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UID:pretalx-2024-VJYHQA@conferences.acspri.org.au
DTSTART;TZID=AEST:20241128T114500
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DESCRIPTION:Belief revision is the process of updating one’s beliefs when 
 presented with new evidence\, while persuasion aims to change those belief
 s. Traditional models of belief revision focus on face-to-face interaction
 s\, but with the rise of social media\, new models are needed to capture b
 elief revision in text-based online discourse. Here we utilise large langu
 age models (LLMs) to develop a model that predicts successful belief revis
 ion using features derived from psychological studies.\nOur approach lever
 ages LLMs for dimension reduction\, using  generated ratings to build a ra
 ndom forest classification model that predicts whether a message will resu
 lt in belief change. Results show that serendipity and willingness to shar
 e are the top-ranking features in the model. Our findings provide insights
  into the characteristics of persuasive messages and demonstrate how LLMs 
 can enhance models based on psychological theory. Given these insights\, t
 his work has broader applications in areas like online influence detection
  and misinformation mitigation\, and potential ways to measure the effecti
 veness of online narratives.
DTSTAMP:20260809T223744Z
LOCATION:Sutherland Room
SUMMARY:Leveraging Large Language Models for Efficient Persuasion Detection
  in Online Discourse - Gia Bao
URL:https://conferences.acspri.org.au/2024/talk/VJYHQA/
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