Despite the salience of misinformation and its consequences, there still lies a tremendous gap in research on the broader tendencies in collective cognition that compels individuals to spread misinformation so excessively. This study examined social learning as an antecedent of engaging with misinformation online. Using data released by Twitter for academic research in 2018, Tweets that included URL news links of both known misinformation and reliable domains were analyzed. Lindström’s computational reinforcement learning model was adapted as an expression of social learning, where a Twitter user’s posting frequency of news links is dependent on the relative engagement they receive in consequence. The research found that those who shared misinformation were highly sensitive to social reward. Inflation of positive social feedback was associated with a decrease in posting latency, indicating that users that posted misinformation were strongly influenced by social learning. However, the posting frequency of authentic news sharers remained fixed, even after receiving an increase in relative and absolute engagement. The results identified social learning is a contributor to the spread of misinformation online. In addition, behavior driven by social validation suggests a positive correlation between posting frequency, gratification received from posting, and a growing mental health dependency on social media. Developing interventions for spreading misinformation online may profit by assessing which online environments amplify social learning, particularly the conditions under which misinformation proliferates.
KeywordsSocial Reinforcement LearningMisinformation OnlineComputational Models of Cognition
Watts, D.J., Rothschild, D.M. and Mobius, M. (2021) Measuring the News and Its Impact on Democracy. Proceedings of the National Academy of Sciences of the United States of America, 118, e1912443118. https://doi.org/10.1073/pnas.1912443118
Global Social Media Statistic (2022) Data Report. https://datareportal.com/social-media-users
Lazer, D.M.J., Baum, M.A., Benkler, Y., Berinsky, A.J., Greenhill, K.M., Menczer, F., Metzger, M.J., Nyhan, B., Pennycook, G., Rothschild, D., Schudson, M., Sloman, S.A., Sunstein, C.R., Thorson, E.A., Watts, D.J. and Zittrain, J.L. (2018) The Science of Fake News. Science, 359, 1094-1096. https://doi.org/10.1126/science.aao2998
Olsson, A., Knapska, E. and Lindström, B. (2020) The Neural and Computational Systems of Social Learning. Nature Reviews Neuroscience, 21, 197-212. https://doi.org/10.1038/s41583-020-0276-4
Sutton, R.S. and Barto, A.G. (1998) Reinforcement Learning: An Introduction. IEEE Transactions on Neural Networks, 9, 1054-1054. https://doi.org/10.1109/TNN.1998.712192
Vélez, N. and Gweon, H. (2021) Learning from Other Minds: An Optimistic Critique of Reinforcement Learning Models of Social Learning. Current Opinion in Behavioral Sciences, 38, 110-115. https://doi.org/10.1016/j.cobeha.2021.01.006 https://www.sciencedirect.com/science/article/pii/S2352154621000073
Ho, M.K., MacGlashan, J., Littman, M.L. and Cushman, F. (2017) Social Is Special: A Normative Framework for Teaching with and Learning from Evaluative Feedback. Cognition, 167, 91-106. https://doi.org/10.1016/j.cognition.2017.03.006
Lindström, B., Bellander, M., Schultner, D.T., Chang, A., Tobler, P.N. and Amodio, D.M. (2021) A Computational Reward Learning Account of Social Media Engagement. Nature Communications, 12, Article No. 1311. https://doi.org/10.1038/s41467-020-19607-x
Hsiang, S., Allen, D., Annan-Phan, S., Bell, K., Bolliger, I., Chong, T., Druckenmiller, H., Huang, L.Y., Hultgren, A., Krasovich, E., Lau, P., Lee, J., Rolf, E., Tseng, J. and Wu, T. (2020) The Effect of Large-Scale Anti-Contagion Policies on the COVID-19 Pandemic. Nature, 584, 262-267. https://doi.org/10.1038/s41586-020-2404-8
