. Some are challenging, while others are easier to navigate.

Below are some of the easy social issues topics to choose from.

• The growing issue of women’s and trans people’s rights
• Religious bigotry and how it affects social progress
• Sustainable living and why it’s important to the society
• The social impact of climate change and global warming

Social science is a broad discipline. If you are looking for social science essay topics, below are some social science topics for research papers to look into;

• Consumerism and how it’s perpetrated on social media
• How religious beliefs impact social relationships
• Inflation and how it affects the economy of a nation
• A study of the limited availability of work opportunities for minority groups
• A look into the concept of “low wage” jobs

Research writing is not always technical or challenging. Sometimes, it can be fun to write. It all depends on your choice of topic. Below are some topics on social media that are fun to work on;

• The importance of social media branding for small businesses
• A look into the monetization of Instagram
• User engagement and how it can be converted into business leads
• The study of emojis and their role in social media engagement
• From Instagram to Tiktok: the poaching nature of social media apps

Research writing on social media networking studies social networking and its design and promotion on social media platforms. Some research papers on social media networking are;

• The impact of social media networking on business owners
• Social media networking and how it impacts influencer culture
• Social media and how it’s used to build and develop social relationships
• How social media made social networking services easier

Social media research writing is one of the most interesting research to conduct. It cuts across several interesting areas. The writer can handle almost every aspect of the dissertation or thesis statement about social media . But, students who find it challenging should seek professional help. You can reach out to  our expert team of writers to help you handle every element of your writing. We have the best on our team who are always ready to give you their best.

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Americans’ Social Media Use

Youtube and facebook are by far the most used online platforms among u.s. adults; tiktok’s user base has grown since 2021, table of contents.

• Which social media sites do Americans use most?
• TikTok sees growth since 2021
• Stark age differences in who uses each app or site
• Other demographic differences in use of online platforms
• Acknowledgments
• 2023 National Public Opinion Reference Survey (NPORS) Methodology

To better understand Americans’ social media use, Pew Research Center surveyed 5,733 U.S. adults from May 19 to Sept. 5, 2023. Ipsos conducted this National Public Opinion Reference Survey (NPORS) for the Center using address-based sampling and a multimode protocol that included both web and mail. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race and ethnicity, education and other categories.

Polls from 2000 to 2021 were conducted via phone. For more on this mode shift, read our Q&A .

Here are the questions used for this analysis , along with responses, and  its methodology ­­­.

A note on terminology: Our May-September 2023 survey was already in the field when Twitter changed its name to “X.” The terms  Twitter  and  X  are both used in this report to refer to the same platform.

Social media platforms faced a range of controversies in recent years, including concerns over misinformation and data privacy . Even so, U.S. adults use a wide range of sites and apps, especially YouTube and Facebook. And TikTok – which some Congress members previously called to ban – saw growth in its user base.

These findings come from a Pew Research Center survey of 5,733 U.S. adults conducted May 19-Sept. 5, 2023.

YouTube by and large is the most widely used online platform measured in our survey. Roughly eight-in-ten U.S. adults (83%) report ever using the video-based platform.

While a somewhat lower share reports using it, Facebook is also a dominant player in the online landscape. Most Americans (68%) report using the social media platform.

Additionally, roughly half of U.S. adults (47%) say they use Instagram .

The other sites and apps asked about are not as widely used , but a fair portion of Americans still use them:

• 27% to 35% of U.S. adults use Pinterest, TikTok, LinkedIn, WhatsApp and Snapchat.
• About one-in-five say they use Twitter (recently renamed “X”) and Reddit.  

This year is the first time we asked about BeReal, a photo-based platform launched in 2020. Just 3% of U.S. adults report using it.

Recent Center findings show that YouTube also dominates the social media landscape among U.S. teens .

One platform – TikTok – stands out for growth of its user base. A third of U.S. adults (33%) say they use the video-based platform, up 12 percentage points from 2021 (21%).

