---
title: "Nuanced Toxicity Detection in Spanish: A New Corpus and"
description: "Spanish has few annotated resources for toxic language detection, especially for subtle forms such as irony, sarcasm… 28 references."
authors: ["Alba María Mármol-Romero", "Robiert Sepúlveda-Torres", "Estela Saquete", "María-Teresa Martín-Valdivia", "L. Alfonso Ureña"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: EACL 2026"
source: "https://aclanthology.org/2026.findings-eacl.100/"
doi: "10.18653/v1/2026.findings-eacl.100"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/romero-2026-nuanced-toxicity-detection-spanish-new/"
---
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# Nuanced Toxicity Detection in Spanish: A New Corpus and Benchmark Study

**Authors:** [Alba María Mármol-Romero](https://md.research.elamur.ai/free/en/authors/alba-maria-marmol-romero/), [Robiert Sepúlveda-Torres](https://md.research.elamur.ai/free/en/authors/robiert-sepulveda-torres/), Estela Saquete, [María-Teresa Martín-Valdivia](https://md.research.elamur.ai/free/en/authors/maria-teresa-martin-valdivia/) and L. Alfonso Ureña · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: EACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-eacl.100/) · [PDF](https://aclanthology.org/2026.findings-eacl.100.pdf) · [DOI 10.18653/v1/2026.findings-eacl.100](https://doi.org/10.18653/v1/2026.findings-eacl.100) · [OpenAlex](https://openalex.org/W7140143473)

**Citations:** 1 citations (OpenAlex, 2026-10-14)

## Summary
Spanish has few annotated resources for toxic language detection, especially for subtle forms such as irony, sarcasm, mockery or delegitimization. Existing Spanish datasets are either tied to one topic (DETOXIS, immigration) or to one source (NECOS-TOX, El Mundo). The authors extend NECOS-TOX into SocialTox, a corpus of 4,011 reader comments from 16 Spanish news outlets, labelled on a three-level scale: Non-Toxic, Slightly Toxic and Toxic. To keep annotation affordable, they used human-in-the-loop active learning: a model proposed labels and annotators corrected them. Guidelines were revised over several rounds until agreement reached κ = 0.74.

The corpus is benchmarked with classical ML, Spanish transformer encoders and instruction-tuned LLMs. The compact RoBERTa-base-bne encoder performs on par with far larger generative models such as LLaMA-3.1-8B, which suggests that in-domain adaptation matters more than model size. Error analysis shows that borderline categories, sarcasm and implicit insults remain hard. Models also use named entities as shortcuts for predicting toxicity, and anonymization reduces this. The openly released data and models give Spanish moderation research a nuanced, multi-source benchmark.

## Key points
- SocialTox combines the 1,419 original NECOS-TOX comments with 2,592 new ones, for 4,011 comments in total. It is released on Hugging Face as gplsi/SocialTOX.
- Pilot annotation with four classes gave low agreement (κ \< 0.30), mostly because annotators confused Toxic with Very Toxic. Very Toxic comments were also rare, since moderators remove them. Merging the scheme into three levels raised Fleiss' κ to 0.74.
- The data was labelled by three annotators. Two were trained in linguistics, and the third came from sociology.
- RoBERTa-base-bne (125M parameters) rivals LLaMA-3.1-8B. Performance drops in the multi-class setting, mainly around the Slightly Toxic class.
- Calibration is weakest for the Toxic class, where the model is underconfident (ECE = 0.218, MCE = 0.461). Non-Toxic is the best calibrated class (ECE = 0.091). Anonymizing named entities reduces entity bias at a small cost in accuracy.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [University of Alicante](https://md.research.elamur.ai/free/en/universities/university-of-alicante/) — University, Alicante, Spain · 21 works · 1,098 citations
- [Universidad de Jaén](https://md.research.elamur.ai/free/en/universities/universidad-de-jaen/) — University, Jaén, Spain · 8 works · 1,671 citations

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## Cite

Cite as: Alba María Mármol-Romero, Robiert Sepúlveda-Torres, Estela Saquete, María-Teresa Martín-Valdivia and L. Alfonso Ureña. 2026. Nuanced Toxicity Detection in Spanish: A New Corpus and Benchmark Study. Findings of the Association for Computational Linguistics: EACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{marmolromero2026nuanced,
  title = {{Nuanced Toxicity Detection in Spanish: A New Corpus and Benchmark Study}},
  author = {Alba María Mármol-Romero, Robiert Sepúlveda-Torres, Estela Saquete, María-Teresa Martín-Valdivia and L. Alfonso Ureña},
  booktitle = {Findings of the Association for Computational Linguistics: EACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-eacl.100},
  url = {https://doi.org/10.18653/v1/2026.findings-eacl.100}
}
```

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27. A Newspaper Okdiario Marca El Mundo El País NTV España The Objective Alerta Digital El Debate Vozpópuli El Español ABC El Confidencial El Diario Moncloa Periodista Digital 20 Minutos • Normalization: Min-Max scaling applied to feature values. • Cross-validation: Stratified k-fold splits to preserve… [find](https://scholar.google.com/scholar?q=A+Newspaper+Okdiario+Marca+El+Mundo+El+Pa%C3%ADs+NTV+Espa%C3%B1a+The+Objective+Alerta+Digital+El+Debate+Vozp%C3%B3puli+El+Espa%C3%B1ol+ABC+El+Confidencial+El+Diario+Moncloa+Periodista+Digital+20+Minutos+%E2%80%A2+Normalization%3A+)
28. Evaluation metric: Macro-F1, chosen for its sensitivity to imbalanced and multi-class scenarios. • Reproducibility: Random seed fixed to 42. B.3 Optimal Model and Hyperparameters Across all tested algorithms, logistic regression consistently yielded the highest performance. The final configuration w… [find](https://scholar.google.com/scholar?q=Evaluation+metric%3A+Macro%2DF1%2C+chosen+for+its+sensitivity+to+imbalanced+and+multi%2Dclass+scenarios%2E+%E2%80%A2+Reproducibility%3A+Random+seed+fixed+to+42%2E+B%2E3+Optimal+Model+and+Hyperparameters+Across+all+tested+alg)

*Updated 1 November 2026*