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Article

Facebook Is “For Old People”—So Why Are We Still Studying It the Most? A Critical Look at Social Media in Science

by
Kamil Maciuk
1,*,
Michal Apollo
2,3,
Julia Skorupa
4,
Mateusz Jakubiak
5,
Yana Wengel
6 and
David C. Geary
7
1
Department of Integrated Geodesy and Cartography, AGH University of Krakow, 30-059 Krakow, Poland
2
Institute of Earth Sciences, University of Silesia in Katowice, 40-007 Katowice, Poland
3
Global Justice Program, Yale University, New Haven, CT 06520, USA
4
Faculty of Polish Studies, Jagiellonian University, 31-007 Krakow, Poland
5
Department of Environmental Management and Protection, AGH University of Krakow, 30-059 Krakow, Poland
6
Arizona State University Joint International Tourism College (HAITC), Hainan University, Haikou 571155, China
7
Department of Psychological Sciences, University of Missouri, Columbia, MO 65201, USA
*
Author to whom correspondence should be addressed.
Journal. Media 2025, 6(2), 62; https://doi.org/10.3390/journalmedia6020062 (registering DOI)
Submission received: 22 February 2025 / Revised: 11 April 2025 / Accepted: 11 April 2025 / Published: 26 April 2025
Browse Figures
Versions Notes

Abstract

:
Social media (SM) platforms allow users to communicate rapidly, exchange information, and create and share real-time content. Currently, 4.5 billion people use social media worldwide, making it an influential part of daily life. Beyond information sharing, social media facilitates communication, transfers information, and serves as a platform for advertising and shaping public opinion. Researchers analyse these aspects to understand and describe societal realities. The primary purpose of this paper is to analyse social media’s impact on global research. The research included an analysis of the most popular social platforms, considering the number of Web of Science (WoS) articles relating to them and the year in which the platform was established or the Monthly Active Users (MAU) factor. Data were collected based on the WoS database in the topic (which contains texts of title, abstract, author keywords, and Keywords Plus) of the articles, where phrases containing names of SM platforms were used. Quantitative research is a type of research that analyses data numerically to find relationships and statistical regularities of searched phrases. The impact of social media on the dissemination of research and findings was analysed based on the results of the study and also on the literature data. This research reveals a lack of correlation between the number of articles indexed in the WoS and the MAU of individual social media platforms. This observation raises an important question: do social media researchers focus on studying the platforms used by the majority, thereby providing a more accurate representation of current social dynamics? This article is helpful for researchers, policymakers, and social media platform developers seeking to understand the role of social media in shaping modern communication and public discourse. The most important finding of the paper is the low correlation between the number of SM users and the impact of social media platforms on learning, as exemplified by the Twitter (Note: Twitter was an American social networking service rebranded as X in 2023. As the period of data analysed in this paper covered the years up to 2022, the authors decided to stay with the name Twitter) platform, which is the 17th largest SM platform but is the 2nd (after Facebook) in implications for science.

