Résumé
Given the recent proliferation of fake news online, fact-checking has emerged as a critical defence against misinformation. Several fact-checking organisations are currently employed in the initiative to assess the truthfulness of online claims. Verified claims serve as foundational data for various cross-domain research, including fields of social science and natural language processing, where they are used to study misinformation and several downstream tasks such as automated fact-verification. However, these fact-checking websites inherently harbour biases, posing challenges for academic endeavours aiming to discern truth from misinformation. In this study, we aim to explore the evolving landscape of online claims verified by multiple fact-checking organisations and analyse the underlying biases of individual fact-checking websites. Leveraging ClaimsKG, the largest available corpus of fact-checked claims, we analyse the temporal evolution of claims, focusing on topics, veracity levels, and entities to offer insights into the complex dimensions of online information. We utilise data and dimensions available from ClaimsKG for our analysis and for dimensions such as topics which are not present in ClaimsKG, we create a topic taxonomy and implement a transformer-based model, for multi-label classification of claims. We also observe how similar claims are co-occurant amongst different websites. Our work serves as a standardised framework for categorising claims sourced from diverse fact-checking organisations, laying the foundation for coherent and interpretable fact-checking datasets. The analysis conducted in this work sheds light on the dynamic landscape of online claims verified by several fact-checking organisations and dives into biases and distributions of several fact-checking websites.