FactOReS: Fact-checking with an Evidence-based Open Resource in Spanish

Abstract

Automated Fact-Checking (AFC) has become a popular research area in Natural Language Processing (NLP), intending to support human verification through evidence-based veracity prediction systems that provide transparency at each stage of the process. Despite the global significance of misinformation and the substantial progress made in AFC research, multilingual approaches to evidence-based fact-checking remain inadequately addressed. This work introduces FactOReS, the first publicly available dataset evaluated for evidence-based veracity prediction in Spanish, constructed from real Spanish-language claims and verified fact-checking articles. We establish performance baselines by systematically applying In-Context Learning (ICL) with Large Language Models (LLMs) to both an established English dataset and our novel Spanish dataset. Despite good zero-shot and few-shot performance, results in both languages demonstrate that each step requires further research in order to improve the overall results in the evidence-based veracity prediction task. Finally, we propose a semi-automated methodology that integrates computational processing with human validation, offering a reproducible framework for developing multilingual evidence-based fact-checking resources for the benefit of the NLP research community. Data and code available: https://github.com/hitz-zentroa/AFC_FactOReS

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