A bibliometric analysis of the impact of media manipulation on adolescent mental health: Policy recommendations for algorithmic transparency

Alfonso Pellegrino 1, Alessandro Stasi 2 *
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1 SASIN Graduate Institute of Business Administration, Chulalongkorn University, Bangkok, THAILAND
2 Business Administration Division, Mahidol University International College, Salaya, THAILAND
* Corresponding Author
Online Journal of Communication and Media Technologies, Volume 14, Issue 4, Article No: e202453. https://doi.org/10.30935/ojcmt/15143
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ABSTRACT

This bibliometric study examines the relationship between media manipulation and adolescent mental health, analyzing 101 articles published from 2016 to 2024. The research reveals a significant increase in attention post-2016, with the United States, Spain, Australia, and Italy leading contributions. Using PRISMA guidelines and VOSviewer for keyword co-occurrence and co-citation mapping, three main research clusters are identified: cognitive dynamics of misinformation, digital literacy, and the social implications of misinformation. The study emphasizes the need for multidisciplinary efforts to enhance digital literacy and develop informed policy interventions. Findings advocate for proactive strategies to mitigate the negative effects of digital misinformation on youth, including policy reforms for effective content moderation and greater transparency in algorithmic processes. Additionally, the study highlights the importance of context-aware AI systems and better access to mental health services to address the psychological impacts of media manipulation on adolescents. These efforts are essential for fostering a sustainable digital environment that supports the mental well-being of young people.
Keywords: media manipulation, adolescent mental health, bibliometric analysis, sustainable digital environment, public policy

CITATION

Pellegrino, A., & Stasi, A. (2024). A bibliometric analysis of the impact of media manipulation on adolescent mental health: Policy recommendations for algorithmic transparency. Online Journal of Communication and Media Technologies, 14(4), e202453. https://doi.org/10.30935/ojcmt/15143

REFERENCES

  • Aida, M., Sakiyama, T., Hashizume, A., & Takano, C. (2023). Cluster structure of online users generated from interaction between fake news and corrections. IEICE Transactions on Communications, 106(5), 392–401. https://doi.org/10.1587/transcom.2022EBP3059
  • American Psychological Association. (2023). Protecting teens on social media. https://www.apa.org/monitor/2023/09/protecting-teens-on-social-media
  • Armijo, E. (2021). Reasonableness as censorship: Section 230 reform, content moderation, and the first amendment. Florida Law Review, 73, Article 1199.
  • Ballestar, M. T., Cuerdo-Mir, M., & Freire-Rubio, M. T. (2020). The concept of sustainability on social media: A social listening approach. Sustainability, 12(5), Article 2122. https://doi.org/10.3390/su12052122
  • Bardram, J. E., & Hansen, T. R. (2010). Why the plan doesn’t hold: A study of situated planning, articulation and coordination work in a surgical ward. In Proceedings of the 2010 ACM Conference on Computer Supported Cooperative Work, 331–340. https://doi.org/10.1145/1718918.1718977
  • Bera, D., Ogbanufe, O., & Kim, D. J. (2023). Towards a thematic dimensional framework of online fraud: An exploration of fraudulent email attack tactics and intentions. Decision Support Systems, 171, Article 113977. https://doi.org/10.1016/j.dss.2023.113977
  • Binns, R., Van Kleek, M., Veale, M., Lyngs, U., Zhao, J., & Shadbolt, N. (2018). ‘It’s reducing a human being to a percentage’ perceptions of justice in algorithmic decisions. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, Article 377. https://doi.org/10.1145/3173574.3173951