Graphical Abstract

1. Introduction

The term Web 2.0 refers to websites in which the primary role is played by content generated by the users of those websites. Contrary to the numbering of software versions, Web 2.0 is not a new World Wide Web or Internet but a unique way of using its existing resources. Web 2.0 sites change the paradigm of interaction between the site’s owners and its users, putting the creation of most content in the hands of users. Social media should be understood as websites in which user-generated content plays a primary role and are generally defined as Internet-driven, unrestricted, and enduring channels of mass personal communication that facilitate the perception of interactions among users, deriving value primarily from user-generated content. Carr and Hayes (2015). Social media is a platform on the internet that allows users to quickly communicate, exchange information, and create and transmit content published there (including text and voice messages, photos, videos, and links to external sites) (Kaplan & Haenlein, 2010).
The number of participants grows every year and often influences our reality (Barrot, 2021; Auxier & Anderson, 2021; Gudi et al., 2022; Kucharczuk et al., 2022; Olanrewaju et al., 2020). Moreover, social media is an excellent tool for shaping public awareness and sentiment (Kaplan & Haenlein, 2010; Zhuravskaya et al., 2020), including through paid methods. One of its most concerning uses is its potential to manipulate voters during election campaigns. This is particularly significant in countries that influence global politics, such as the United States. For example, Barack Obama used technology to overcome Hillary Clinton in the primaries (Talbot, 2008). Like many other innovators in the digital space, the Obama campaign did not create entirely new strategies but rather leveraged existing tools in innovative ways. Instead, by bolting together social networking applications under the banner of a movement, they made an unforeseen force to raise money, organise locally, fight smear campaigns, and get out the vote that helped them topple the Clinton machine and then John McCain and the Republicans. An analysis of the 2016 presidential campaign between Donald Trump and Hilary Clinton illustrated how voters can be manipulated through paid advertisements on social media or fake accounts, shaping public sentiment through posts and comments (Bossetta, 2018). Social media has been studied by researchers, mainly in sociology (Mishnick & Wise, 2024; Olan et al., 2024; Tähtinen, 2024; Xue et al., 2024), health studies (Arachchige, 2024; Pretorius, 2024; Shrivastava et al., 2024), or medicine (J. Chen & Wang, 2021; Montag et al., 2018; Puri et al., 2020; Raturi et al., 2024; Zhang et al., 2017). However, the spectrum of the analysed topic is vast and ranges from live events streaming (Haimson & Tang, 2017), through contingency planning during COVID-19 (Eghtesadi & Florea, 2020), to the source of health information (J. Chen & Wang, 2021; Paige et al., 2015) or interpersonal relations research (Cingel et al., 2022; Vaterlaus et al., 2016) to the virtual geographies of social networks (Papacharissi, 2009). Facebook and Twitter were the most studied sites, with other social media against these two representing significantly less (Kietzmann et al., 2011; Park et al., 2018; Wang, 2020). Of course, the use of SM in the electoral process is not the only—though significant—place where they are used, and scientists subject them to subsequent analysis.
Furthermore, Phua, Jin and Kim (Phua et al., 2017) studied SM in brand identification gratification, brand engagement and commitment, and membership intention. Wengel et al. (2022) proved that a seemingly trivial video about the “magnificent sunrise and sea of clouds” posted on TikTok quickly received 65,000 likes and turned Jianfengling Peak (Hainan, China) into a ”hot” destination overnight. Young et al. (2017) analysed four SM platforms in a college student society, but most participants were white females. Research shows that Snapchat and Instagram have the highest intensity (nearly equally), followed by Facebook and Twitter. SM was also the field of study as addiction, dividing participants into passive and active users. Research shows no association between escapism in passive Facebook use and passive addiction. Another aspect of social media usage is recruitment procedures, where LinkedIn and Facebook are most often used (Caers & Castelyns, 2011). Research shows that LinkedIn is not the only SM used in recruiting candidates. Facebook is also a tool that might give additional information about candidates (e.g., profile pictures, activity, etc.). SM can be used for predicting stock market moods (Bollen et al., 2011), preventing natural hazards by microblogging in awareness situations (M. Chan et al., 2012), and chronic diseases management (X. Chen et al., 2020) as a source of misinformation on COVID-19 (Li et al., 2020). In China, the WeChat Public Account feature allows users to access various information by subscribing to a specific account. This functionality has been widely adopted by organisations, including businesses, universities, and government agencies, as an effective platform for disseminating information to targeted audiences and engaging with them directly (Montag et al., 2018).
As the (very selective) analysis above illustrates, researchers analyse virtually all SM. However, do they study those with the most users or devote more space to selected specific types of SM? Although such studies have already been conducted, they focus on only six social media platforms and primarily examine titles indexed in the Web of Science (Blank & Lutz, 2017). The results (out of six) showed that only two social media platforms count in today’s science—Facebook (mainly due to the number of users) and Twitter (due to the nature of its target). In just one decade, social media has revolutionised many people’s lives and, in doing so, has attracted much attention, not only from industry but also from academia (Ngai et al., 2015). As information systems are moving beyond the organisational periphery and becoming part of the broader social context, research on strategic information systems must delve into the competitive environment of dynamic social systems (Kapoor et al., 2018). Thus, it is essential to include the right social media in the research, not the ones that seem right to us.
This research investigates whether there is a discrepancy between the most widely used social media platforms (in terms of monthly active users, MAU) and those most frequently studied in academic research indexed by the Web of Science (WoS). We do not assume a direct or necessary correlation between usage levels and scientific “worthiness” nor do we suggest that platforms with the highest user numbers should automatically receive the most scholarly attention. Instead, we aim to explore whether specific high-impact platforms—by their reach and influence—are being overlooked in academic literature and to raise questions about how research priorities align with evolving digital landscapes. The study thus invites reflection on whether the current distribution of scholarly attention across platforms corresponds with their broader societal significance.
The research includes an analysis of the most popular social platforms, considering the number of WoS articles dedicated to them, their year of establishment, and the MAU metric. Significantly, we also recognise that each platform’s design, user demographics, affordances, and cultural positioning profoundly influence its relevance for different research fields and that these qualitative differences must be considered alongside quantitative comparisons.

2. Methods and Data

In this paper, the authors use a Web of Science Core Collection database to determine the impact of social media on science, such as the number of appearances on the topic. In the WoS, topic means searches in title, abstract, author keywords, and Keywords Plus. Appearance of the phrase in title, abstract, or keywords means that it deals directly with the article. Authors limited the search to document types of “article” and “review article” only to avoid other kinds, such as letters, corrections, books, or conference proceedings papers, which do not affect journal impact factor (IF) and other indicators. Overall, items with any other document type, including editorial material, letters, and meeting abstracts, are not included in the denominator and thus have no impact on IF value (Clarivate, 2022). The authors chose the WoS database as one of the biggest, most known, and most impactful databases in the USA, Europe, and the rest of the world. Although the WoS database does not include all scientific publications, it is the most suitable for conducting the analysis that is the subject of the study.
The criterion for selecting the world’s most popular SM was the monthly active users factor (MAU, as of October 2022) (Wikipedia, 2022), which has at least 100 mln MAU. We examined the number of WoS appearances for each social media platform within the WoS topic. We analysed the top 15 platforms with the highest number of appearances. This approach was to study the impact of SM on science, not to study the largest SM, e.g., as the results of the analyses showed, Twitter is next to Facebook’s most impactful on science SM (share in the number of papers is 36% for Facebook and 30% for Twitter; see Table 1), but it is the 17th biggest according to MAU (Table 1).
MAU (monthly active users) is a key performance indicator (KPI) used by social networks to check their position in the market. Table 1 shows that Facebook is the most popular social media platform among users, followed closely by TikTok. Other platforms, including Messenger, Telegram, QQ, and Douyin, also hold prominent positions, while Instagram, WhatsApp, YouTube, and WeChat remain significant contenders.
While this study focuses on quantitative indicators—namely, article frequency in the WoS database and monthly active users (MAU)—we acknowledge that qualitative factors also shape the impact of social media on scientific research. These may include disciplinary norms, institutional interests, funding trends, or social and cultural perceptions of each platform’s relevance. As such, this study provides a preliminary, data-driven analysis that can serve as a foundation for future qualitative or mixed-methods research exploring why specific platforms attract more academic attention than others and how researchers interact with them in practice.

