Enhancing Debunking Effectiveness through LLM-based Personality Adaptation
This addresses the challenge of making fake news debunking more effective for specific audiences, though it is incremental as it builds on existing LLM and personality research.
The study tackled the problem of fake news debunking by proposing a method to generate personalized messages using LLMs adapted to Big Five personality traits, finding that personalized messages were generally more persuasive, with traits like Openness increasing persuadability and Neuroticism lowering it.
This study proposes a novel methodology for generating personalized fake news debunking messages by prompting Large Language Models (LLMs) with persona-based inputs aligned to the Big Five personality traits: Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness. Our approach guides LLMs to transform generic debunking content into personalized versions tailored to specific personality profiles. To assess the effectiveness of these transformations, we employ a separate LLM as an automated evaluator simulating corresponding personality traits, thereby eliminating the need for costly human evaluation panels. Our results show that personalized messages are generally seen as more persuasive than generic ones. We also find that traits like Openness tend to increase persuadability, while Neuroticism can lower it. Differences between LLM evaluators suggest that using multiple models provides a clearer picture. Overall, this work demonstrates a practical way to create more targeted debunking messages exploiting LLMs, while also raising important ethical questions about how such technology might be used.