  • Breakstone, J., McGrew, S., & Smith, M. (2024). Measuring what matters: Investigating what new types of assessments reveal about students’ online source evaluations. Harvard Kennedy School Misinformation Review. https://doi.org/10.37016/mr-2020-133
  • Bush, V. D., & Gilbert, F. W. (2002). The web as a medium: An exploratory comparison of internet users versus newspaper readers. Journal of Marketing Theory and Practice, 10(1), 1–10. https://doi.org/10.1080/10696679.2002.11501905
  • Caled, D., & Silva, M. J. (2022). Digital media and misinformation: An outlook on multidisciplinary strategies against manipulation. Journal of Computational Social Science, 5(1), 123–159. https://doi.org/10.1007/s42001-021-00118-8
  • Capraro, V., & Celadin, T. (2023). “I think this news is accurate”: Endorsing accuracy decreases the sharing of fake news and increases the sharing of real news. Personality and Social Psychology Bulletin, 49(12), 1635–1645. https://doi.org/10.1177/01461672221117691
  • Chen, E., Lerman, K., & Ferrara, E. (2020). Tracking social media discourse about the COVID-19 pandemic: Development of a public coronavirus Twitter data set. JMIR Public Health and Surveillance, 6(2), Article e19273. https://doi.org/10.2196/19273
  • Chen, H., & Cai, W. (2023). How information manipulation on social media influences the NFT investors’ behavior: A case study of Goblintown.wtf. IEEE Transactions on Computational Social Systems. https://doi.org/10.1109/TCSS.2023.3234183
  • Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv. https://doi.org/10.48550/arXiv.1810.04805
  • Dhar, B. K., Ayittey, F. K., & Sarkar, S. M. (2020). Impact of COVID‐19 on psychology among the university Students. Global Challenges, 4(11), Article 2000038. https://doi.org/10.1002/gch2.202000038
  • Durieux, V., & Gevenois, P. A. (2010). Bibliometric indicators: Quality measurements of scientific publication. Radiology, 255(2), 342–351. https://doi.org/10.1148/radiol.09090626
  • Ecker, U. K., Lewandowsky, S., & Chadwick, M. (2020). Can corrections spread misinformation to new audiences? Testing for the elusive familiarity backfire effect. Cognitive Research: Principles and Implications, 5, 1–25. https://doi.org/10.31219/osf.io/et4p3
  • Fang, X., Wu, H., Jing, J., Meng, Y., Yu, B., Yu, H., & Zhang, H. (2024). NSEP: Early fake news detection via news semantic environment perception. Information Processing & Management, 61(2), Article 103594. https://doi.org/10.1016/j.ipm.2023.103594
  • Fast Company. (2021). On social media, child sexual abuse material spreads faster than it can be taken down. https://www.fastcompany.com/90654692/on-social-media-child-sexual-abuse-material-spreads-faster-than-it-can-be-taken-down
  • Ghai, S., Fassi, L., Awadh, F., & Orben, A. (2023). Lack of sample diversity in research on adolescent depression and social media use: A scoping review and meta-analysis. Clinical Psychological Science, 11(5), 759–772. https://doi.org/10.1177/21677026221114859
  • Giordano, A. L., Prosek, E. A., & Watson, J. C. (2021). Understanding adolescent cyberbullies: Exploring social media addiction and psychological factors. Journal of Child and Adolescent Counseling, 7(1), 42–55. https://doi.org/10.1080/23727810.2020.1835420
  • Gopalkrishnan, N. (2018). Cultural diversity and mental health: Considerations for policy and practice. Frontiers in Public Health, 6, Article 308538. https://doi.org/10.3389/fpubh.2018.00179
  • Gorwa, R., Binns, R., & Katzenbach, C. (2020). Algorithmic content moderation: Technical and political challenges in the automation of platform governance. Big Data & Society, 7(1), Article 2053951719897945.