3. Results and Discussion

As expected, the number of MAU participants is not positively correlated with the number of articles indexed in the WoS (Figure 1). Treemap visualisation shows 13 SM platforms with at least 500 million MAU ranked by the number of MAU participants (Figure 1a) and the top 5 SM platforms ranked by the number of appearances in articles indexed in the WoS (Figure 1b). Only three platforms—Facebook, YouTube, and WhatsApp—reach over 2 billion monthly users.
Among these, Facebook is a social media platform, YouTube is an online video-sharing platform with social media features limited to comments on individual videos, and WhatsApp is a communication app. The following three platforms are Messenger (a communication app), WeChat (a digital ecosystem), and Instagram (a platform for sharing photos and short videos).
Apps such as WeChat and Douyin are Chinese products primarily used within the People’s Republic of China. WeChat, developed by Tencent, is an essential digital ecosystem that integrates messaging, social networking, and mobile payment functionalities, serving as a cornerstone of digital life in China. It enables users to communicate, share content, perform financial transactions, and access services within a single ecosystem, including e-commerce, transportation, and government platforms (Montag et al., 2018). While its widespread adoption underscores its significance in Chinese society, WeChat’s compliance with regulatory frameworks has raised critical discussions regarding privacy and data security. QQ, an instant messaging service launched in 1999 by Tencent, a multinational conglomerate based in China, offers a range of functions, including chat, email, file sharing, and features similar to traditional online forums or bulletin board systems. These services are accessible through the conventional internet and mobile phones, PDAs, and other emerging platforms. A standout feature of QQ is the QQ Group, which can accommodate anywhere from a few hundred to several thousand members. QQ groups provide a space for exclusive, member-only interactions and enable users to create hierarchical groups tailored to specific interests, purposes, and communication needs (Tai, 2022). Due to China’s vast population, these apps are among the world’s most widely used social media platforms, and their popularity in Asia is growing. Notably, within a single state with centralised governance, an app designed for daily interpersonal communication and another focused on entertainment, particularly entertainment and opinion-sharing, hold significant prominence (Harwit, 2017; Tu, 2016). Both applications operate under state supervision.
Douyin and its global counterpart, TikTok, are among the fastest-growing short video platforms worldwide. Both platforms, owned by ByteDance, share similar designs, functionality, and features but operate in distinct markets under different regulatory environments. Unlike other prominent mobile platforms, TikTok and Douyin are not affiliated with China’s “big three” tech giants—Baidu, Alibaba, and Tencent (BAT) or the US “big five” internet companies—Google, Amazon, Facebook, Apple, and Microsoft (GAFAM). Both platforms focus on short videos ranging from 15 to 60 s, predominantly featuring user-generated content (UGC) rather than professionally generated content (PGC) (Kaye et al., 2021).
Globally, the most widely used social network is Facebook. Most users are from India, followed by the USA, Indonesia, and Brazil. These are the most populous countries in the world. The most popular messengers are WhatsApp, Messenger, and Telegram. WhatsApp is the most popular messenger. It differs from the others mentioned by having an account linked to a phone number.
As more social media platforms are introduced to the market, one can see a tendency to make communicating information more straightforward and attractive. Still, at the same time, this accustoms our brains to quick and easy gratification, contributing to a reduced ability to concentrate for longer. One effect of this phenomenon is, for example, the negative impact of social media on short-term memory. Facebook (launched 2004) is a platform for posting pictures and longer user contributions. YouTube (2005) posts videos and films of unlimited duration. Instagram (2010) is mainly used for posting photos. TikTok (2016) is a platform where the average length of published videos is 2.24 min.
The authors were looking for a correlation between MAU and the presence of a given platform on the internet, but there was no such correlation (Figure 2). Figure 2 shows the top 13 social media (x-axis) platforms with the highest number of MAU (left y-axis, blue colour) and their launch year (right y-axis, red colour). The first top SM platforms were launched around 2010, and there is a slight correlation between the launch date and active users, but it is not a rule (e.g., QQ). Moreover, only 6 social media platforms have at least 1 billion MAU, most established around 2010. However, it must be noted that there are new SM platforms with an increasing year-to-year number of users, like TikTok or Telegram, established in 2017 and 2013, respectively. The newer the social platform, the lower the monthly rate of individual user logins on average; this may be related to users logging in once and not logging out (mainly using smartphone apps) for “convenience”. Over time, the use of social media changes, so an indicator in the form of the number of logins becomes unreliable.
The two values were correlated to test whether the number of MAU participants influences the amount of “time” (number of articles) devoted to space in articles indexed in the WoS database (Figure 3). Below is the graph showing the top 15 SM platforms with the biggest MAU and their impact on the WoS. The direction of SM is sorted in descending order of the number of occurrences in the WoS database (left y-axis, blue colour), with the number of MAU (right y-axis, red colour; the same data as in Figure 2). It is evident that Facebook appears most frequently in the Web of Science, followed closely by Twitter. The monthly number of logins, which determines the popularity of a medium, is high for Facebook and low for Twitter. Relating this observation to the frequency of a given social media in the WOS may be due to the specificity of the platforms. Facebook is an application aimed at the general population regardless of society status (it is egalitarian). For this reason, the number of users active at least once a month is higher than Twitter, which is aimed at a specific environment, as it is a microblogging platform used most often by public figures.
Figure 4 shows data similar to the previous figure, such as yearly WoS appearances, but also a WoS appearances ratio, which is calculated as the number of appearances. The ratio (left y-axis, ref) is calculated as the number of users per one paper in the WoS using a logarithmic scale to interpret the results better. The right y-axis shows how many WoS papers appear yearly since the year of the SM launches. The number of Twitter and Facebook mentions of scientific articles in the WoS per individual user of these SM platforms indicates their most significant impact on science. However, Twitter is ranked first in such terms. A comparison of the number of occurrences of the names of these two SM platforms in scientific articles in the WoS per year of their existence indicates the same positions in the ranking.