Grinberg, N., Joseph, K., Friedland, L., Swire-thompson, B. and Lazer, D. (2019) Fake News on Twitter during the 2016 U.S. Presidential Election. Science, 363, 374-378. https://doi.org/10.1126/science.aau2706
Guess, A., Nagler, J. and Tucker, J. (2019) Less than you Think: Prevalence and Predictors of Fake News Dissemination on Facebook. Science Advances, 5, eaau4586. https://doi.org/10.1126/sciadv.aau4586
Wardle, C. and Derakhshan, H. (2017) Information Disorder: Toward an Interdisciplinary Framework for Research and Policy-Making. Council of Europe Report. http://tverezo.info/wp-content/uploads/2017/11/PREMS-162317-GBR-2018-Report-desinformation-A4-BAT.pdf
Osmundsen, M., Bor, A., Vahlstrup, P.B., Bechmann, A. and Petersen, M.B. (2021) Partisan Polarization Is the Primary Psychological Motivation behind Political Fake News Sharing on Twitter. American Political Science Review, 115, 999-1015. https://doi.org/10.1017/S0003055421000290
Dapcevich, M. (2022) Snopestionary: What Is an Echo Chamber? https://www.snopes.com/articles/428074/what-is-an-echo-chamber/
Chen, E., Chang, H., Rao, A., Lerman, K., Cowan, G. and Ferrara, E. (2021) COVID-19 Misinformation and the 2020 U.S. Presidential Election. The Harvard Kennedy School Misinformation Review, 1, 1-17. https://doi.org/10.37016/mr-2020-57
Sathyanarayana, R.T.S. and Andrade, C. (2011) The MMR Vaccine and Autism: Sensation, Refutation, Retraction, and Fraud. Indian Journal of Psychiatry, 53, 95-96. https://doi.org/10.4103/0019-5545.82529
The Official Website of the City of New York (2019) De Blasio Administration’s Health Department Declares Public Health Emergency due to Measles Crisis. https://www1.nyc.gov/office-of-the-mayor/news/186-19/de-blasio-administration-s-health-department-declares-public-health-emergency-due-measles-crisis#/0
U.S. Public Health Service Surgeon General of the United States (2021) Confronting Health Misinformation. The U.S. Surgeon General’s Advisory on Building a Healthy Information Environment, 1-22. https://www.hhs.gov/sites/default/files/surgeon-general-misinformation-advisory.pdf
Jabbour, D., Masri, J.E., Nawfal, R., Malaeb, D. and Salameh, P. (2022) Social Media Medical Misinformation: Impact on Mental Health and Vaccination Decision among University Students. Irish Journal of Medical Science. https://doi.org/10.1007/s11845-022-02936-9
Pennycook, G., Epstein, Z., Mosleh, M., Arechar, A.A., Eckles, D. and Rand, D.G. (2021) Shifting Attention to Accuracy Can Reduce Misinformation Online. Nature, 592, 590-595. https://doi.org/10.1038/s41586-021-03344-2
Graves, L. and Mantzarlis, A. (2020) Amid Political Spin and Online Misinformation, Fact-Checking Adapts. The Political Quarterly, 91, 585-591. https://doi.org/10.1111/1467-923X.12896
Brady, W.J., Mcloughlin, K., Doan, T.N. and Crockett, M.J. (2021) How Social Learning Amplifies Moral Outrage Expression in Online Social Networks. Science Advances, 7, eabe5641. https://doi.org/10.1126/sciadv.abe5641
Bashir, H. and Bhat, S.A. (2017) Effects of Social Media on Mental Health: A Review. The International Journal of Indian Psychology, 4, 125-131. https://doi.org/10.25215/0403.134
Strickland, A.C. (2014) Exploring the Effects of Social Media Use on the Mental Health of Young Adults. Ph.D. Thesis, University of Central Florida, Orlando.
Crockett, M.J. (2017) Moral Outrage in the Digital Age. Nature Human Behaviour, 1, 769-771. https://doi.org/10.1038/s41562-017-0213-3
Thompson, N. and Lapowsky, I. (2018) How Russian Trolls Used Meme Warfare to Divide America. https://www.wired.com/story/russia-ira-propaganda-senate-report/
Gadde, V. and Roth, Y. (2018) Enabling Further Research of Information Operations on Twitter. Twitter Blog. https://blog.twitter.com/en_us/topics/company/2018/enabling-further-research-of-information-operations-on-twitter
Information Operations (n.d.) Twitter Moderation Research Consortium. https://transparency.twitter.com/en/reports/information-operations.html