The other sites asked about had more modest or no growth over the past couple of years. For instance, while YouTube and Facebook dominate the social media landscape, the shares of adults who use these platforms has remained stable since 2021.

The Center has been tracking use of online platforms for many years. Recently, we shifted from gathering responses via telephone to the web and mail. Mode changes can affect study results in a number of ways, therefore we have to take a cautious approach when examining how things have – or have not – changed since our last study on these topics in 2021. For more details on this shift, please read our Q&A .

Adults under 30 are far more likely than their older counterparts to use many of the online platforms. These findings are consistent with previous Center data .

Age gaps are especially large for Instagram, Snapchat and TikTok – platforms that are used by majorities of adults under 30. For example:

• 78% of 18- to 29-year-olds say they use Instagram, far higher than the share among those 65 and older (15%).
• 65% of U.S. adults under 30 report using Snapchat, compared with just 4% of the oldest age cohort.
• 62% of 18- to 29-year-olds say they use TikTok, much higher than the share among adults ages 65 years and older (10%).
• Americans ages 30 to 49 and 50 to 64 fall somewhere in between for all three platforms.

YouTube and Facebook are the only two platforms that majorities of all age groups use. That said, there is still a large age gap between the youngest and oldest adults when it comes to use of YouTube. The age gap for Facebook, though, is much smaller.

Americans ages 30 to 49 stand out for using three of the platforms – LinkedIn, WhatsApp and Facebook – at higher rates. For instance, 40% of this age group uses LinkedIn, higher than the roughly three-in-ten among those ages 18 to 29 and 50 to 64. And just 12% of those 65 and older say the same. 

Overall, a large majority of the youngest adults use multiple sites and apps. About three-quarters of adults under 30 (74%) use at least five of the platforms asked about. This is far higher than the shares of those ages 30 to 49 (53%), 50 to 64 (30%), and ages 65 and older (8%) who say the same.  

Refer to our social media fact sheet for more detailed data by age for each site and app.

A number of demographic differences emerge in who uses each platform. Some of these include the following:

• Race and ethnicity: Roughly six-in-ten Hispanic (58%) and Asian (57%) adults report using Instagram, somewhat higher than the shares among Black (46%) and White (43%) adults. 1
• Gender: Women are more likely than their male counterparts to say they use the platform.
• Education: Those with some college education and those with a college degree report using it at somewhat higher rates than those who have a high school degree or less education.
• Race and ethnicity: Hispanic adults are particularly likely to use TikTok, with 49% saying they use it, higher than Black adults (39%). Even smaller shares of Asian (29%) and White (28%) adults say the same.
• Gender: Women use the platform at higher rates than men (40% vs. 25%).
• Education: Americans with higher levels of formal education are especially likely to use LinkedIn. For instance, 53% of Americans with at least a bachelor’s degree report using the platform, far higher than among those who have some college education (28%) and those who have a high school degree or less education (10%). This is the largest educational difference measured across any of the platforms asked about.

Twitter (renamed “X”)

• Household income: Adults with higher household incomes use Twitter at somewhat higher rates. For instance, 29% of U.S. adults who have an annual household income of at least $100,000 say they use the platform. This compares with one-in-five among those with annual household incomes of $70,000 to $99,999, and around one-in-five among those with annual incomes of less than $30,000 and those between $30,000 and $69,999.
• Gender: Women are far more likely to use Pinterest than men (50% vs. 19%).
• Race and ethnicity: 54% of Hispanic adults and 51% of Asian adults report using WhatsApp. This compares with 31% of Black adults and even smaller shares of those who are White (20%).

• Estimates for Asian adults are representative of English speakers only. ↩

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David Wallace-Wells

Are smartphones driving our teens to depression.

