Identifying Hate Speech using Neural Networks and Discourse Analysis Techniques
1st Workshop on Language Technology and Resources for a Fair, Inclusive, and Safe Society, LATERAISSE 2022, Marseille, Fransa, 25 Haziran 2022, ss.32-41, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Basıldığı Şehir: Marseille
- Basıldığı Ülke: Fransa
- Sayfa Sayıları: ss.32-41
- Anahtar Kelimeler: bert, deep learning, hierarchical attention network, linguistic features
- Boğaziçi Üniversitesi Adresli: Evet
Özet
Discriminatory language, in particular hate speech, is a global problem posing a grave threat to democracy and human rights. Yet, it is not always easy to identify, as it is rarely explicit. In order to detect hate speech, we developed Hierarchical Attention Network (HAN) based and Bidirectional Encoder Representations from Transformer (BERT) based deep learning models to capture the changing discursive cues and understand the context around the discourse. In addition, we designed linguistic features using critical discourse analysis techniques and integrated them to the these neural network models. We studied the compatibility of our model with the hate speech detection problem by comparing it with traditional machine learning models, as well as a Convolution Neural Network (CNN) based model, a Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) based model which reached significant performance results for hate speech detection. Our results on a manually annotated corpus of print media in Turkish show that the proposed approach is effective for hate speech detection. We believe that the feature sets created for the Turkish language will encourage new studies in the quantitative analysis of hate speech.