AI-Driven Mediation Strategies for Audience Depolarisation in Online Debates

Abstract

Online polarisation can tear the fabric of civility through reinforcing social media's perceptions of division and discord. Social media platforms often rely on content-moderation to combat polarisation, contingent on the reactive removal or fagging of content. However, this approach often remains agnostic of the underlying debate's ideas and stifes open discourse. In this study, we use prompt-tuned language models to mediate social media debates, applying the strategies of the Thomas-Kilmann Confict Mode Instrument (TKI). We evaluate multiple mediation strategies in providing targeted responses to the debates, as shown to a debate audience. Our fndings show that high-cooperativeness TKI strategies ofered more persuasive arguments, while an accommodating argument strategy was the most successful at depolarising the audience's opinion. Furthermore, high-cooperativeness strategies also increased the perception that the debaters will reach a consensus. Our work paves the way for scalable and personalised tools that mediate social media debates to encourage depolarisation.