Mitigating opinion polarization in social networks using adversarial attacks | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Mitigating opinion polarization in social networks using adversarial attacks Genki Ichinose, Michinori Ninomiya, Katsumi Chiyomaru, Kazuhiro Takemoto This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5301229/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Mar, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract In recent years, the spread of social networking services (SNS) has made it easier to connect with people who have similar opinions. Accordingly, similar opinions are shared within a group, while the frequency of exposure to different opinions tends to decrease. As a result, the polarization of opinion among groups is more likely to occur. Some studies have been conducted to identify the conditions under which opinion polarization occurs by simulating opinion dynamics, but specific methods for mitigating it have been poorly understood. Recently, it was found that even a few artificial perturbations inspired by the adversarial attack reverse the result in voter models, where a minority opinion becomes dominant through these perturbations. In this study, we conducted numerical simulations to determine whether it is possible to mitigate opinion polarization by adding such perturbations to the network in opinion dynamics models. The results show that opinion polarization can be mitigated by strategically generating perturbations to the weights of network links, and the effect increases as the perturbation strength parameter increases. Moreover, our analysis reveals that the effectiveness of this polarization mitigating method is enhanced in larger networks. Our results propose an effective way to prevent polarization of opinion in social networks. Physical sciences/Mathematics and computing/Computational science Physical sciences/Mathematics and computing/Computer science Earth and environmental sciences/Environmental social sciences/Psychology and behaviour Earth and environmental sciences/Environmental social sciences/Socioeconomic scenarios Full Text Additional Declarations No competing interests reported. Supplementary Files PolarizationAdversarialAttackSI.pdf Cite Share Download PDF Status: Published Journal Publication published 16 Mar, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 30 Dec, 2024 Reviews received at journal 28 Dec, 2024 Reviewers agreed at journal 20 Dec, 2024 Reviews received at journal 25 Nov, 2024 Reviewers agreed at journal 14 Nov, 2024 Reviewers agreed at journal 12 Nov, 2024 Reviewers invited by journal 12 Nov, 2024 Editor assigned by journal 12 Nov, 2024 Editor invited by journal 12 Nov, 2024 Submission checks completed at journal 12 Nov, 2024 First submitted to journal 21 Oct, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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