Multilingual, Multimodal Pipeline for Creating Authentic and Structured Fact-Checked Claim Dataset
This addresses the need for robust fact-checking resources against misinformation, though it is incremental as it builds on existing ClaimReview feeds with new processing methods.
The paper tackles the problem of limited multilingual multimodal fact-checking datasets by introducing a pipeline that constructs structured datasets in French and German, using LLMs for evidence extraction and justification generation, with evaluation showing it enables fine-grained comparison of fact-checking practices and supports interpretable model development.
The rapid proliferation of misinformation across online platforms underscores the urgent need for robust, up-to-date, explainable, and multilingual fact-checking resources. However, existing datasets are limited in scope, often lacking multimodal evidence, structured annotations, and detailed links between claims, evidence, and verdicts. This paper introduces a comprehensive data collection and processing pipeline that constructs multimodal fact-checking datasets in French and German languages by aggregating ClaimReview feeds, scraping full debunking articles, normalizing heterogeneous claim verdicts, and enriching them with structured metadata and aligned visual content. We used state-of-the-art large language models (LLMs) and multimodal LLMs for (i) evidence extraction under predefined evidence categories and (ii) justification generation that links evidence to verdicts. Evaluation with G-Eval and human assessment demonstrates that our pipeline enables fine-grained comparison of fact-checking practices across different organizations or media markets, facilitates the development of more interpretable and evidence-grounded fact-checking models, and lays the groundwork for future research on multilingual, multimodal misinformation verification.