Multilingual Twitter Corpus and Baselines for Evaluating Demographic Bias in Hate Speech Recognition
Xiaolei Huang, Linzi Xing, Franck Dernoncourt, Michael J. Paul
Abstract
Existing research on fairness evaluation of document classification models mainly uses synthetic monolingual data without ground truth for author demographic attributes. In this work, we assemble and publish a multilingual Twitter corpus for the task of hate speech detection with inferred four author demographic factors: age, country, gender and race/ethnicity. The corpus covers five languages: English, Italian, Polish, Portuguese and Spanish. We evaluate the inferred demographic labels with a crowdsourcing platform, Figure Eight. To examine factors that can cause biases, we take an empirical analysis of demographic predictability on the English corpus. We measure the performance of four popular document classifiers and evaluate the fairness and bias of the baseline classifiers on the author-level demographic attributes.- Anthology ID:
- 2020.lrec-1.180
- Volume:
- Proceedings of the Twelfth Language Resources and Evaluation Conference
- Month:
- May
- Year:
- 2020
- Address:
- Marseille, France
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 1440–1448
- Language:
- English
- URL:
- https://aclanthology.org/2020.lrec-1.180
- DOI:
- Cite (ACL):
- Xiaolei Huang, Linzi Xing, Franck Dernoncourt, and Michael J. Paul. 2020. Multilingual Twitter Corpus and Baselines for Evaluating Demographic Bias in Hate Speech Recognition. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 1440–1448, Marseille, France. European Language Resources Association.
- Cite (Informal):
- Multilingual Twitter Corpus and Baselines for Evaluating Demographic Bias in Hate Speech Recognition (Huang et al., LREC 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.lrec-1.180.pdf
- Code
- xiaoleihuang/Multilingual_Fairness_LREC + additional community code