@misc{10481/106228,
year = {2025},
month = {7},
url = {https://hdl.handle.net/10481/106228},
abstract = {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’s Kappa: 0.55 for sexism
and 0.79 for racism). Models including logistic regression, SpaCy’s baseline n-gram bagof-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’s 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.},
organization = {MCIN/AEI/10.13039/501100011033 (TED2021-132470B-I00)},
publisher = {MDPI},
keywords = {hate speech detection},
keywords = {bias},
keywords = {natural language processing},
keywords = {corpus annotation},
keywords = {sexism and racism detection},
keywords = {machine learning for toxicity},
title = {EsCorpiusBias: The Contextual Annotation and Transformer-Based Detection of Racism and Sexism in Spanish Dialogue},
doi = {10.3390/fi17080340},
author = {Kharitonova, Ksenia and Pérez Fernández, David and Gutiérrez Hernando, Javier and Gutiérrez Fandiño, Asier and Callejas Carrión, Zoraida and Griol Barres, David},
}