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Nudging Civility on Online Social Networks with Large Language Models

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  • t'Serstevens, François
  • Oschatz, Corinna
  • , Abdul.Sittar
  • Trilling, Damian
  • Guček, Alenka

Abstract

Online social networks have become a central arena for political and social discourse, yet interactions on these platforms are frequently characterized by hostile interactions. While disagreement is a normal and required feature of democratic debate, research suggests that disrespectful communication discourages users from engaging in political discussions and may negatively affect both participants and the broader audience exposed to such interactions. In response, previous interventions have attempted to improve online discourse through behavioral nudges and interface design changes, though their effectiveness has often been limited. This study examines whether AI-mediated paraphrasing interventions can reduce uncivil expression while preserving substantive disagreement in online political discussions. Using an experimental setting that simulates social media interactions, we analyse how AI-generated paraphrases influence the tone of conversations and assess their effects not only on the direct participants of a debate but also on external observers who encounter these exchanges. The findings provide insights into the potential of AI-assisted communication tools to foster healthier online discourse.

Suggested Citation

  • t'Serstevens, François & Oschatz, Corinna & , Abdul.Sittar & Trilling, Damian & Guček, Alenka, 2026. "Nudging Civility on Online Social Networks with Large Language Models," SocArXiv jhbuf_v1, Center for Open Science.
    • Handle: RePEc:osf:socarx:jhbuf_v1
    DOI: 10.31235/osf.io/jhbuf_v1
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    References listed on IDEAS

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    1. Bor, Alexander & Petersen, Michael Bang, 2022. "The Psychology of Online Political Hostility: A Comprehensive, Cross-National Test of the Mismatch Hypothesis," American Political Science Review, Cambridge University Press, vol. 116(1), pages 1-18, February.
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