Rumor Detection

Rumor detection research aims to automatically identify and classify false information spreading on social media, focusing on mitigating its societal impact. Current efforts leverage various machine learning techniques, including graph neural networks to analyze information propagation patterns, and large language models to assess textual content and generate counter-narratives. These models are increasingly incorporating multimodal data (text, images, videos) and addressing challenges like early detection, cross-domain generalization, and robustness against adversarial attacks. The field's advancements are crucial for improving online information credibility and informing strategies for combating misinformation.