  • Hassen, H., Behera, M., Jena, P., & Satpathy, S. (2020). A quasi-experimental and guided social media intervention to improve mental health literacy level of urban school adolescents in Ethiopia: A detailed study protocol. Research Square. https://doi.org/10.21203/rs.3.rs-17074/v1
  • Heizomi, H., Allahverdipour, H., Jafarabadi, M., Bhalla, D., & Nadrian, H. (2020). Effects of a mental health promotion intervention on mental health of Iranian female adolescents: A school-based study. Child and Adolescent Psychiatry and Mental Health, 14(1). https://doi.org/10.1186/s13034-020-00342-6
  • Hinck, R. S., Cooley, S., & Kluver, R. (2019). Global media and strategic narratives of contested democracy. Routledge. https://doi.org/10.4324/9780429289804
  • Holmes, E. A., O’Connor, R. C., Perry, V. H., Tracey, I., Wessely, S., Arseneault, L., Ballard, C., Christensen, H., Silver, R. C., Everall, I., Ford, T., John, A., Kabir, T., King, K., Madan, I., Michie, S., Przybylski, A. K., Shafran, R., Sweeney, A., ..., & Bullmore, E. (2020). Multidisciplinary research priorities for the COVID-19 pandemic: A call for action for mental health science. The Lancet Psychiatry, 7(6), 547–560. https://doi.org/10.1016/S2215-0366(20)30168-1
  • Horne, B., & Adali, S. (2017). The impact of crowds on news engagement: A Reddit case study. Proceedings of the International AAAI Conference on Web and Social Media, 11(1), 751–758. https://doi.org/10.1609/icwsm.v11i1.14977
  • Hosseini, H., Kannan, S., Zhang, B., & Poovendran, R. (2017). Deceiving Google’s Perspective API built for detecting toxic comments. arXiv. https://arxiv.org/abs/1702.08138
  • Jabbar, J., Dharmarajan, S., Raveendranathan, R., Syamkumar, D., & Jasseer, A. (2022). Influence of social media on adolescent mental health. International Journal of English Literature and Social Sciences, 7(1), 072–076. https://doi.org/10.22161/ijels.71.13
  • Jardina, A., & Traugott, M. (2019). The genesis of the birther rumor: Partisanship, racial attitudes, and political knowledge. Journal of Race, Ethnicity, and Politics, 4(1), 60–80. https://doi.org/10.1017/rep.2018.25
  • Laestadius, L., Craig, K., & Campos-Castillo, C. (2021). Perceptions of alerts issued by social media platforms in response to self-injury posts among Latinx adolescents: Qualitative analysis. Journal of Medical Internet Research, 23(8), Article e28931. https://doi.org/10.2196/28931
  • Lewandowsky, S., Armaos, K., Bruns, H., Schmid, P., Holford, D. L., Hahn, U., Al-Rawi, A., Sah, S., & Cook, J. (2022). When science becomes embroiled in conflict: Recognizing the public’s need for debate while combating conspiracies and misinformation. The ANNALS of the American Academy of Political and Social Science, 700(1), 26–40. https://doi.org/10.1177/00027162221084663
  • Li, Q., Wei, W., Xiong, N., Feng, D., Ye, X., & Jiang, Y. (2017). Social media research, human behavior, and sustainable society. Sustainability, 9(3), Article 384. https://doi.org/10.3390/su9030384
  • Marwick, A. E., & Lewis, R. (2017). Media manipulation and disinformation online. https://datasociety.net/library/media-manipulation-and-disinfo-online/
  • McGrew, S., Ortega, T., Breakstone, J., & Wineburg, S. (2017). The challenge that’s bigger than fake news: Civic reasoning in a social media environment. American Educator, 41(3), Article 4.
