Large-Scale Hate Speech Detection with Cross-Domain Transfer
Çağrı Toraman, Furkan Şahi̇nuç, Eyüp Halit Yılmaz · 2022
The performance of hate speech detection models relies on the datasets on which the models are trained.Existing datasets are mostly prepared with a limited number of instances or hate domains that define hate topics.This hinders large-scale analysis and transfer learning with respect to hate domains.In this study, we construct large-scale tweet datasets for hate speech detection in English and a low-resource language, Turkish, consisting of human-labeled 100k tweets per each.Our datasets are designed to have equal number of tweets distributed over five domains.The experimental results supported by statistical tests show that Transformer-based language models outperform conventional bag-of-words and neural models by at least 5% in English and 10% in Turkish for large-scale hate speech detection.The performance is also scalable to different training sizes, such that 98% of performance in English, and 97% in Turkish, are recovered when 20% of training instances are used.We further examine the generalization ability of cross-domain transfer among hate domains.We show that 96% of the performance of a target domain in average is recovered by other domains for English, and 92% for Turkish.Gender and religion are more successful to generalize to other domains, while sports fail most.