  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.