It is essential to recognise that a platform’s presence—or absence—in scientific literature is not solely determined by the number of users (MAU) but also by its function, openness, and sociopolitical context. Scientists frequently use platforms like Facebook, Twitter, LinkedIn, and YouTube to share research, engage with the public, and build professional networks, which may explain their stronger representation in academic publications. Conversely, platforms such as TikTok, WeChat, or Telegram, despite having large user bases, are less often used for scholarly communication. Moreover, bots, state-sponsored disinformation, and politically motivated content can shape how platforms are perceived and used in both public and academic contexts. These dynamics may influence the trustworthiness, research interest, and even accessibility of specific platforms, affecting their presence in databases like the WoS. Thus, platform-specific affordances and sociotechnical environments must be considered alongside user metrics.
Today’s most popular social networks are Facebook, Instagram, and TikTok (Weimann & Masri, 2020). This is due to publishers (primarily politicians) and users (political followers) directly impacting the formation of societies, including science. On the other hand, Messenger is the fourth biggest according to MAU, but it appears only 930 in WoS topics because it is just a person-to-person chat (communicator) and does not contain publicly available content (Cheung et al., 2011; Di Minin et al., 2015). The first social media platforms emerged in the early 21st century with the development of the internet, its speed, and accessibility, and after 2010, with the popularity of smartphones and tablets. Two of the first social media platforms were MySpace and Facebook, which were created in 2003 and 2004 (Brügger, 2015; Goodings, 2012). Currently, there are several SM platforms in use, and they are becoming more and more popular (Roseberry et al., 2014; Wang, 2020). In recent years, the influence of social media on various aspects of human life, including science, has been steadily increasing.
The growing interplay between social media platforms and scientific endeavours has reshaped how research is conducted, communicated, and perceived (Kapoor et al., 2018). Social media impacts the dissemination of research and findings. With different social media platforms, scientists can quickly share their latest research findings and publications, and among the most commonly used platforms are Twitter, LinkedIn, and ResearchGate. This accelerates the spread of knowledge beyond traditional academic journals. Furthermore, research shared on social media can reach a broader audience, including peers in different disciplines, practitioners, policymakers, and the general public, enhancing the visibility and impact of scientific work. Another advantage of social media is related to collaboration and networking. Social media facilitates connections between researchers worldwide, enabling collaborations that might not have been possible otherwise. Platforms like Twitter chats, specialised Facebook groups, and LinkedIn networks help scientists find collaborators with complementary expertise. These platforms encourage interactions across scientific fields, fostering interdisciplinary research and innovative solutions to complex problems.
Scientists use social media to engage directly with the public, making science more accessible and understandable. This helps bridge the gap between complex scientific concepts and public comprehension. Transparent communication through social media can build trust between the scientific community and the public, especially during crises like the COVID-19 pandemic, where accurate information dissemination is crucial (A. K. M. Chan et al., 2020; Saud et al., 2020; Wong et al., 2021).
Additionally, social media platforms can be used to mobilise large groups of people to contribute data for scientific research, as seen in projects like tracking disease outbreaks or environmental monitoring. Crowdsourcing ideas and solutions through social media can lead to innovative approaches and accelerate problem-solving in various scientific fields (Ghermandi & Sinclair, 2019). Besides researching with social media assistance, scientists use social media to stay updated on the latest research trends, conferences, and job opportunities, facilitating continuous professional development. Furthermore, platforms like Twitter, LinkedIn, and Facebook can provide avenues for mentorship, support, and community building among early-career researchers and established scientists (for example, groups with several thousand members such as the AACR Early-career Researcher Network on LinkedIn1 or Women in Academia Support Network Careers Support #wiasn on Facebook2).
Another use of social media in research is disseminating preprints and promoting work with open access. Researchers often share preprints of their studies on platforms like Twitter or dedicated preprint servers, allowing for immediate feedback and collaboration before formal publication. Social media provides a platform for promoting open-access journals, which are freely accessible to the public. Researchers and institutions can use social media to highlight new articles published in open-access journals, making it easier for others to find and use these resources.
Monitoring and analysing public sentiment are a crucial function of social media in the context of scientific communication. Scientists and institutions can utilise social media analytics to measure public opinion on a range of scientific topics, which can, in turn, inform policy decisions and communication strategies. By closely monitoring public discussions, scientists can better understand the concerns and interests of various audiences, enabling them to craft more effective messages. Additionally, monitoring social media platforms allows scientists to identify and address misinformation. In cases where misconceptions or false narratives about scientific issues are circulating (West & Bergstrom, 2021), scientists can proactively intervene by providing accurate information to the public, helping maintain the scientific community’s credibility.
Social media also plays a significant role in educational outreach (Deeken et al., 2020; Ngai et al., 2015; Ruangkanjanases et al., 2022; Szeto et al., 2021). It enables interactive learning opportunities, such as live Q&A sessions, webinars, and instructional videos, enhancing science education and outreach efforts. These platforms allow educators to engage directly with audiences in a dynamic and accessible way, breaking down complex scientific topics into more digestible formats. Furthermore, social media serves as a powerful tool for inspiring future scientists. Many videos on social media platforms (Instagram, YouTube, TikTok, and Doyuin) are used to highlight exciting research, scientific discoveries, and achievements, which can motivate younger generations to pursue careers in science.
However, using social media in scientific communication presents several challenges and considerations. One of the most significant issues is the spread of misinformation and fake news (Aïmeur et al., 2023; Bin Naeem et al., 2021; Olan et al., 2024). The rapid dissemination of information on social media can sometimes spread false narratives, undermining scientific credibility and eroding public trust. To combat this, scientists must be vigilant in addressing misinformation and providing accurate, evidence-based content. Another challenge involves data privacy and ethics (Barrett-Maitland & Lynch, 2020; Nicholas et al., 2020; Townsend & Wallace, 2017). The use of social media for research purposes raises essential concerns related to data privacy, consent, and ethical considerations, which must be carefully managed to protect individuals’ rights. Finally, there is the digital divide issue (Lythreatis et al., 2022; van Dijk, 2020). Not all communities have equal access to social media platforms, which can lead to disparities in who benefits from the scientific discourse facilitated by these platforms. Addressing this inequality is essential to ensure that scientific knowledge is accessible to a broad and diverse audience.

