Mitigating Disinformation in Social Networks through Noise
Abstract: An abundance of literature has shown that the injection of noise into complex socio-economic systems can improve their resilience. This study aims to understand whether the same applies in the context of information diffusion in social networks. Specifically, we aim to understand whether the injection of noise in a social network of agents seeking to uncover a ground truth among a set of competing hypotheses can build resilience against disinformation. We implement two different stylized policies to inject noise in a social network, i.e., via random bots and via randomized recommendations, and find both to improve the population's overall belief in the ground truth. Notably, we find noise to be as effective as debunking when disinformation is particularly strong. On the other hand, such beneficial effects may lead to a misalignment between the agents' privately held and publicly stated beliefs, a phenomenon which is reminiscent of cognitive dissonance.
- R. N. Mantegna and B. Spagnolo, Noise enhanced stability in an unstable system, Physical review letters 76, 563 (1996).
- R. Albert, H. Jeong, and A.-L. Barabási, Error and attack tolerance of complex networks, nature 406, 378 (2000).
- A. E. Biondo, A. Pluchino, and A. Rapisarda, The beneficial role of random strategies in social and financial systems, Journal of Statistical Physics 151, 607 (2013).
- G. Livan, Don’t follow the leader: how ranking performance reduces meritocracy, Royal Society Open Science 6, 191255 (2019).
- M. Bardoscia, G. Livan, and M. Marsili, Statistical mechanics of complex economies, Journal of Statistical Mechanics: Theory and Experiment 2017, 043401 (2017).
- M. de Arquer, B. Ponte, and R. Pino, Examining the balance between efficiency and resilience in closed-loop supply chains, Central European Journal of Operations Research 30, 1307 (2022).
- A. Pluchino, A. Rapisarda, and C. Garofalo, The peter principle revisited: A computational study, Physica A: Statistical Mechanics and its Applications 389, 467 (2010).
- J. D. Farmer, P. Patelli, and I. I. Zovko, The predictive power of zero intelligence in financial markets, Proceedings of the National Academy of Sciences 102, 2254 (2005).
- S. Vosoughi, D. Roy, and S. Aral, The spread of true and false news online, science 359, 1146 (2018).
- A. Mitra, J. A. Richards, and S. Sundaram, A new approach to distributed hypothesis testing and non-bayesian learning: Improved learning rate and byzantine resilience, IEEE Transactions on Automatic Control 66, 4084 (2020).
- L. Su and N. H. Vaidya, Defending non-bayesian learning against adversarial attacks, Distributed Computing 32, 277 (2019).
- J. Li and X. Chang, Combating misinformation by sharing the truth: a study on the spread of fact-checks on social media, Information systems frontiers 25, 1479 (2023).
- A. Lalitha, T. Javidi, and A. D. Sarwate, Social learning and distributed hypothesis testing, IEEE Transactions on Information Theory 64, 6161 (2018).
- D. Riazi and G. Livan, Public and private beliefs under disinformation in social networks, Physica A: Statistical Mechanics and its Applications , 129621 (2024).
- J. Cooper, Cognitive dissonance: Where we’ve been and where we’re going, International Review of Social Psychology 32, 7 (2019).
- A. A. Gouda and T. Szántai, New sampling techniques for calculation of dirichlet probabilities, Central European Journal of Operations Research 12, 389 (2004).
- G. Livan, F. Caccioli, and T. Aste, Excess reciprocity distorts reputation in online social networks, Scientific reports 7, 3551 (2017).
- “Instagram Pauses Updates Following Criticism about Being Too Video Focused.” The Independent, Independent Digital News and Media, 29 July 2022, www.independent.co.uk/tech/instagram-video-update-tiktok-criticism-b2133731.html.
- M. Kunaver and T. Požrl, Diversity in recommender systems–a survey, Knowledge-based systems 123, 154 (2017).
- S. Bhadani, Biases in recommendation system, in Proceedings of the 15th ACM Conference on Recommender Systems (2021) pp. 855–859.
- M. Fernández, A. Bellogín, and I. Cantador, Analysing the effect of recommendation algorithms on the amplification of misinformation, arXiv preprint arXiv:2103.14748 (2021).
- A.-L. Barabási and R. Albert, Emergence of scaling in random networks, science 286, 509 (1999).
- Https://help.twitter.com/en/resources/addressing-misleading-info.
- N. N. Taleb, Antifragile: Things that gain from disorder, Vol. 3 (Random House Trade Paperbacks, 2014).
- D. P. Redlawsk, Hot cognition or cool consideration? testing the effects of motivated reasoning on political decision making, Journal of Politics 64, 1021 (2002).
- B. Nyhan and J. Reifler, When corrections fail: The persistence of political misperceptions, Political Behavior 32, 303 (2010).
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