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 Recent studies on Graph Neural Networks (GNNs) provide both empirical and
theoretical evidence supporting their effectiveness in capturing structural patterns on both …

Claim verification plays a crucial role in combating misinformation. While existing works on
claim verification have shown promising results, a crucial piece of the puzzle that remains …

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 …

Many real-world graph learning tasks require handling dynamic graphs where new nodes
and edges emerge. Dynamic graph learning methods commonly suffer from the catastrophic …

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 proliferation of Large Language Models (LLMs) has driven considerable interest in fine-
tuning them with domain-specific data to create specialized language models. Nevertheless …

Existing reinforcement learning (RL) mainly utilize online or offline settings. The online
methods explore the environment with expensive time cost, and the offline methods …

The problem of community-level information pathway prediction (CLIPP) aims at predicting
the transmission trajectory of content across online communities. A successful solution to …

Audiovisual data is everywhere in this digital age, which raises higher requirements for the
deep learning models developed on them. To well handle the information of the multi-modal …