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

The detection of toxic content in social media has become a critical task in Natural Language Processing (NLP), particularly given its intersection with complex issues like subjectivity, implicit language, and cultural context. Among these challenges, bias in training data remains a central concern—especially as language models risk reproducing and amplifying societal inequalities. This paper investigates the interplay between toxicity and gender bias on Twitter/X by introducing a novel dataset of violent and non-violent tweets, annotated not only for violence but also for gender. We conduct an exploratory analysis of how biased data can distort toxicity classification and present algorithms to mitigate these effects through dataset balancing and debiasing. Our contributions include four new dataset splits—two balanced and two debiased—that aim to support the development of fairer and more inclusive NLP models. By foregrounding the importance of equity in data curation, this work lays the groundwork for more ethical approaches to automated violence detection and gender annotation.