Year: 2026
Type: conference-paper
Abstract: The rise of toxic content on digital platforms has intensified the demand for automatic moderation tools.While English has benefited from large-scale annotated corpora, Spanish remains under-resourced, particularly for nuanced cases of toxicity such as irony, sarcasm, or indirect aggression.We present an extended version of the NECOS-TOX corpus, Social-Tox, comprising 4,011 Spanish comments collected from 16 major news outlets.Each comment is annotated across three levels of toxicity (Non-Toxic, Slightly Toxic, and Toxic), following an iterative annotation protocol that achieved substantial inter-annotator agreement (k = 0.74).To reduce annotation costs while maintaining quality, we employed a humanin-the-loop active learning strategy, with manual correction of model pre-labels.We benchmarked the dataset with traditional machine learning (ML) methods, domain-specific transformers, and instruction-tuned large language models (LLMs).Results show that compact encoder models (e.g., RoBERTa-base-bne, 125M parameters) perform on par with much larger models (e.g., LLaMA-3.1-8B),underscoring the value of in-domain adaptation over raw scale.Our error analysis highlights persistent challenges in distinguishing subtle forms of toxicity, especially sarcasm and implicit insults, and reveals entity-related biases that motivate anonymization strategies.The dataset and trained models are released publicly.
Institutions: Universidad de Jaén, University of Alicante
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funder: European Commission
OpenAlex ID: W7140143473