---
title: "Ara-Women-Hate — paper by Guellil et al. (2022)"
description: "The authors build a new YouTube corpus, called Arabic_fr_en. 86% F1. 61 references, cited by 4 papers here."
authors: ["Imane Guellil", "Ahsan Adeel", "Faical Azouaou", "Mohamed Boubred", "Yousra Houichi", "Akram Abdelhaq Moumna"]
year: 2022
venue: "Proceedings of the Workshop on Dataset Creation for Lower-Resourced Languages within the 13th Language Resources and Evaluation Conference"
source: "https://aclanthology.org/2022.dclrl-1.9/"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/guellil-2022-ara-women-hate-annotated-corpus/"
---
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# Ara-Women-Hate: An Annotated Corpus Dedicated to Hate Speech Detection against Women in the Arabic Community

**Authors:** [Imane Guellil](https://md.research.elamur.ai/free/en/authors/imane-guellil/), [Ahsan Adeel](https://md.research.elamur.ai/free/en/authors/ahsan-adeel/), [Faical Azouaou](https://md.research.elamur.ai/free/en/authors/faical-azouaou/), [Mohamed Boubred](https://md.research.elamur.ai/free/en/authors/mohamed-boubred/), [Yousra Houichi](https://md.research.elamur.ai/free/en/authors/yousra-houichi/) and [Akram Abdelhaq Moumna](https://md.research.elamur.ai/free/en/authors/akram-abdelhaq-moumna/) · **Year:** 2022 · **Venue:** Proceedings of the Workshop on Dataset Creation for Lower-Resourced Languages within the 13th Language Resources and Evaluation Conference · **Publisher:** [European Language Resources Association (ELRA)](https://md.research.elamur.ai/free/en/publishers/european-language-resources-association-elra/) · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2022.dclrl-1.9/) · [PDF](https://aclanthology.org/2022.dclrl-1.9.pdf)

**Citations:** [4](https://md.research.elamur.ai/free/en/papers/guellil-2022-ara-women-hate-annotated-corpus/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
The paper addresses the lack of Arabic resources for detecting online hate directed at women. Most earlier work on sexism and misogyny covers English, and the few Arabic hate speech studies mostly use Twitter. The authors build a new YouTube corpus, called Arabic_fr_en. They found videos about women by searching for feminine adjectives, some flattering and some insulting, and then collected the comments under those videos. The resulting 5,000 comments were labeled manually by three annotators. The text reflects real Arabic online usage: Modern Standard Arabic and Algerian dialect, written in Arabic script or in Arabizi (Latin letters plus digits), with French and English mixed in. To check that the corpus is usable, they train three deep learning classifiers (CNN, LSTM and Bi-LSTM) on word embedding features such as word2vec and FastText. The CNN performs best, reaching an F1-score of up to 86% on the unbalanced corpus. The resource matters because it gives a starting point for misogyny detection in a multi-script, code-switched, lower-resourced setting.

## Key points
- The corpus has 5,000 manually annotated YouTube comments, each labeled by three annotators.
- It covers MSA and Algerian dialect, written in Arabic script and Arabizi (e.g. "rani fer7ana", meaning "I am happy"), plus French and English. Code switching is common: Maghreb users tend to mix in French and Mashreq users English.
- Collection was seeded by feminine adjectives such as "beautiful", "stupid" and "dog" to find videos about women.
- Three classifiers were tested (CNN, LSTM, Bi-LSTM), with word2vec and FastText features. The CNN scored best, at up to 86% F1 on the unbalanced data.
- The authors count only about 5 earlier Arabic hate speech studies, mostly on Twitter. They argue for adding political hate speech to the gendered, religious, racist and disability taxonomy of Chetty and Alathur.

## Interesting ideas
- The authors seeded collection with evaluative adjectives about the target group, both positive and negative, so the corpus contains hateful and benign comments on the same topic rather than only obvious abuse.
- They kept code-switched and Latin-script dialect text in the corpus instead of filtering it out, treating mixed scripts as a property of the real data.
- The hate speech definition they adopt from Nockleby says a message disparages a person or group because of a characteristic, which implies a structure of speaker, target and attribute.
- They audited an existing taxonomy for missing classes and proposed political hate speech as a gap.
- They validated the new corpus by training several different model families on it and comparing them, rather than relying on a single model.

## For Mova

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

- **I1** (learn): Evaluation sets for insult or irony could be seeded the same way, pairing praise and abuse adjectives about one target, so that negative controls share topic words with the positives.
- **I2** (tokens): The English tokenizer and tagger could explicitly flag foreign-language or letter-digit tokens like "fer7ana" as out-of-language spans, instead of silently guessing a POS for them.
- **I3** (frames): Abuse detection in prag could be modeled as a disparagement frame with explicit target and attribute roles, so a verdict is explained by the tree rather than by keyword counts.
- **I4** (world): Category inventories compiled from WordNet/FrameNet could be checked periodically for classes the data needs but the ontology lacks.
- **I5** (learn): A new silver treebank could be validated by training both the perceptron parser and gparse on it and comparing results, alongside a negative control.

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

📚 Shelves (2): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works

🧭 Topics (3): [😊 Sentiment & emotion](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/) · 972 works · [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/) · 2,908 works · [🛡️ Bias, fairness & safety](https://md.research.elamur.ai/free/en/topics/bias-fairness-safety/) · 517 works

## Cite

Cite as: Imane Guellil, Ahsan Adeel, Faical Azouaou, Mohamed Boubred, Yousra Houichi and Akram Abdelhaq Moumna. 2022. Ara-Women-Hate: An Annotated Corpus Dedicated to Hate Speech Detection against Women in the Arabic Community. Proceedings of the Workshop on Dataset Creation for Lower-Resourced Languages within the 13th Language Resources and Evaluation Conference.

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

```bibtex
@inproceedings{guellil2022ara,
  title = {{Ara-Women-Hate: An Annotated Corpus Dedicated to Hate Speech Detection against Women in the Arabic Community}},
  author = {Imane Guellil, Ahsan Adeel, Faical Azouaou, Mohamed Boubred, Yousra Houichi and Akram Abdelhaq Moumna},
  booktitle = {Proceedings of the Workshop on Dataset Creation for Lower-Resourced Languages within the 13th Language Resources and Evaluation Conference},
  year = {2022},
  url = {https://aclanthology.org/2022.dclrl-1.9/}
}
```

- [← previous Alternatives and attention in language and reasoning: A reply to Mascarenhas & Picat 2019](https://md.research.elamur.ai/free/en/papers/bade-2022-alternatives-attention-language-reasoning-reply/)
- [next → AraBEM at WANLP 2022 Shared Task: Propaganda Detection in Arabic Tweets](https://md.research.elamur.ai/free/en/papers/refaee-2022-arabem-wanlp-2022-shared-task/)
- [↑ back to papers of 2022](https://md.research.elamur.ai/free/en/papers/2022/page/7/)

## References

61 references, 18 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 1 November 2026*