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
Multi-modal Misinformation Detection: Approaches, Challenges and Opportunities
Sara Abdali, Sina shaham, Bhaskar Krishnamachari
An Audit of Misinformation Filter Bubbles on YouTube: Bubble Bursting and Recent Behavior Changes
Matus Tomlein, Branislav Pecher, Jakub Simko, Ivan Srba, Robert Moro, Elena Stefancova, Michal Kompan, Andrea Hrckova, Juraj Podrouzek, Maria Bielikova