Abstract
Work on bias in hate speech typically aims to improve classification performance while relatively overlooking the quality of the data. We examine selection bias in hate speech in a language and label independent fashion. We first use topic models to discover latent semantics in eleven hate speech corpora, then, we present two bias evaluation metrics based on the semantic similarity between topics and search words frequently used to build corpora. We discuss the possibility of revising the data collection process by comparing datasets and analyzing contrastive case studies.- Anthology ID:
- 2020.emnlp-main.199
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2532–2542
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.199
- DOI:
- 10.18653/v1/2020.emnlp-main.199
- Cite (ACL):
- Nedjma Ousidhoum, Yangqiu Song, and Dit-Yan Yeung. 2020. Comparative Evaluation of Label-Agnostic Selection Bias in Multilingual Hate Speech Datasets. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 2532–2542, Online. Association for Computational Linguistics.
- Cite (Informal):
- Comparative Evaluation of Label-Agnostic Selection Bias in Multilingual Hate Speech Datasets (Ousidhoum et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2020.emnlp-main.199.pdf