---
title: "Hate Speech Detection and Racial Bias Mitigation in Social"
description: "The authors build on their earlier BERT transfer-learning classifier for hateful and offensive content… 41 references, cited by 52 papers here."
authors: ["Marzieh Mozafari", "Reza Farahbakhsh", "Noel Crespi"]
year: 2020
venue: "arXiv:2008.06460"
source: "https://arxiv.org/abs/2008.06460"
doi: "10.1371/journal.pone.0237861"
arxiv: "2008.06460"
licence: "arXiv — non-exclusive license to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/mozafari-2020-hate-speech-detection-racial-bias/"
---
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# Hate Speech Detection and Racial Bias Mitigation in Social Media based on BERT model

**Authors:** Marzieh Mozafari, Reza Farahbakhsh and Noel Crespi · **Year:** 2020 · **Venue:** [arXiv:2008.06460](https://md.research.elamur.ai/free/en/venues/plos-one/) ([PLoS ONE 2020](https://md.research.elamur.ai/free/en/venues/plos-one/2020/)) · **Publisher:** [Public Library of Science](https://md.research.elamur.ai/free/en/publishers/public-library-of-science/) · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://doi.org/10.1371/journal.pone.0237861) · [PDF](https://arxiv.org/pdf/2008.06460) · [DOI 10.1371/journal.pone.0237861](https://doi.org/10.1371/journal.pone.0237861) · [arXiv 2008.06460](https://arxiv.org/abs/2008.06460) · [OpenAlex](https://openalex.org/W3049565363)

**Citations:** 253 citations (OpenAlex, 2026-10-14) · [52](https://md.research.elamur.ai/free/en/papers/mozafari-2020-hate-speech-detection-racial-bias/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Hate speech classifiers trained on Twitter corpora can learn shortcuts. Some n-grams co-occur strongly with negative labels, and many of them are everyday vocabulary in African-American English (AAE). The authors build on their earlier BERT transfer-learning classifier for hateful and offensive content and test it on two public datasets: Waseem (racism/sexism/neither) and Davidson (hate/offensive/neither). To reduce bias, they borrow a reweighting regularizer that Schuster et al. designed for debiasing fact verification. Training samples dominated by n-grams tightly linked to one class receive adjusted weights, and these weights enter the classification loss during fine-tuning. Bias is measured cross-domain. The trained classifiers label tweets from an AAE-aligned group and a White-aligned group (Standard American English, SAE), and the authors compare how often each group lands in negative classes. Without reweighting, AAE tweets are pushed into racism, sexism, hate or offensive labels clearly more often. With reweighting, that gap shrinks substantially. The contribution matters because it targets bias created by the trained model, not only bias in the collected data, and the fix is simple and independent of the model.

## Key points
- Base model: pre-trained BERT fine-tuned with supervised strategies from the authors' earlier work. Reweighted samples enter through a modified loss function.
- Training data:
  - Waseem et al.: 16,849 tweets labelled Racism / Sexism / Neither.
  - Davidson et al.: about 24K tweets labelled Hate / Offensive / Neither.
- Debiasing: the sample-reweighting regularizer of Schuster et al. lowers the influence of training-set n-grams that correlate highly with class labels.
- Bias test set: AAE-aligned versus White-aligned (SAE) tweets, used purely for cross-domain prediction. In the AAE-aligned set, tweets containing "n*gga" are about thirty times more frequent and those containing "b*tch" about five times more frequent than in the SAE set.
- Analysis tools:
  - Top-20 unigram and 2-gram lists per dialect group (Table 6).
  - PCA plots of 768-dimensional sentence embeddings from all 12 BERT layers, before and after fine-tuning, extracted with bert-as-service.

## Interesting ideas
- Weighting each training example by how predictable its label is from surface n-grams alone discourages a model from leaning on lexical shortcuts; this works with any classifier.
- Bias is measured by running a trained model on two groups of texts that differ mainly in dialect and comparing the rates of negative labels; this is a clean probe needing no new gold annotation.
- The paper stresses that a word can be insulting from outsiders but neutral inside the speaker's own community, so offensiveness depends on who speaks to whom.
- Projecting each transformer layer's sentence embeddings with PCA before and after fine-tuning shows where task information concentrates across the stack.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (learn): when training on silver data agreed by witness parsers, downweight sentences whose frame or label could be guessed from a few cue words, so learned repair rules and classifiers rely on structure instead.
- **I2** (learn): add dialect-paired or style-paired text sets as negative controls, checking that tree, frame and answer outputs do not change when only the surface variety changes.
- **I3** (frames): speech-act and insult or irony judgments in prag should be conditioned on speaker and addressee identity stored in the frame, not triggered by the lexical item alone.
- **I4** (tree): apply the same layer-by-layer inspection to gparse to choose which layers to feed the dependency scorer and to see what fine-tuning on treebanks changes.

*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

- [Centre National de la Recherche Scientifique](https://md.research.elamur.ai/free/en/labs/centre-national-de-la-recherche-scientifique/) — Research institute, Paris, France · 104 works · 60,877 citations
- [Institut Polytechnique de Paris](https://md.research.elamur.ai/free/en/universities/institut-polytechnique-de-paris/) — University, Palaiseau, France · 10 works · 1,555 citations

Also on the byline: [Télécom SudParis](https://ror.org/05xvk4r52).

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🧭 Topics (2): [😊 Sentiment & emotion](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/) · 609 works · [🛡️ Bias, fairness & safety](https://md.research.elamur.ai/free/en/topics/bias-fairness-safety/) · 209 works

## Cite

Cite as: Marzieh Mozafari, Reza Farahbakhsh and Noel Crespi. 2020. Hate Speech Detection and Racial Bias Mitigation in Social Media based on BERT model. arXiv:2008.06460.

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

```bibtex
@misc{mozafari2020hate,
  title = {{Hate Speech Detection and Racial Bias Mitigation in Social Media based on BERT model}},
  author = {Marzieh Mozafari, Reza Farahbakhsh and Noel Crespi},
  year = {2020},
  doi = {10.1371/journal.pone.0237861},
  eprint = {2008.06460},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.1371/journal.pone.0237861}
}
```

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## References

41 references, 17 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 24 October 2026*