A Hybrid Theory and Data-driven Approach to Persuasion Detection with Large Language Models
This work addresses the need for scalable models of persuasion detection in social media, with applications in online influence detection and misinformation mitigation, though it is incremental as it builds on existing psychological theories.
The researchers tackled the problem of predicting belief change in online discourse by developing a hybrid model that uses large language models (LLMs) to extract psychological features, with epistemic emotion and willingness to share identified as top predictors of persuasion.
Traditional psychological models of belief revision focus on face-to-face interactions, but with the rise of social media, more effective models are needed to capture belief revision at scale, in this rich text-based online discourse. Here, we use a hybrid approach, utilizing large language models (LLMs) to develop a model that predicts successful persuasion using features derived from psychological experiments. Our approach leverages LLM generated ratings of features previously examined in the literature to build a random forest classification model that predicts whether a message will result in belief change. Of the eight features tested, \textit{epistemic emotion} and \textit{willingness to share} were the top-ranking predictors of belief change in the model. Our findings provide insights into the characteristics of persuasive messages and demonstrate how LLMs can enhance models of successful persuasion based on psychological theory. Given these insights, this work has broader applications in fields such as online influence detection and misinformation mitigation, as well as measuring the effectiveness of online narratives.