Aug 2026· The Review of Economic Studies· 0 citations
Abstract
We study opinion dynamics in a social network consisting of two groups. Agents update their opinions by conforming to members of their own group while rejecting the views of the opposing group (affective polarization), and by listening to a media outlet that may provide biased information. We characterize the long-run opinions and identify when affective polarization and media bias lead to ideological polarization, persistent disagreement, or failures of learning. We also derive when information interventions or censorship improve the accuracy of average opinions} and reduce disagreement, and when they backfire: better information helps only under specific media bias configurations and when directed to the agents we identify as most effective at propagating it through the network.
This paper introduces the concept of affective polarization – the tendency for an individual to distrust someone not because of their specific opinion, but because of their political party – into an opinion dynamics model and shows that incorporating affective polarization into the opinion dynamics model affects the le...
This study investigates the relationship between algorithm-driven social media content and the growing phenomenon of political polarization. With social media platforms increasingly relying on personalized algorithms to curate user feeds, concerns have emerged about the creation of ideological echo chambers and the rei...
Entisar Al-Obaidi, M. Mallek, Fakhri Elfaki· International Journal of Sci...· 1 citation
The results demonstrate that grounding Mistral-7B within empirically cloned interaction networks produces a realistic simulation framework capable of reproducing complex social dynamics.
Omran Berjawi, G. Fenza, Rida Khatoun et al.· 0 citations
Understanding the dynamics of stance change on social media is crucial for addressing polarization and information integrity, yet observational studies face challenges including limited experimental control, restricted data access, and algorithmic confounds. We leverage Generative Agent-Based Modeling (GABM)—a novel si...
Valerio La Gatta, Gian Marco Orlando, Marco Perillo et al.· Proceedings of the 37th ACM...· 1 citation
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