4. Conclusions

This paper aimed to explore the relationship between the popularity of social media platforms—measured by monthly active users (MAU)—and the extent of their coverage in academic research indexed in the Web of Science (WoS) database. While the study is primarily quantitative, it raises broader questions about how and why specific platforms attract more scholarly attention than others.
Key insights from the analysis include:
  • There is no straightforward correlation between platform popularity (MAU) and scholarly attention. Platforms with the largest user bases, such as WhatsApp, Instagram, or TikTok, are not necessarily the most studied in academic literature, suggesting a possible disconnect between societal influence and research focus.
  • Facebook and Twitter dominate scholarly references, likely due to their open nature, affordances for public communication, and extensive use by researchers for networking and dissemination. In contrast, more closed or informal platforms, such as messaging apps, tend to be underrepresented.
  • The nature and affordances of platforms matter. The qualitative characteristics of social media—such as openness, data accessibility, target user groups, and potential for scholarly use—play a significant role in determining their visibility in academic databases.
  • A moderate correlation was found between the time since a platform’s launch and its user base. The most established platforms (launched 10–15 years ago) tend to have the highest MAU; however, newer platforms like TikTok are growing rapidly.
  • Social media plays a diverse and growing role in science. Beyond being study subjects, platforms like Twitter, YouTube, LinkedIn, and Facebook are now tools for scientific communication, collaboration, education, outreach, and even crowdsourced research.
Overall, this study suggests the need for more critical reflection on which platforms we study, how we study them, and whether current academic focus aligns with the real-world influence of digital media in shaping public understanding and scientific discourse.

5. Additional Consideration and Future Research

One limitation of this study—and research on social media more broadly—is that specific platforms remain under-analysed due to barriers in data access, perceived irrelevance, or underdeveloped methodologies. For instance, platforms such as Messenger or WhatsApp, while widely used, are primarily private communication tools, limiting their visibility in research focused on public discourse. Others, like TikTok, have historically been dismissed as frivolous or youth-oriented. However, this perception no longer aligns with demographic trends. As of 2022, US TikTok users aged 30 and above made up over 50% of its user base (Statista, 2022). Neglecting such platforms risks omitting significant portions of the population and overlooking how everyday digital behaviours shape knowledge production and social engagement.
Future research should explore the societal roles of widely used social media platforms, particularly their influence on communication, information diffusion, and public sentiment formation. Comparative studies across platforms with varying user bases could illuminate differences in audience engagement, platform functionality, and sector-specific impacts. Furthermore, ethical dimensions—such as privacy, misinformation, and algorithmic bias—deserve deeper analysis, as these factors significantly affect science and public discourse trust. Methodologically, future work would benefit from mixed-methods approaches that combine quantitative techniques (e.g., sentiment or network analysis) with qualitative methods (e.g., content analysis, interviews, and user experience studies). This would enable a more nuanced understanding of social media’s functions within different cultural, political, and disciplinary contexts. Expanding the research focus to include underrepresented platforms and emerging practices will help scholars better capture social media’s complex and evolving role in shaping science and society.

Author Contributions

All authors contributed equally to this paper. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data derived from public domain resources.

Conflicts of Interest

The authors declare no conflict of interest.

Notes

1
https://www.linkedin.com/groups/8208935/ (accessed on 21 February 2025).
2
https://www.facebook.com/groups/905644729576673 (accessed on 21 February 2025).

