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Self-Explaining Hate Speech Detection with Moral Rationales

Paper · 2026 · Francielle Vargas, Jackson Trager, Diego Alves et al.

Summary

Hate speech classifiers often depend on surface lexical cues. This makes them prone to spurious correlations, hard to interpret and weak with culturally contingent language. The authors argue that moral values, framed through Moral Foundations Theory, give a more stable signal than wording alone. They propose Supervised Moral Rationale Attention (SMRA), a self-explaining framework. During training, SMRA adds a loss that pushes the model's token-level attention toward spans that experts marked as moral rationales, so explanations come from the model itself rather than from post-hoc attribution.

To train and test SMRA, they release HateBRMoralXplain, the third version of the Brazilian Portuguese HateBR corpus. This version enriches the data with labels for moral foundations, word-level spans justifying those labels, and socio-political information about each comment.

On binary hate detection and multi-label moral classification, SMRA gives small predictive gains and clearly better agreement with human rationales, with no notable change in fairness. Tests with GPT-4o-mini and LLaMA-70B show that moral information in prompts helps hate detection, but LLMs remain weak at identifying moral categories. The work matters for building more transparent, culture-aware moderation tools, especially for lower-resource languages.

Key points

  • HateBRMoralXplain corpus:
    • 7,000 Instagram comments from public political accounts, balanced at 3,500 offensive and 3,500 non-offensive.
    • Hate labels were annotated by three experts.
    • Each comment receives one to three of the five MFT foundations (Care/Harm, Fairness/Cheating, Loyalty/Betrayal, Authority/Subversion, Purity/Degradation), ranked by salience, with rationales for each.
  • Binary task results: SMRA raises Macro F1 by +0.0082 and improves rationale alignment, with IoU F1 up by +0.0743 and Token F1 up by +0.0503. Comprehensiveness falls by 0.0203 and Sufficiency by 0.0231.
  • Multi-label moral task results: Macro F1 rises by +0.015 and Token F1 by +0.241. Fairness metrics stay roughly unchanged.
  • LLM experiments (GPT-4o-mini, LLaMA-70B):
    • Adding moral rationales to prompts improves hate detection by about 2–3%.
    • Moral sentiment F1 stays below 0.40 for both Portuguese and English translations.
    • LLM-generated rationales show a Jaccard similarity of only 0.11 with human rationales.
  • Release and limitations: Data, per-annotator disagreements and code are available at github.com/franciellevargas/SMRA under CC BY-NC 4.0. The authors note that rationale annotation is expensive and that evaluation is so far limited to Brazilian Portuguese.

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

🏢GoogleMountain View · United States430 works · 424,363 citations🎓University of Southern CaliforniaLos Angeles · United States162 works · 35,836 citations🎓Saarland UniversitySaarbrücken · Germany148 works · 18,287 citations🎓The University of MelbourneMelbourne · Australia100 works · 22,618 citations🎓Technical University of MunichMunich · Germany55 works · 48,474 citations🎓Virginia TechBlacksburg · United States36 works · 43,036 citations🎓Portland State UniversityPortland · United States6 works · 53 citations🎓University of ChileSantiago · Chile2 works · 2,016 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksSocial Sciences · 1,772 worksSafety Research · 543 works

🧭 Topics 2Research subjects and languages the work is about.Sentiment & emotion · 972 worksBias, fairness & safety · 517 works

Cite

Cite as Francielle Vargas, Jackson Trager, Diego Alves, Matteo Guida, Surendrabikram Thapa, Berk Atıl, Daryna Dementieva, Andrew J Smart and Ameeta Agrawal. 2026. Self-Explaining Hate Speech Detection with Moral Rationales. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-acl.1704

@inproceedings{vargas2026self,
  title = {{Self-Explaining Hate Speech Detection with Moral Rationales}},
  author = {Francielle Vargas, Jackson Trager, Diego Alves, Matteo Guida, Surendrabikram Thapa, Berk Atıl, Daryna Dementieva, Andrew J Smart and Ameeta Agrawal},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.1704},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.1704}
}

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