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# Offensive Hebrew Corpus and Detection using BERT

**Authors:** Nagham Hamad; [Mustafa Jarrar](https://md.mova.elamur.ai/free/en/docs/papers/authors/mustafa-jarrar/); Mohammad Khalilia; Nadim Nashif · **Year:** 2023 · **Venue:** arXiv:2309.02724 **Link:** [arxiv.org/abs/2309.02724](https://arxiv.org/abs/2309.02724) · library `langstream-2023-hamad-offensive-hebrew-corpus-detection-using` · **Licence:** [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) (arXiv) — summary only

[**7** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/langstream-2023-hamad-offensive-hebrew-corpus-detection-using/) of the 140673 papers whose bibliographies we readarXiv:2309.02724[licence](http://creativecommons.org/licenses/by-nc-sa/4.0/)

- [Publisher's page](https://arxiv.org/abs/2309.02724)
- [PDF](https://arxiv.org/pdf/2309.02724)
- [arXiv 2309.02724](https://arxiv.org/abs/2309.02724)

**Authors (4)**

1. Nagham Hamad
2. Mustafa Jarrar
3. Mohammad Khalilia
4. Nadim Nashif

Citations and author metrics from OpenAlex (); authors with the same name are not always told apart.

## What it is about

The paper releases a Hebrew offensive-language corpus of 15,881 tweets, collected in late 2020 and early 2021 with 55 keywords and labelled by Arabic-Hebrew bilingual annotators. Each tweet gets one or more of five classes (abusive, hate, violence, pornographic, none), plus the target group, the topic and the offensive phrase. Only about 1,200 tweets are offensive, so the authors balance the set to 2,500 for experiments. They fine-tune HeBERT and AlephBERT as binary classifiers, alone and combined with an older Hebrew Facebook dataset. The data is not reviewed fully: a second annotator revised 21% of it, and the number of changes shows where the labels are unstable (target labels changed most). AlephBERT trained on the new data and tested on the Facebook set reaches 69% accuracy, and the reverse reaches 57%. Adding the new data to the older set helps HeBERT by about 2 points but hurts AlephBERT.

## Ideas

- I1. Report cross-dataset accuracy, not only in-domain accuracy. The 69% and 57% figures show that a label set does not transfer well between annotation guidelines. Any Mova offence or register detector should be tested on a second source before a claim is made.
- I2. Use the paper's annotation schema as a data format for pragmatic labels: a class, a target, a topic and the phrase that triggers the label. In `prag` this maps to a labelled span with a reason, which fits Mova's rule of explaining each decision.
- I3. Measure label instability by counting reviewer changes per label, as the paper does (targets changed most). A label that changes often needs a rule or a smaller label set before it is used as a gold standard.

## For Mova

I1 is a measurement rule for any register or offence work in [9. Pragmatics and politeness](https://md.mova.elamur.ai/free/en/docs/languages/he/09-pragmatics-politeness/). I2 is a data schema for `prag`, Hebrew side, and fits [8. Lexicon and register](https://md.mova.elamur.ai/free/en/docs/languages/he/08-lexicon-register/). I3 is a check for the labels in [12. Mova and Modern Hebrew: where it stands, what it would need](https://md.mova.elamur.ai/free/en/docs/languages/he/12-mova/) before they are used. No text from the corpus is carried into Mova: the licence is non-commercial, so it is only read for the method.

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [arXiv 2309.02724](https://arxiv.org/abs/2309.02724)

```
@misc{hamad2023offensive,
  title = {{Offensive Hebrew Corpus and Detection using BERT}},
  author = {Hamad, Nagham and Jarrar, Mustafa and Khalilia, Mohammad and Nashif, Nadim},
  year = {2023},
  eprint = {2309.02724},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2309.02724}
}
```

- [← previous Explicit Morphological Knowledge Improves Pre-training of Language Models for Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/langstream-2023-gueta-explicit-morphological-knowledge-improves-pre/)
- [next → ivrit.ai: A Comprehensive Dataset of Hebrew Speech for AI Research and Development](https://md.mova.elamur.ai/free/en/docs/papers/langstream-2023-marmor-ivrit-ai-comprehensive-dataset-hebrew/)
- [↑ back to Modern Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/he/)

## References

38 references, 3 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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