AI-Driven Mediation Strategies for Audience Depolarisation in Online Debates

要旨

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 flagging of content. However, this approach often remains agnostic of the underlying debate's ideas and stifles open discourse. In this study, we use prompt-tuned language models to mediate social media debates, applying the strategies of the Thomas-Kilmann Conflict Mode Instrument (TKI). We evaluate multiple mediation strategies in providing targeted responses to the debates, as shown to a debate audience. Our findings show that high-cooperativeness TKI strategies offered 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.

著者
Jarod Govers
University of Melbourne, Melbourne, Victoria, Australia
Eduardo Velloso
University of Melbourne, Melbourne, Victoria, Australia
Vassilis Kostakos
University of Melbourne, Melbourne, Victoria, Australia
Jorge Goncalves
University of Melbourne, Melbourne, Australia
論文URL

doi.org/10.1145/3613904.3642322

動画

会議: CHI 2024

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2024.acm.org/)

セッション: Reflecting on Online Content

317
5 件の発表
2024-05-14 20:00:00
2024-05-14 21:20:00