Large Language Models (LLMs) suffer from hallucinations, referring to the non-factual
information in generated content, despite their superior capacities across tasks. Meanwhile …

Both accuracy and timeliness are key factors in detecting fake news on social media.
However, most existing methods encounter an accuracy-timeliness dilemma: Content-only …

The advent of large language models (LLMs) has revolutionized online content creation,
making it much easier to generate high-quality fake news. This misuse threatens the integrity …

Fake news has become a significant challenge on online social platforms, increasing
uncertainty and unwanted tension in society. The negative impact of fake news on political …

Evidence-enhanced detectors present remarkable abilities in identifying malicious social
text with related evidence. However, the rise of large language models (LLMs) brings …

Information spreads faster through social media platforms than traditional media, thus
becoming an ideal medium to spread misinformation. Meanwhile, automated accounts …

The rapid spread of rumors on social media platforms during breaking events severely
hinders the dissemination of the truth. Previous studies reveal that the lack of annotated …

Current rumor detectors exhibit limitations in fully exploiting responses to the source tweet
as essential public opinions, and in explaining and indicating the reliability of the results …

In implicit emotion analysis (IEA), the subtlety of emotional expressions makes it particularly
sensitive to user-specific characteristics. Existing studies often inject personalization into the …