Abstract
Offensive language detection is crucial in natural language processing (NLP). We investigated the importance of context for detecting such language in reply tweets on Twitter, where the use of offensive language is widespread. We collected a Turkish tweet dataset where the target group was unvaccinated people during the Covid period. Tweets in the dataset were enriched with contextual information by adding the original tweet to which a particular tweet was posted as a reply. The dataset, which includes over 28,000 tweet-reply pairs, was manually labeled by human annotators and made publicly available. In addition, we compared the performance of different machine learning models with and without contextual information. Our results show that this type of contextual information was not very useful in improving the performance of the models in general, although it slightly increased the macro-averaged F1-score of certain models.- Anthology ID:
- 2023.findings-emnlp.103
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1543–1549
- Language:
- URL:
- https://aclanthology.org/2023.findings-emnlp.103
- DOI:
- 10.18653/v1/2023.findings-emnlp.103
- Cite (ACL):
- Musa İhtiyar, Ömer Özdemir, Mustafa Erengül, and Arzucan Özgür. 2023. A Dataset for Investigating the Impact of Context for Offensive Language Detection in Tweets. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 1543–1549, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- A Dataset for Investigating the Impact of Context for Offensive Language Detection in Tweets (İhtiyar et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2023.findings-emnlp.103.pdf