  • Merigó, J. M., Mas-Tur, A., Roig-Tierno, N., & Ribeiro-Soriano, D. (2015). A bibliometric overview of the Journal of Business Research between 1973 and 2014. Journal of Business Research, 68(12), 2645–2653. https://doi.org/10.1016/j.jbusres.2015.04.006
  • Mkhongi, F. A., & Musakwa, W. (2022). Trajectories of deagrarianization in South Africa−Past, current and emerging trends: A bibliometric analysis and systematic review. Geography and Sustainability, 3(4), 325–333. https://doi.org/10.1016/j.geosus.2022.10.003
  • NBC News. (2023). FBI warning teens about ‘sextortion’ as incidents surge. https://www.nbcnews.com/think/opinion/fbi-warning-teens-sextortion-means-parents-need-take-steps-rcna62795
  • NCBI. (2022). Impact of social media on health and behavior. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9407706/
  • Neylan, J., Biddlestone, M., Roozenbeek, J., & van der Linden, S. (2023). How to “inoculate” against multimodal misinformation: A conceptual replication of Roozenbeek and van der Linden (2020). Scientific Reports, 13(1), Article 18273. https://doi.org/10.1038/s41598-023-43885-2
  • Nobre, T. L., Abrantes, L. P., & Silva, C. C. (2019). The impact of digital influencers on adolescent identity building. IROCAMM-International Review of Communication and Marketing Mix, 2(2), 32–40. https://doi.org/10.12795/IROCAMM.2019.v02.i02.04
  • Nygren, T., Guath, M., Axelsson, C. A. W., & Frau-Meigs, D. (2021). Combatting visual fake news with a professional fact-checking tool in education in France, Romania, Spain and Sweden. Information, 12(5), Article 201. https://doi.org/10.3390/info12050201
  • Odgers, C. L., & Jensen, M. R. (2020). Annual research review: Adolescent mental health in the digital age: Facts, fears, and future directions. Journal of Child Psychology and Psychiatry, 61(3), 336–348. https://doi.org/10.1111/jcpp.13190
  • Papapicco, C., Lamanna, I., & D’Errico, F. (2022). Adolescents’ vulnerability to fake news and to racial hoaxes: A qualitative analysis on Italian sample. Multimodal Technologies and Interaction, 6(3), Article 20. https://doi.org/10.3390/mti6030020
  • Pennycook, G., & Rand, D. G. (2020). Who falls for fake news? The roles of bullshit receptivity, overclaiming, familiarity, and analytic thinking. Journal of Personality, 88(2), 185–200. https://doi.org/10.1111/jopy.12476
  • Pennycook, G., Epstein, Z., Mosleh, M., Arechar, A. A., Eckles, D., & Rand, D. G. (2021). Shifting attention to accuracy can reduce misinformation online. Nature, 592(7855), 590–595. https://doi.org/10.1038/s41586-021-03344-2
  • Porter, E., & Wood, T. J. (2019). False alarm: The truth about political mistruths in the Trump era. Cambridge University Press. https://doi.org/10.1017/9781108688338
  • Qi, X. (2024). The effect of social media upward comparison on Chinese adolescent learning engagement: A moderated multiple mediation model. BMC Psychology, 12(1). https://doi.org/10.1186/s40359-024-01621-z
  • Reisach, U. (2021). The responsibility of social media in times of societal and political manipulation. European Journal of Operational Research, 291(3), 906–917. https://doi.org/10.1016/j.ejor.2020.09.020
  • Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why should I trust you?” Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM. https://doi.org/10.18653/v1/N16-3020
  • Sandvig, C., Hamilton, K., Karahalios, K., & Langbort, C. (2014). Auditing algorithms: Research methods for detecting discrimination on internet platforms. Data and Discrimination: Converting Critical Concerns into Productive Inquiry, 22(2014), 4349–4357.
