Misinformation Mitigation
Misinformation mitigation research aims to develop effective methods for detecting and counteracting the spread of false or misleading information, particularly online. Current efforts heavily utilize large language models (LLMs), often augmented with web search capabilities or other data sources, to analyze text and multimedia content, identify misinformation tactics, and generate accurate counter-narratives. These approaches are evaluated using various metrics, including accuracy, fairness, and explainability, with a focus on improving model calibration and generalization across different languages and contexts. This field is crucial for safeguarding public discourse and mitigating the harmful societal impacts of misinformation.
Papers
Nexus at ArAIEval Shared Task: Fine-Tuning Arabic Language Models for Propaganda and Disinformation Detection
Yunze Xiao, Firoj Alam
ArAIEval Shared Task: Persuasion Techniques and Disinformation Detection in Arabic Text
Maram Hasanain, Firoj Alam, Hamdy Mubarak, Samir Abdaljalil, Wajdi Zaghouani, Preslav Nakov, Giovanni Da San Martino, Abed Alhakim Freihat