Fighting disinformation with AI tools: datasets for detecting fake news on climate change and the Russian invasion of Ukraine

Purpose This paper provides an extensive overview of existing datasets designed for the AI-based detection of false information, and it presents two new datasets. These datasets contain disinformation cases on climate change and the Russian invasion of Ukraine. Design/methodology/approach The Debunking datasets were built using a systematic and methodologically rigorous approach to ensure data accuracy and representativeness. Data collection involved the amalgamation of multiple sources and put together a wide range of disinformation and real claims and related multimedia content. Findings Early tests demonstrate the potential of these datasets to improve the accuracy of fake news classification in these domains. The study concludes with a discussion on future developments and the ethical implications of AI-based fact-checking systems. Originality/value The study produced high-quality, domain-specific datasets that address the deficiencies of existing resources. First, a fundamental aspect of the Debunking datasets is domain-specific curation. In fact, unlike general fake news datasets, the AI4Debunk project focuses on two critical areas where disinformation has a profound societal impact. Additionally, Debunking datasets consist of multimodal data. That means the datasets contain images, videos and social media metadata in addition to text statements. Another major strength of the Debunking datasets is their intensive annotation process. The data labeling process is conducted by experienced fact-checkers so that the disinformation claims are accurately labelled.