By David Wallace-Wells

Opinion Writer

Here is a story. In 2007, Apple released the iPhone, initiating the smartphone revolution that would quickly transform the world. In 2010, it added a front-facing camera, helping shift the social-media landscape toward images, especially selfies. Partly as a result, in the five years that followed, the nature of childhood and especially adolescence was fundamentally changed — a “great rewiring,” in the words of the social psychologist Jonathan Haidt — such that between 2010 and 2015 mental health and well-being plummeted and suffering and despair exploded, particularly among teenage girls.

For young women, rates of hospitalization for nonfatal self-harm in the United States, which had bottomed out in 2009, started to rise again, according to data reported to the C.D.C., taking a leap beginning in 2012 and another beginning in 2016, and producing , over about a decade, an alarming 48 percent increase in such emergency room visits among American girls ages 15 to 19 and a shocking 188 percent increase among girls ages 10 to14.

Here is another story. In 2011, as part of the rollout of the Affordable Care Act, the Department of Health and Human Services issued a new set of guidelines that recommended that teenage girls should be screened annually for depression by their primary care physicians and that same year required that insurance providers cover such screenings in full. In 2015, H.H.S. finally mandated a coding change, proposed by the World Health Organization almost two decades before, that required hospitals to record whether an injury was self-inflicted or accidental — and which seemingly overnight nearly doubled rates for self-harm across all demographic groups. Soon thereafter, the coding of suicidal ideation was also updated. The effect of these bureaucratic changes on hospitalization data presumably varied from place to place. But in one place where it has been studied systematically, New Jersey, where 90 percent of children had health coverage even before the A.C.A., researchers have found that the changes explain nearly all of the state’s apparent upward trend in suicide-related hospital visits, turning what were “essentially flat” trendlines into something that looked like a youth mental health “crisis.”

Could both of these stories be partially true? Of course: Emotional distress among teenagers may be genuinely growing while simultaneous bureaucratic and cultural changes — more focus on mental health, destigmatization, growing comfort with therapy and medication — exaggerate the underlying trends. (This is what Adriana Corredor-Waldron, a co-author of the New Jersey study, believes — that suicidal behavior is distressingly high among teenagers in the United States and that many of our conventional measures are not very reliable to assess changes in suicidal behavior over time.) But over the past several years, Americans worrying over the well-being of teenagers have heard much less about that second story, which emphasizes changes in the broader culture of mental illness, screening guidelines and treatment, than the first one, which suggests smartphones and social-media use explain a whole raft of concerns about the well-being of the country’s youth.

When the smartphone thesis first came to prominence more than six years ago, advanced by Haidt’s sometime collaborator Jean Twenge, there was a fair amount of skepticism from scientists and social scientists and other commentators: Were teenagers really suffering that much? they asked. How much in this messy world could you pin on one piece of technology anyway? But some things have changed since then, including the conventional liberal perspective on the virtues of Big Tech, and, in the past few years, as more data has rolled in and more red flags have been raised about American teenagers — about the culture of college campuses, about the political hopelessness or neuroticism or radicalism or fatalism of teenagers, about a growing political gender divide, about how often they socialize or drink or have sex — a two-part conventional wisdom has taken hold across the pundit class. First, that American teenagers are experiencing a mental health crisis; second, that it is the fault of phones.

“Smartphones and social media are destroying children’s mental health,” the Financial Times declared last spring. This spring, Haidt’s new book on the subject, The Anxious Generation: How the Great Rewiring of Childhood Is Causing an Epidemic of Mental Illness, debuted at the top of the New York Times best-seller list. In its review of the book, The Guardian described the smartphone as “a pocket full of poison,” and in an essay , The New Yorker accepted as a given that Gen Z was in the midst of a “mental health emergency” and that “social media is bad for young people.” “Parents could see their phone-obsessed children changing and succumbing to distress,” The Wall Street Journal reflected . “Now we know the true horror of what happened.”

But, well, do we? Over the past five years, “Is it the phones?” has become “It’s probably the phones,” particularly among an anxious older generation processing bleak-looking charts of teenage mental health on social media as they are scrolling on their own phones. But however much we may think we know about how corrosive screen time is to mental health, the data looks murkier and more ambiguous than the headlines suggest — or than our own private anxieties, as parents and smartphone addicts, seem to tell us.