References

  1. Aïmeur, E., Amri, S., & Brassard, G. (2023). Fake news, disinformation and misinformation in social media: A review. Social Network Analysis and Mining, 13(1), 1–36. [Google Scholar] [CrossRef]
  2. Arachchige, A. S. P. M. (2024). Controversial perspectives on the use of Twitter in radiology education. Canadian Association of Radiologists Journal, 75(2), 428. [Google Scholar] [CrossRef] [PubMed]
  3. Auxier, B., & Anderson, M. (2021). Social media use in 2021. Pew Research Center. [Google Scholar]
  4. Barrett-Maitland, N., & Lynch, J. (2020). Social media, ethics and the privacy paradox. In Security and privacy from a legal, ethical, and technical perspective. IntechOpen. [Google Scholar] [CrossRef]
  5. Barrot, J. S. (2021). Scientific mapping of social media in education: A decade of exponential growth. Journal of Educational Computing Research, 59(4), 645–668. [Google Scholar] [CrossRef]
  6. Bin Naeem, S., Bhatti, R., & Khan, A. (2021). An exploration of how fake news is taking over social media and putting public health at risk. Health Information and Libraries Journal, 38(2), 143–149. [Google Scholar] [CrossRef]
  7. Blank, G., & Lutz, C. (2017). Representativeness of social media in Great Britain: Investigating Facebook, LinkedIn, Twitter, Pinterest, Google+, and Instagram. American Behavioral Scientist, 61(7), 741–756. [Google Scholar] [CrossRef]
  8. Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1–8. [Google Scholar] [CrossRef]
  9. Bossetta, M. (2018). The digital architectures of social media: Comparing political campaigning on Facebook, Twitter, Instagram, and Snapchat in the 2016 U.S. election. Journalism & Mass Communication Quarterly, 95(2), 471–496. [Google Scholar] [CrossRef]
  10. Brügger, N. (2015). A brief history of Facebook as a media text: The development of an empty structure. First Monday, 20(5). [Google Scholar] [CrossRef]
  11. Caers, R., & Castelyns, V. (2011). Linkedin and Facebook in Belgium: The influences and biases of social network sites in recruitment and selection procedures. Social Science Computer Review, 29(4), 437–448. [Google Scholar] [CrossRef]
  12. Carr, C. T., & Hayes, R. A. (2015). Social media: Defining, developing, and divining. Atlantic Journal of Communication, 23(1), 46–65. [Google Scholar] [CrossRef]
  13. Chan, A. K. M., Nickson, C. P., Rudolph, J. W., Lee, A., & Joynt, G. M. (2020). Social media for rapid knowledge dissemination: Early experience from the COVID-19 pandemic. Anaesthesia, 75(12), 1579–1582. [Google Scholar] [CrossRef] [PubMed]
  14. Chan, M., Wu, X., Hao, Y., Xi, R., & Jin, T. (2012). Microblogging, online expression, and political efficacy among young Chinese citizens: The moderating role of information and entertainment needs in the use of Weibo. Cyberpsychology, Behavior, and Social Networking, 15(7), 345–349. [Google Scholar] [CrossRef]
  15. Chen, J., & Wang, Y. (2021). Social media use for health purposes: Systematic review. Journal of Medical Internet Research, 23(5), e17917. [Google Scholar] [CrossRef]
  16. Chen, X., Zhou, X., Li, H., Li, J., & Jiang, H. (2020). The value of WeChat application in chronic diseases management in China. Computer Methods and Programs in Biomedicine, 196, 105710. [Google Scholar] [CrossRef] [PubMed]
  17. Cheung, C. M., Chiu, P., & Lee, M. K. (2011). Online social networks: Why do students use facebook? Computers in Human Behavior, 27(4), 1337–1343. [Google Scholar] [CrossRef]
  18. Cingel, D. P., Carter, M. C., & Krause, H. (2022). Social media and self-esteem. Current Opinion in Psychology, 45, 101304. [Google Scholar] [CrossRef]
  19. Clarivate. (2022). Journal citation reports: Document types included in the impact factor calculation. Available online: https://support.clarivate.com/ScientificandAcademicResearch/s/article/Journal-Citation-Reports-Document-Types-Included-in-the-Impact-Factor-Calculation?language=en_US (accessed on 21 February 2025).
  20. Deeken, A. H., Mukhopadhyay, S., & Jiang, X. (2020). Social media in academics and research: 21st-century tools to turbocharge education, collaboration, and dissemination of research findings. Histopathology, 77(5), 688–699. [Google Scholar] [CrossRef]
  21. Di Minin, E., Tenkanen, H., & Toivonen, T. (2015). Prospects and challenges for social media data in conservation science. Frontiers in Environmental Science, 3, 63. [Google Scholar] [CrossRef]
  22. Eghtesadi, M., & Florea, A. (2020). Facebook, Instagram, Reddit and TikTok: A proposal for health authorities to integrate popular social media platforms in contingency planning amid a global pandemic outbreak. Canadian Journal of Public Health, 111(3), 389–391. [Google Scholar] [CrossRef]
  23. Ghermandi, A., & Sinclair, M. (2019). Passive crowdsourcing of social media in environmental research: A systematic map. Global Environmental Change, 55, 36–47. [Google Scholar] [CrossRef]
  24. Goodings, L. (2012). Understanding social network sites: Lessons from MySpace. Visual Communication, 11(4), 485–510. [Google Scholar] [CrossRef]
  25. Gudi, S. K., George, S. M., & Jose, J. (2022). Influence of social media on the public perspectives of the safety of COVID-19 vaccines. Expert Review of Vaccines, 21(12), 1697–1699. [Google Scholar] [CrossRef] [PubMed]
  26. Haimson, O. L., & Tang, J. C. (2017, May 6–11). What makes live events engaging on Facebook live, periscope, and snapchat. CHI’17: CHI Conference on Human Factors in Computing Systems (pp. 48–60), Denver, CO, USA. [Google Scholar] [CrossRef]
  27. Harwit, E. (2017). WeChat: Social and political development of China’s dominant messaging app. Chinese Journal of Communication, 10(3), 312–327. [Google Scholar] [CrossRef]
  28. Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of Social Media. Business Horizons, 53(1), 59–68. [Google Scholar] [CrossRef]
  29. Kapoor, K. K., Tamilmani, K., Rana, N. P., Patil, P., Dwivedi, Y. K., & Nerur, S. (2018). Advances in social media research: Past, present and future. Information Systems Frontiers, 20(3), 531–558. [Google Scholar] [CrossRef]
  30. Kaye, D. B. V., Chen, X., & Zeng, J. (2021). The co-evolution of two Chinese mobile short video apps: Parallel platformization of Douyin and TikTok. Mobile Media & Communication, 9(2), 229–253. [Google Scholar] [CrossRef]
  31. Kietzmann, J. H., Hermkens, K., McCarthy, I. P., & Silvestre, B. S. (2011). Social media? Get serious! Understanding the functional building blocks of social media. Business Horizons, 54(3), 241–251. [Google Scholar] [CrossRef]
