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From Linguistics to Practice: a Case Study of Offensive Language Taxonomy in Hebrew

What it is about

The paper applies a Hebrew translation of the explicit-offence taxonomy of Lewandowska-Tomaszczyk et al. (2023) to Hebrew social-media text. Candidate offensive terms were found by co-occurrence (Dice coefficient) from 67 seed terms, and 683 were chosen by hand. Tweets were then extracted with those terms, and 450 texts (9 aspect files of 50 each) were annotated by two native speakers, with a third for disagreements. Cohen's kappa was 0.49 for target presence, 0.84 for target type, 0.73 for insult and hate speech severity (0.66 for discrediting, 0.97 for threat), 0.63 for vulgarity and 0.68 for the aspect list. Binary classifiers (XGBoost on AlephBERT sentence vectors) reach F1 0.699 for presence against a majority of 0.518. Vulgarity and severity are hard, and the multi-class aspect task gives accuracy 0.488 against a majority of 0.545. Fine-tuned multilingual BERT and Hebrew BERT do worse than the XGBoost model. The authors conclude that fixed taxonomies and word-based classes do not capture Hebrew offence well at the lower levels.

Ideas

For Mova

I1 is a rule design for prag. I2 goes into 8. Lexicon and register. I3 is the test plan for 9. Pragmatics and politeness. The offence aspects also overlap with the political and register work in 8. Lexicon and register.

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.18653/v1/2024.woah-1.8

@inproceedings{liebeskind2024linguistics,
  title = {{From Linguistics to Practice: a Case Study of Offensive Language Taxonomy in Hebrew}},
  author = {Liebeskind, Chaya and Litvak, Marina and Vanetik, Natalia},
  booktitle = {Proceedings of the 8th Workshop on Online Abuse and Harms (WOAH 2024)},
  year = {2024},
  doi = {10.18653/v1/2024.woah-1.8},
  url = {https://doi.org/10.18653/v1/2024.woah-1.8}
}

References

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