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 …

Abstract Graph Structure Learning (GSL) has recently garnered considerable attention due
to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the …

Fake news possesses a destructive and negative impact on our lives. With the rapid growth
of multimodal content in social media communities, multimodal fake news detection has …

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 …

Multimodal fake news detection, which aims to detect fake news across vast amounts of
multimodal data in social networks, greatly contributes to identifying potential risks on the …

Graph Neural Networks (GNNs) have emerged as a mainstream technique for graph
representation learning. However, their efficacy within an end-to-end supervised framework …

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 …

Social media platforms have been establishing content moderation guidelines and
employing various moderation policies to counter hate speech and misinformation. The goal …

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 …

Large language models (LLMs) are transforming the ways the general public accesses and
consumes information. Their influence is particularly pronounced in pivotal sectors like …