What do we really know about the state of mental health among teenagers today? Suicide offers the most concrete measure of emotional distress, and rates among American teenagers ages 15 to 19 have indeed risen over the past decade or so, to about 11.8 deaths per 100,000 in 2021 from about 7.5 deaths per 100,000 in 2009. But the American suicide epidemic is not confined to teenagers. In 2022, the rate had increased roughly as much since 2000 for the country as a whole, suggesting a national story both broader and more complicated than one focused on the emotional vulnerabilities of teenagers to Instagram. And among the teenagers of other rich countries, there is essentially no sign of a similar pattern. As Max Roser of Our World in Data recently documented , suicide rates among older teenagers and young adults have held roughly steady or declined over the same time period in France, Spain, Italy, Austria, Germany, Greece, Poland, Norway and Belgium. In Sweden there were only very small increases.

Is there a stronger distress signal in the data for young women? Yes, somewhat. According to an international analysis by The Economist, suicide rates among young women in 17 wealthy countries have grown since 2003, by about 17 percent, to a 2020 rate of 3.5 suicides per 100,000 people. The rate among young women has always been low, compared with other groups, and among the countries in the Economist data set, the rate among male teenagers, which has hardly grown at all, remains almost twice as high. Among men in their 50s, the rate is more than seven times as high.

In some countries, we see concerning signs of convergence by gender and age, with suicide rates among young women growing closer to other demographic groups. But the pattern, across countries, is quite varied. In Denmark, where smartphone penetration was the highest in the world in 2017, rates of hospitalization for self-harm among 10- to 19-year-olds fell by more than 40 percent between 2008 and 2016. In Germany, there are today barely one-quarter as many suicides among women between 15 and 20 as there were in the early 1980s, and the number has been remarkably flat for more than two decades. In the United States, suicide rates for young men are still three and a half times as high as for young women, the recent increases have been larger in absolute terms among young men than among young women, and suicide rates for all teenagers have been gradually declining since 2018. In 2022, the latest year for which C.D.C. data is available, suicide declined by 18 percent for Americans ages 10 to 14 and 9 percent for those ages 15 to 24.

None of this is to say that everything is fine — that the kids are perfectly all right, that there is no sign at all of worsening mental health among teenagers, or that there isn’t something significant and even potentially damaging about smartphone use and social media. Phones have changed us, and are still changing us, as anyone using one or observing the world through them knows well. But are they generating an obvious mental health crisis?

The picture that emerges from the suicide data is mixed and complicated to parse. Suicide is the hardest-to-dispute measure of despair, but not the most capacious. But while rates of depression and anxiety have grown strikingly for teenagers in certain parts of the world, including the U.S., it’s tricky to disentangle those increases from growing mental-health awareness and destigmatization, and attempts to measure the phenomenon in different ways can yield very different results.

According to data Haidt uses, from the U.S. National Survey on Drug Use and Health, conducted by the Substance Abuse and Mental Health Services Administration, the percent of teenage girls reporting major depressive episodes in the last year grew by about 50 percent between 2005 and 2017, for instance, during which time the share of teenage boys reporting the same grew by roughly 75 percent from a lower level. But in a biannual C.D.C. survey of teenage mental health, the share of teenagers reporting that they had been persistently sad for a period of at least two weeks in the past year grew from only 28.5 percent in 2005 to 31.5 percent in 2017. Two different surveys tracked exactly the same period, and one showed an enormous increase in depression while the other showed almost no change at all.

And if the rise of mood disorders were a straightforward effect of the smartphone, you’d expect to see it everywhere smartphones were, and, as with suicide, you don’t. In Britain, the share of young people who reported “feeling down” or experiencing depression grew from 31 percent in 2012 to 38 percent on the eve of the pandemic and to 41 percent in 2021. That is significant, though by other measures British teenagers appear, if more depressed than they were in the 2000s, not much more depressed than they were in the 1990s.

