Fact Checking
Fact-checking research aims to automate the verification of claims, combating the spread of misinformation across various media. Current efforts focus on improving evidence retrieval using techniques like contrastive learning and leveraging large language models (LLMs) for claim verification and explanation generation, often incorporating knowledge graphs and multimodal data (text and images). These advancements are crucial for enhancing the accuracy and efficiency of fact-checking, with implications for journalism, public health communication, and broader efforts to mitigate the impact of misinformation.
Papers
Monant Medical Misinformation Dataset: Mapping Articles to Fact-Checked Claims
Ivan Srba, Branislav Pecher, Matus Tomlein, Robert Moro, Elena Stefancova, Jakub Simko, Maria Bielikova
Science Checker: Extractive-Boolean Question Answering For Scientific Fact Checking
Loïc Rakotoson, Charles Letaillieur, Sylvain Massip, Fréjus Laleye
CoVERT: A Corpus of Fact-checked Biomedical COVID-19 Tweets
Isabelle Mohr, Amelie Wührl, Roman Klinger