  • Sherwin, E., Barendse, M. E. A., Dahl, R., & Magis-Weinberg, L. (2022). Prospective, directional associations between social media intensity, loneliness, and anxiety among Peruvian adolescents during the COVID-19 pandemic. PsyArXiv. https://doi.org/10.31234/osf.io/8bvjc
  • Shu, K., Wang, S., Lee, D., & Liu, H. (2020). Mining disinformation and fake news: Concepts, methods, and recent advancements. In Disinformation, misinformation, and fake news in social media: Emerging research challenges and opportunities (pp. 1–19). https://doi.org/10.1007/978-3-030-42699-6
  • Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., & Hassabis, D. (2018). A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. Science, 362(6419), 1140–1144. https://doi.org/10.1126/science.aar6404
  • Small, H. (1999). Visualizing science by citation mapping. Journal of the American society for Information Science, 50(9), 799–813. https://doi.org/10.1002/(SICI)1097-4571(1999)50:9<799::AID-ASI9>3.0.CO;2-G
  • Statista.com. (2022a). Share of global mobile website traffic 2015-2021. https://www.statista.com/statistics/277125/share-of-website-traffic-coming-from-mobile-devices/
  • Statista.com. (2022b). Global social media traffic 2015-2021. https://www.statista.com/statistics/277115/social-media-traffic/
  • The Guardian. (2022). Self-generated sexual abuse of children aged seven to 10 rises by two-thirds. https://www.theguardian.com/technology/2022/aug/09/self-generated-sexual-abuse-of-children-aged-seven-to-10-rises-two-thirds
  • Traberg, C. S., Harjani, T., Roozenbeek, J., & van der Linden, S. (2024). The persuasive effects of social cues and source effects on misinformation susceptibility. Scientific Reports, 14(1), Article 4205. https://doi.org/10.1038/s41598-024-54030-y
  • U.S. Department of Health & Human Services. (2023a). Surgeon general issues new advisory about effects social media use has youth mental health. https://www.hhs.gov/sites/default/files/sg-youth-mental-health-social-media-advisory.pdf
  • U.S. Department of Health & Human Services. (2023b). HHS news release. https://www.hhs.gov/about/news/2023/05/23/surgeon-general-issues-new-advisory-about-effects-social-media-use-has-youth-mental-health.html
  • van der Linden, S. (2023). Foolproof: Why misinformation infects our minds and how to build immunity. WW Norton & Company.
  • Van Eck, N. J., & Waltman, L. (2007). Bibliometric mapping of the computational intelligence field. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 15(05), 625–645. https://doi.org/10.1142/S0218488507004911
  • Venkataramani, A., Cook, E., O’Brien, R., Kawachi, I., Jena, A., & Tsai, A. (2019). College affirmative action bans and smoking and alcohol use among underrepresented minority adolescents in the United States: A difference-in-differences study. PLoS Medicine, 16(6), Article e1002821. https://doi.org/10.1371/journal.pmed.1002821
  • Vlasceanu, M., Dyckovsky, A. M., & Coman, A. (2024). A network approach to investigate the dynamics of individual and collective beliefs: Advances and applications of the bending model. Perspectives on Psychological Science, 19(2), 444–453. https://doi.org/10.1177/17456916231185776
  • Vo, T. H., Phan, T. L. T., & Ninh, K. C. (2022). Development of a fake news detection tool for Vietnamese based on deep learning techniques. Eastern-European Journal of Enterprise Technologies 5(2(119)), 14–20. https://doi.org/10.15587/1729-4061.2022.265317
  • Wall Street Journal. (2021). Facebook knows Instagram is toxic for teen girls, company documents show. https://www.wsj.com/articles/facebook-knows-instagram-is-toxic-for-teen-girls-company-documents-show-11631620739
  • Wall Street Journal. (2023). States sue meta, alleging harm to young people on Instagram, Facebook. https://www.wsj.com/tech/states-sue-meta-alleging-harm-to-young-people-on-instagram-facebook-f9ff4641
  • Wineburg, S., & McGrew, S. (2017). Lateral reading: Reading less and learning more when evaluating digital information. SSRN. https://doi.org/10.2139/ssrn.3048994
  • Wineburg, S., Breakstone, J., McGrew, S., Smith, M. D., & Ortega, T. (2022). Lateral reading on the open Internet: A district-wide field study in high school government classes. Journal of Educational Psychology, 114(5), Article 893. https://doi.org/10.1037/edu0000740
  • Xing, Y., Zhang, J. Z., Storey, V. C., & Koohang, A. (2024). Diving into the divide: A systematic review of cognitive bias-based polarization on social media. Journal of Enterprise Information Management, 37(1), 259–287. https://doi.org/10.1108/JEIM-09-2023-0459
  • Zafarani, R., Liu, H., Phoha, V. V., & Azimi, J. (2021). Introduction on recent trends and perspectives in fake news research. Digital Threats: Research and Practice, 2(2), Article 13. https://doi.org/10.1145/3448634
  • Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429–472. https://doi.org/10.1177/1094428114562629