Overall, when you dig into the country-by-country data, many places seem to be registering increases in depression among teenagers, particularly among the countries of Western Europe and North America. But the trends are hard to disentangle from changes in diagnostic patterns and the medicalization of sadness, as Lucy Foulkes has argued , and the picture varies considerably from country to country. In Canada , for instance, surveys of teenagers’ well-being show a significant decline between 2015 and 2021, particularly among young women; in South Korea rates of depressive episodes among teenagers fell by 35 percent between 2006 and 2018.

Because much of our sense of teenage well-being comes from self-reported surveys, when you ask questions in different ways, the answers vary enormously. Haidt likes to cite data collected as part of an international standardized test program called PISA, which adds a few questions about loneliness at school to its sections covering progress in math, science and reading, and has found a pattern of increasing loneliness over the past decade. But according to the World Happiness Report , life satisfaction among those ages 15 to 24 around the world has been improving pretty steadily since 2013, with more significant gains among women, as the smartphone completed its global takeover, with a slight dip during the first two years of the pandemic. An international review published in 2020, examining more than 900,000 adolescents in 36 countries, showed no change in life satisfaction between 2002 and 2018.

“It doesn’t look like there’s one big uniform thing happening to people’s mental health,” said Andrew Przybylski, a professor at Oxford. “In some particular places, there are some measures moving in the wrong direction. But if I had to describe the global trend over the last decade, I would say there is no uniform trend showing a global crisis, and, where things are getting worse for teenagers, no evidence that it is the result of the spread of technology.”

If Haidt is the public face of worry about teenagers and phones, Przybylski is probably the most prominent skeptic of the thesis. Others include Amy Orben, at the University of Cambridge, who in January told The Guardian, “I think the concern about phones as a singular entity are overblown”; Chris Ferguson, at Stetson University, who is about to publish a new meta-analysis showing no relationship between smartphone use and well-being; and Candice Odgers, of the University of California, Irvine, who published a much-debated review of Haidt in Nature, in which she declared “the book’s repeated suggestion that digital technologies are rewiring our children’s brains and causing an epidemic of mental illness is not supported by science.”

Does that overstate the case? In a technical sense, I think, no: There may be some concerning changes in the underlying incidence of certain mood disorders among American teenagers over the past couple of decades, but they are hard to separate from changing methods of measuring and addressing mental health and mental illness. There isn’t great data on international trends in teenage suicide — but in those places with good reporting, the rates are generally not worsening — and the trends around anxiety, depression and well-being are ambiguous elsewhere in the world. And the association of those local increases with the rise of the smartphone, while now almost conventional wisdom among people like me, is, among specialists, very much a contested claim. Indeed, even Haidt, who has also emphasized broader changes to the culture of childhood , estimated that social media use is responsible for only about 10 percent to 15 percent of the variation in teenage well-being — which would be a significant correlation, given the complexities of adolescent life and of social science, but is also a much more measured estimate than you tend to see in headlines trumpeting the connection. And many others have arrived at much smaller estimates still.

But this all also raises the complicated question of what exactly we mean by “science,” in the context of social phenomena like these, and what standard of evidence we should be applying when asking whether something qualifies as a “crisis” or “emergency” and what we know about what may have caused it. There is a reason we rarely reduce broad social changes to monocausal explanations, whether we’re talking about the rapid decline of teenage pregnancy in the 2000s, or the spike in youth suicide in the late ’80s and early 1990s, or the rise in crime that began in the 1960s: Lives are far too complex to easily reduce to the influence of single factors, whether the factor is a recession or political conditions or, for that matter, climate breakdown.

