Large-Scale Hate Speech Detection with Cross-Domain Transfer
share
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 English and 10 performance is also scalable to different training sizes, such that 98 performance in English, and 97 instances are used. We further examine the generalization ability of cross-domain transfer among hate domains. We show that 96 of a target domain in average is recovered by other domains for English, and 92 domains, while sports fail most.
READ FULL TEXT