  32. Kucharczuk, A. J., Oliver, T. L., & Dowdell, E. B. (2022). Social media’s influence on adolescents′ food choices: A mixed studies systematic literature review. Appetite, 168, 105765. [Google Scholar] [CrossRef]
  33. Li, H. O., Bailey, A., Huynh, D., & Chan, J. (2020). YouTube as a source of information on COVID-19: A pandemic of misinformation? BMJ Global Health, 5(5), e002604. [Google Scholar] [CrossRef]
  34. Lythreatis, S., Singh, S. K., & El-Kassar, A. (2022). The digital divide: A review and future research agenda. Technological Forecasting and Social Change, 175, 121359. [Google Scholar] [CrossRef]
  35. Mishnick, N., & Wise, D. (2024). Social media engagement: An analysis of the impact of social media campaigns on Facebook, Instagram, and LinkedIn. International Journal of Technology in Education, 7(3), 535–549. [Google Scholar] [CrossRef]
  36. Montag, C., Becker, B., & Gan, C. (2018). The multipurpose application WeChat: A review on recent research. Frontiers in Psychology, 9, 2247. [Google Scholar] [CrossRef] [PubMed]
  37. Ngai, E. W., Tao, S. S., & Moon, K. K. (2015). Social media research: Theories, constructs, and conceptual frameworks. International Journal of Information Management, 35(1), 33–44. [Google Scholar] [CrossRef]
  38. Nicholas, J., Onie, S., & Larsen, M. E. (2020). Ethics and privacy in social media research for mental health. Current Psychiatry Reports, 22(12), 1–7. [Google Scholar] [CrossRef]
  39. Olan, F., Jayawickrama, U., Arakpogun, E. O., Suklan, J., & Liu, S. (2024). Fake news on social media: The impact on society. Information Systems Frontiers, 26(2), 443–458. [Google Scholar] [CrossRef]
  40. Olanrewaju, A. T., Hossain, M. A., Whiteside, N., & Mercieca, P. (2020). Social media and entrepreneurship research: A literature review. International Journal of Information Management, 50, 90–110. [Google Scholar] [CrossRef]
  41. Paige, S. R., Stellefson, M., Chaney, B. H., & Alber, J. M. (2015). Pinterest as a resource for health information on chronic obstructive pulmonary disease (COPD): A social media content analysis. American Journal of Health Education, 46(4), 241–251. [Google Scholar] [CrossRef]
  42. Papacharissi, Z. (2009). The virtual geographies of social networks: A comparative analysis of Facebook, LinkedIn and ASmallWorld. New Media & Society, 11(1–2), 199–220. [Google Scholar] [CrossRef]
  43. Park, A., Conway, M., & Chen, A. T. (2018). Examining thematic similarity, difference, and membership in three online mental health communities from reddit: A text mining and visualization approach. Computers in Human Behavior, 78, 98–112. [Google Scholar] [CrossRef]
  44. Phua, J., Jin, S. V., & Kim, J. (2017). Gratifications of using Facebook, Twitter, Instagram, or Snapchat to follow brands: The moderating effect of social comparison, trust, tie strength, and network homophily on brand identification, brand engagement, brand commitment, and membership intention. Telematics and Informatics, 34(1), 412–424. [Google Scholar] [CrossRef]
  45. Pretorius, K. (2024). A simple and systematic approach to qualitative data extraction from social media for novice health care researchers: Tutorial. JMIR Formative Research, 8(1), e54407. [Google Scholar] [CrossRef]
  46. Puri, N., Coomes, E. A., Haghbayan, H., & Gunaratne, K. (2020). Social media and vaccine hesitancy: New updates for the era of COVID-19 and globalized infectious diseases. Human Vaccines & Immunotherapeutics, 16(11), 2586–2593. [Google Scholar] [CrossRef]
  47. Raturi, V., Delashaw, J., Dumont, A., & Wang, A. (2024). Most influential women neurosurgeons on Twitter. World Neurosurgery: X, 23, 100385. [Google Scholar] [CrossRef]
  48. Roseberry, S., Hirsh-Pasek, K., & Golinkoff, R. M. (2014). Skype me! Socially contingent interactions help toddlers learn language. Child Development, 85(3), 956–970. [Google Scholar] [CrossRef] [PubMed]
  49. Ruangkanjanases, A., Sivarak, O., Wibowo, A., & Chen, S. (2022). Creating behavioral engagement among higher education’s prospective students through social media marketing activities: The role of brand equity as mediator. Frontiers in Psychology, 13, 1004573. [Google Scholar] [CrossRef]
  50. Saud, M., Mashud, M., & Ida, R. (2020). Usage of social media during the pandemic: Seeking support and awareness about COVID-19 through social media platforms. Journal of Public Affairs, 20(4), e02417. [Google Scholar] [CrossRef]
  51. Shrivastava, S. R., Petkar, P. B., Bankar, N. J., & Bobhate, P. S. (2024). Utilizing the tool of Facebook to supplement the delivery of medical education. International Journal of Academic Medicine, 10(1), 4–9. [Google Scholar] [CrossRef]
  52. Statista. (2022). Distribution of TikTok users in the United States as of September 2021, by age group. Available online: https://www.statista.com/statistics/1166153/usa-tiktok-user-distribution-age-gender/ (accessed on 21 February 2025).
  53. Szeto, M. D., Mamo, A., Afrin, A., Militello, M., & Barber, C. (2021). Social media in dermatology and an overview of popular social media platforms. Current Dermatology Reports, 10(4), 97–104. [Google Scholar] [CrossRef]
  54. Tai, Z. (2022). Social media and contentious action: The use and users of QQ groups in China. Media and Communication, 10(4), 66–76. [Google Scholar] [CrossRef]
  55. Talbot, D. (2008). How Obama really did it social technology helped bring him to the brink of the presidency. Technology Review, 9. Available online: https://www.technologyreview.com/2008/08/19/219185/how-obama-really-did-it-2/ (accessed on 21 February 2025).
  56. Tähtinen, T. (2024). When Facebook is the internet: The role of social media in ethnic conflict. World Development, 180, 106633. [Google Scholar] [CrossRef]
  57. Townsend, L., & Wallace, C. (2017). Chapter 8: The ethics of using social media data in research: A new framework. In The ethics of online research (Advances in research ethics and integrity) (pp. 189–207). Emerald Publishing Limited. [Google Scholar] [CrossRef]
  58. Tu, F. (2016). WeChat and civil society in China. Communication and the Public, 1(3), 343–350. [Google Scholar] [CrossRef]
  59. van Dijk, J. (2020). The digital divide (1st ed., 208p). Policy. ISBN 978-1509534456. [Google Scholar]
  60. Vaterlaus, J. M., Barnett, K., Roche, C., & Young, J. A. (2016). “Snapchat is more personal”: An exploratory study on Snapchat behaviors and young adult interpersonal relationships. Computers in Human Behavior, 62, 594–601. [Google Scholar] [CrossRef]
  61. Wang, Y. (2020). Humor and camera view on mobile short-form video apps influence user experience and technology-adoption intent, an example of TikTok (DouYin). Computers in Human Behavior, 110, 106373. [Google Scholar] [CrossRef]