To me, the number of places where rates of depression among teenagers are markedly on the rise is a legitimate cause for concern. But it is also worth remembering that, for instance, between the mid-1990s and the mid-2000s, diagnoses of American youth for bipolar disorder grew about 40-fold , and it is hard to find anyone who believes that change was a true reflection of underlying incidence. And when we find ourselves panicking over charts showing rapid increases in, say, the number of British girls who say they’re often unhappy or feel they are a failure, it’s worth keeping in mind that the charts were probably zoomed in to emphasize the spike, and the increase is only from about 5 percent of teenagers to about 10 percent in the first case, or from about 15 percent to about 20 percent in the second. It may also be the case, as Orben has emphasized , that smartphones and social media may be problematic for some teenagers without doing emotional damage to a majority of them. That’s not to say that in taking in the full scope of the problem, there is nothing there. But overall it is probably less than meets the eye.

If you are having thoughts of suicide, call or text 988 to reach the 988 Suicide and Crisis Lifeline or go to SpeakingOfSuicide.com/resources for a list of additional resources.

Further reading (and listening):

On Jonathan Haidt’s After Babel Substack , a series of admirable responses to critics of “The Anxious Generation” and the smartphone thesis by Haidt, his lead researcher Zach Rausch, and his sometime collaborator Jean Twenge.

In Vox, Eric Levitz weighs the body of evidence for and against the thesis.

Tom Chivers and Stuart Ritchie deliver a useful overview of the evidence and its limitations on the Studies Show podcast.

Five experts review the evidence for the smartphone hypothesis in The Guardian.

A Substack survey of “diagnostic inflation” and teenage mental health.

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Social media companies have too much political power, 78% of Americans say in Pew survey

Finally, something that both sides of the aisle can agree on: social media companies are too powerful.

According to a survey by the Pew Research Center , 78% of American adults say social media companies have too much influence on politics — to break it down by party, that’s 84% of surveyed Republicans and 74% of Democrats. Overall, this viewpoint has become 6% more popular since the last presidential election year.

Americans’ feelings about social media reflect that of their legislators. Some of the only political pursuits that have recently garnered significant bipartisan support have been efforts to hold social media platforms accountable. Senators Marsha Blackburn (R-TN) and Richard Blumenthal (D-CT) have been working across the aisle on their Kids Online Safety Act , a bill that would put a duty of care on social media platforms to keep children safe. However, some privacy advocates have criticized the bill’s potential to make adults more vulnerable to government surveillance.

Meanwhile, Senators Lindsey Graham (R-SC) and Elizabeth Warren (D-MA) have also forged an unlikely partnership to propose a bill that would create a commission to oversee big tech platforms.

“The only thing worse than me doing a bill with Elizabeth Warren is her doing a bill with me,” Graham said at a Senate hearing in January.

It’s obvious why Americans think tech companies have too much political power — since the 2020 survey, social platforms were used to coordinate an attack on the Capitol, and then as a result, a sitting president got banned from those platforms for egging on those attacks. Meanwhile, the government is so concerned about the influence of Chinese-owned TikTok that President Biden just signed a bill that could ban the app for good.

But the views of conservative and liberal Americans diverge on the topic of tech companies’ bias. While 71% of Republicans surveyed said that big tech favors liberal perspectives over conservative ones, 50% of Democrats said that tech companies support each set of views equally. Only 15% of adults overall said that tech companies support conservatives over liberals.

These survey results make sense given the rise of explicitly conservative social platforms, like Rumble , Parler and Trump’s own Truth Social app.

During Biden’s presidency, government agencies like the FTC and DOJ have taken a sharper aim at tech companies. Some of the country’s biggest companies like Amazon , Apple and Meta have faced major lawsuits alleging monopolistic behaviors. But according to Pew’s survey, only 16% of U.S. adults think that tech companies should be regulated less than they are now. This percentage has grown since 2021, when Pew found that value to be 9%.

Liberals and conservatives may not agree on everything when it comes to tech policy, but the predominant perspective from this survey is clear: Americans are tired of the outsized influence of big tech.

Breaking down TikTok’s legal arguments around free speech, national security claims