Misinformation such as fake news and rumors is a serious threat on information ecosystems
and public trust. The emergence of Large Language Models (LLMs) has great potential to …

Cross-modal learning is essential to enable accurate fake news detection due to the fast-
growing multimodal contents in online social communities. A fundamental challenge of …

The wide spread of fake news has caused serious societal issues. We propose a subgraph
reasoning paradigm for fake news detection, which provides a crystal type of explainability …

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 …

The spread of fake news has become a significant social problem, drawing great concern for
fake news detection (FND). Pretrained language models (PLMs), such as BERT and …

Recent efforts in fake news detection have witnessed a surge of interest in using graph
neural networks (GNNs) to exploit rich social context. Existing studies generally leverage …

Recent years have witnessed a massive growth in the proliferation of fake news online. User-
generated content is a blend of text and visual information leading to producing different …

Though a fair amount of research is being done to address disinformation in online social
media, it has so far managed to stay ahead of the researchers' learning curves forcing the …

Misinformation poses a critical societal challenge, and current approaches have yet to
produce an effective solution. We propose focusing on generalization, uncertainty, and how …

It is commonly perceived that online fake news and reliable news exhibit stark differences in
writing styles, such as the use of sensationalist versus objective language. However, we …