  62. Weimann, G., & Masri, N. (2020). Research note: Spreading hate on TikTok. Studies in Conflict & Terrorism, 46(5), 752–765. [Google Scholar] [CrossRef]
  63. Wengel, Y., Ma, L., Ma, Y., Apollo, M., Maciuk, K., & Ashton, A. S. (2022). The TikTok effect on destination development: Famous overnight, now what? Journal of Outdoor Recreation and Tourism, 37, 100458. [Google Scholar] [CrossRef]
  64. West, J. D., & Bergstrom, C. T. (2021). Misinformation in and about science. Proceedings of the National Academy of Sciences, 118(15), e1912444117. [Google Scholar] [CrossRef]
  65. Wikipedia. (2022). List of social platforms with at least 100 million active users—Wikipedia. Available online: https://en.wikipedia.org/wiki/List_of_social_platforms_with_at_least_100_million_active_users (accessed on 21 February 2025).
  66. Wong, A., Ho, S., Olusanya, O., Antonini, M. V., & Lyness, D. (2021). The use of social media and online communications in times of pandemic COVID-19. Journal of the Intensive Care Society, 22(3), 255–260. [Google Scholar] [CrossRef]
  67. Xue, J., Zhang, Q., Zhang, Y., Shi, H., Zheng, C., Fan, J., Chen, C., Li, L., & Shier, M. (2024). Bridging and bonding social capital: Analyzing the demographics, user activities, social networks dynamics of sexual assault senters on X: Mixed methods study. Journal of Medical Internet Research, 26, e50552. [Google Scholar] [CrossRef]
  68. Young, N. L., Kuss, D. J., Griffiths, M. D., & Howard, C. J. (2017). Passive Facebook use, Facebook addiction, and associations with escapism: An experimental vignette study. Computers in Human Behavior, 71, 24–31. [Google Scholar] [CrossRef]
  69. Zhang, C., Li, Y., Wu, B., & Li, D. (2017). How WeChat can retain users: Roles of network externalities, social interaction ties, and perceived values in building continuance intention. Computers in Human Behavior, 69, 284–293. [Google Scholar] [CrossRef]
  70. Zhuravskaya, E., Petrova, M., & Enikolopov, R. (2020). Political effects of the internet and social media. Annual Review of Economics, 12(1), 415–438. [Google Scholar] [CrossRef]
Journalmedia 06 00062 g001
Figure 1. Treemaps of the 13 SM platforms with at least 500 mln MAU users ranked by the number of MAU participants (a) and the number of total appearances in articles indexed in WoS of 5 top SM platforms and cumulative sum of other SM (b).
Figure 1. Treemaps of the 13 SM platforms with at least 500 mln MAU users ranked by the number of MAU participants (a) and the number of total appearances in articles indexed in WoS of 5 top SM platforms and cumulative sum of other SM (b).
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Figure 2. SM platforms with at least 500 mln MAU and their launch years.
Figure 2. SM platforms with at least 500 mln MAU and their launch years.
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Figure 3. Top 15 SM platforms with the most significant impact on WoS database with their MAU, sorted by number of WoS appearances.
Figure 3. Top 15 SM platforms with the most significant impact on WoS database with their MAU, sorted by number of WoS appearances.
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Figure 4. Top 15 SM platforms with the most significant impact on WoS database with their MAU, sorted by number of WoS appearances per used (ratio).
Figure 4. Top 15 SM platforms with the most significant impact on WoS database with their MAU, sorted by number of WoS appearances per used (ratio).
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Table 1. List of social platforms with at least 100 million active users sorted by MAU with appearances in WoS field “topic *” (Wikipedia, 2022).
Table 1. List of social platforms with at least 100 million active users sorted by MAU with appearances in WoS field “topic *” (Wikipedia, 2022).
NoSocial MediaCompanyYearMAU WoS
No of MAUMarket Share
[%]		Number
of Papers		Share in Number of Papers
[%]
1FacebookMeta20042,910,000,000 1522,897 36.09
2YouTubeAlphabet20052,291,000,000 126951 10.96
3WhatsAppMeta20092,000,000,000 102262 3.57
4MessengerMeta20111,300,000,000 7335 0.53
5WeChatTencent20111,225,000,000 61374 2.17
6InstagramMeta20101,200,000,000 63748 5.91
7TikTokBytedance2017732,000,000 4189 0.30
8TelegramTelegram2013700,000,000 4139 0.22
9DouyinBytedance2016600,000,000 329 0.05
10QQTencent1999595,000,000 343 0.07
11SnapchatSnap2011528,000,000 3405 0.64
12WeiboSina2009521,000,000 31392 2.19
13QzoneTencent2005517,000,000 36 0.01
14KuaishouKuaishou2011481,000,000 28 0.01
15PinterestPinterest2009459,000,000 2287 0.45
16RedditReddit2005430,000,000 2671 1.06
17TwitterTwitter2006396,000,000 219,334 30.48
18LinkedInMicrosoft2003310,000,000 2840 1.32
19SkypeMicrosoft2003300,000,000 21182 1.86
20QuoraQuora2009300,000,000 261 0.10
21TiebaBaidu2003300,000,000 256 0.09
22ViberRakuten2010260,000,000 173 0.12
23TeamsMicrosoft2016250,000,000 1110 0.17
24imoPageBites2007200,000,000 114 0.02
25LineNaver2011178,000,000 160.01
26LikeeBigo Live2017150,000,000 115 0.02
27PicsartPicsart2011150,000,000 120.00
28TwitchAmazon2011140,000,000 1910.14
29DiscordDiscord2015140,000,000 1780.12
30Stack OverflowStack Exchange2008100,000,000 1844 1.33
* Searches title, abstract, author keywords, and Keywords Plus.
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Maciuk, K.; Apollo, M.; Skorupa, J.; Jakubiak, M.; Wengel, Y.; Geary, D.C. Facebook Is “For Old People”—So Why Are We Still Studying It the Most? A Critical Look at Social Media in Science. Journal. Media 2025, 6, 62. https://doi.org/10.3390/journalmedia6020062

AMA Style

Maciuk K, Apollo M, Skorupa J, Jakubiak M, Wengel Y, Geary DC. Facebook Is “For Old People”—So Why Are We Still Studying It the Most? A Critical Look at Social Media in Science. Journalism and Media. 2025; 6(2):62. https://doi.org/10.3390/journalmedia6020062

Chicago/Turabian Style

Maciuk, Kamil, Michal Apollo, Julia Skorupa, Mateusz Jakubiak, Yana Wengel, and David C. Geary. 2025. "Facebook Is “For Old People”—So Why Are We Still Studying It the Most? A Critical Look at Social Media in Science" Journalism and Media 6, no. 2: 62. https://doi.org/10.3390/journalmedia6020062

APA Style

Maciuk, K., Apollo, M., Skorupa, J., Jakubiak, M., Wengel, Y., & Geary, D. C. (2025). Facebook Is “For Old People”—So Why Are We Still Studying It the Most? A Critical Look at Social Media in Science. Journalism and Media, 6(2), 62. https://doi.org/10.3390/journalmedia6020062

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