Misinformation such as fake news and rumors is a serious threat for information ecosystems
and public trust. The emergence of large language models (LLMs) has great potential to …

The emergence of various social networks has generated vast volumes of data. Efficient
methods for capturing, distinguishing, and filtering real and fake news are becoming …

The proliferation of fake news on social media has the probability to bring an unfavorable
impact on public opinion and social development. Many efforts have been paid to develop …

Fake news spread widely on social media in various domains, which lead to real-world
threats in many aspects like politics, disasters, and finance. Most existing approaches focus …

The wide spread of fake news is increasingly threatening both individuals and society. Great
efforts have been made for automatic fake news detection on a single domain (eg, politics) …

The wide dissemination of fake news is increasingly threatening both individuals and
society. Fake news detection aims to train a model on the past news and detect fake news of …

Many recent studies have demonstrated that the propagation patterns of news on social
media can facilitate the detection of fake news. Most of these studies rely on the complete …

The detection of fake news often requires sophisticated reasoning skills, such as logically
combining information by considering word-level subtle clues. In this paper, we move …

With social media being a major force in information consumption, accelerated propagation
of fake news has presented new challenges for platforms to distinguish between legitimate …

Massive false rumors emerging along with breaking news or trending topics severely hinder
the truth. Existing rumor detection approaches achieve promising performance on the …