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SOS: Systematic Offensive Stereotyping Bias in Word Embeddings

Elsafoury, Fatma; Wilson, Steven R.; Katsigiannis, Stamos; Ramzan, Naeem

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Fatma Elsafoury

Steven R. Wilson

Naeem Ramzan


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.


Elsafoury, F., Wilson, S. R., Katsigiannis, S., & Ramzan, N. (2022). SOS: Systematic Offensive Stereotyping Bias in Word Embeddings.

Conference Name 29th International Conference on Computational Linguistics (COLING 2022)
Conference Location Gyeongju, Republic of Korea
Start Date Oct 12, 2022
End Date Oct 17, 2022
Acceptance Date Aug 16, 2022
Publication Date 2022-10
Deposit Date Aug 19, 2022
Publicly Available Date Apr 28, 2023
Pages 1263-1274
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Published Conference Proceeding (408 Kb)

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