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.

通过采用大型语言模型作为用户模拟器和评论生成器，我们提出了 GenFEND，一种生成的反馈增强检测框架，通过为多个子人群组提示大型语言模型并聚合生成的评论，以做到多样化评论的可用性，尤其是来自沉默用户的评论。实验证明了 GenFEND 的有效性，并进一步分析表明，生成的评论涵盖了更多多样化的用户，并且甚至比实际评论更有效。