Misinformation Claim
Misinformation research focuses on detecting and mitigating the spread of false or misleading information, aiming to understand its creation, propagation, and impact. Current efforts utilize various machine learning models, including large language models (LLMs) and graph neural networks, often incorporating techniques like attention mechanisms and retrieval-augmented generation to improve accuracy and explainability. This field is crucial for addressing the societal harms of misinformation, informing the development of tools for fact-checking, social media moderation, and public health communication, and improving the reliability of AI systems themselves.
Papers
Diverse, but Divisive: LLMs Can Exaggerate Gender Differences in Opinion Related to Harms of Misinformation
Terrence Neumann, Sooyong Lee, Maria De-Arteaga, Sina Fazelpour, Matthew Lease
Capturing Pertinent Symbolic Features for Enhanced Content-Based Misinformation Detection
Flavio Merenda, José Manuel Gómez-Pérez
Image-Text Out-Of-Context Detection Using Synthetic Multimodal Misinformation
Fatma Shalabi, Huy H. Nguyen, Hichem Felouat, Ching-Chun Chang, Isao Echizen