We show that Transformer-based language models outperform bag-of-words and deep learning models in gender identity-based hate speech detection for both English and Turkish. Second, we find that gender identity-based hate speech detection can be improved by removing the gender bias in neural embeddings since gender-biased words can have offensive or hateful implications.
The paper can be accessed here.
Hate speech against individuals or communities with different backgrounds is a major problem in online social networks. The domain of hate speech has spread to various topics, including race, religion, and gender. Although there are many efforts for hate speech detection in different domains and languages, the effects of gender identity are not solely examined in hate speech detection. Moreover, hate speech detection is mostly studied for particular languages, specifically English, but not low-resource languages, such as Turkish. We examine gender identity-based hate speech detection for both English and Turkish tweets. We compare the performances of state-of-the-art models using 20 k tweets per language. We observe that transformer-based language models outperform bag-of-words and deep learning models, while the conventional bag-of-words model has surprising performances, possibly due to offensive or hate-related keywords. Furthermore, we analyze the effect of debiased embeddings for hate speech detection. We find that the performance can be improved by removing the gender-related bias in neural embeddings since gender-biased words can have offensive or hateful implications.