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 12th 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:
Bibkey:
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 12th 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)
Copy Citation:
PDF:
https://aclanthology.org/2020.lrec-1.180.pdf
Code
 xiaoleihuang/Multilingual_Fairness_LREC +  additional community code