SOS: Systematic Offensive Stereotyping Bias in Word Embeddings

Fatma Elsafoury, Steve R. Wilson, Stamos Katsigiannis, Naeem Ramzan


Abstract
Systematic Offensive stereotyping (SOS) in word embeddings could lead to associating marginalised groups with hate speech and profanity, which might lead to blocking and silencing those groups, especially on social media platforms. In this [id=stk]work, we introduce a quantitative measure of the SOS bias, [id=stk]validate it in the most commonly used word embeddings, and investigate if it explains the performance of different word embeddings on the task of hate speech detection. Results show that SOS bias exists in almost all examined word embeddings and that [id=stk]the proposed SOS bias metric correlates positively with the statistics of published surveys on online extremism. We also show that the [id=stk]proposed metric reveals distinct information [id=stk]compared to established social bias metrics. However, we do not find evidence that SOS bias explains the performance of hate speech detection models based on the different word embeddings.
Anthology ID:
2022.coling-1.108
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Editors:
Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
1263–1274
Language:
URL:
https://aclanthology.org/2022.coling-1.108
DOI:
Bibkey:
Cite (ACL):
Fatma Elsafoury, Steve R. Wilson, Stamos Katsigiannis, and Naeem Ramzan. 2022. SOS: Systematic Offensive Stereotyping Bias in Word Embeddings. In Proceedings of the 29th International Conference on Computational Linguistics, pages 1263–1274, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
SOS: Systematic Offensive Stereotyping Bias in Word Embeddings (Elsafoury et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.108.pdf