Let Silence Speak: Enhancing Fake News Detection with Generated Comments from Large Language Models

TL;DR

This paper introduces GenFEND, a framework that uses large language models to generate diverse comments for fake news detection, especially from silent users, improving detection accuracy.

Contribution

It proposes a novel method to generate diverse comments using LLMs to enhance fake news detection, addressing the challenge of limited real user comments.

Findings

Generated comments cover more diverse user opinions

Generated comments can outperform real comments in detection accuracy

GenFEND improves early fake news detection performance

Abstract

Fake news detection plays a crucial role in protecting social media users and maintaining a healthy news ecosystem. Among existing works, comment-based fake news detection methods are empirically shown as promising because comments could reflect users' opinions, stances, and emotions and deepen models' understanding of fake news. Unfortunately, due to exposure bias and users' different willingness to comment, it is not easy to obtain diverse comments in reality, especially for early detection scenarios. Without obtaining the comments from the ``silent'' users, the perceived opinions may be incomplete, subsequently affecting news veracity judgment. In this paper, we explore the possibility of finding an alternative source of comments to guarantee the availability of diverse comments, especially those from silent users. Specifically, we propose to adopt large language models (LLMs) as a user simulator and comment generator, and design GenFEND, a generated feedback-enhanced detection framework, which generates comments by prompting LLMs with diverse user profiles and aggregating generated comments from multiple subpopulation groups. Experiments demonstrate the effectiveness of GenFEND and further analysis shows that the generated comments cover more diverse users and could even be more effective than actual comments.