{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T18:03:18Z","timestamp":1778695398917,"version":"3.51.4"},"reference-count":80,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,7,28]],"date-time":"2025-07-28T00:00:00Z","timestamp":1753660800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"MCIN\/AEI\/10.13039\/501100011033"},{"name":"European Union \u2018NextGenerationEU\/PRTR\u2019"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>The rise in online communication platforms has significantly increased exposure to harmful discourse, presenting ongoing challenges for digital moderation and user well-being. This paper introduces the EsCorpiusBias corpus, designed to enhance the automated detection of sexism and racism within Spanish-language online dialogue, specifically sourced from the Mediavida forum. By means of a systematic, context-sensitive annotation protocol, approximately 1000 three-turn dialogue units per bias category are annotated, ensuring the nuanced recognition of pragmatic and conversational subtleties. Here, annotation guidelines are meticulously developed, covering explicit and implicit manifestations of sexism and racism. Annotations are performed using the Prodigy tool (v1. 16.0) resulting in moderate to substantial inter-annotator agreement (Cohen\u2019s Kappa: 0.55 for sexism and 0.79 for racism). Models including logistic regression, SpaCy\u2019s baseline n-gram bag-of-words model, and transformer-based BETO are trained and evaluated, demonstrating that contextualized transformer-based approaches significantly outperform baseline and general-purpose models. Notably, the single-turn BETO model achieves an ROC-AUC of 0.94 for racism detection, while the contextual BETO model reaches an ROC-AUC of 0.87 for sexism detection, highlighting BETO\u2019s superior effectiveness in capturing nuanced bias in online dialogues. Additionally, lexical overlap analyses indicate a strong reliance on explicit lexical indicators, highlighting limitations in handling implicit biases. This research underscores the importance of contextually grounded, domain-specific fine-tuning for effective automated detection of toxicity, providing robust resources and methodologies to foster socially responsible NLP systems within Spanish-speaking online communities.<\/jats:p>","DOI":"10.3390\/fi17080340","type":"journal-article","created":{"date-parts":[[2025,7,28]],"date-time":"2025-07-28T14:05:47Z","timestamp":1753711547000},"page":"340","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["EsCorpiusBias: The Contextual Annotation and Transformer-Based Detection of Racism and Sexism in Spanish Dialogue"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9139-2622","authenticated-orcid":false,"given":"Ksenia","family":"Kharitonova","sequence":"first","affiliation":[{"name":"Department Software Engineering, University of Granada, 18071 Granada, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2214-0245","authenticated-orcid":false,"given":"David","family":"P\u00e9rez-Fern\u00e1ndez","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Universidad Aut\u00f3noma de Madrid, Ciudad Universitaria de Cantoblanco, 28049 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Javier","family":"Guti\u00e9rrez-Hernando","sequence":"additional","affiliation":[{"name":"Department Software Engineering, University of Granada, 18071 Granada, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7368-6950","authenticated-orcid":false,"given":"Asier","family":"Guti\u00e9rrez-Fandi\u00f1o","sequence":"additional","affiliation":[{"name":"LHF Labs, 48007 Bilbao, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8891-5237","authenticated-orcid":false,"given":"Zoraida","family":"Callejas","sequence":"additional","affiliation":[{"name":"Department Software Engineering, University of Granada, 18071 Granada, Spain"},{"name":"Research Centre for Information and Communication Technologies (CITIC-UGR), University of Granada, 18071 Granada, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6266-5321","authenticated-orcid":false,"given":"David","family":"Griol","sequence":"additional","affiliation":[{"name":"Department Software Engineering, University of Granada, 18071 Granada, Spain"},{"name":"Research Centre for Information and Communication Technologies (CITIC-UGR), University of Granada, 18071 Granada, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1097","DOI":"10.1162\/coli_a_00524","article-title":"Bias and Fairness in Large Language Models: A Survey","volume":"50","author":"Gallegos","year":"2024","journal-title":"Comput. 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