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Profiling Haters on Twitter using Statistical and Contextualized Embeddings

Hate Speech (HS) in social media such as Twitter is a complex phenomenon that attracted a significant body of research in the NLP. HS Spreaders (haters) aim to spread HS via social media. In this task, we aim to identify such haters. On one hand, our proposed class-dependent LDSE representation is fed to a linear SVM classifier to identify the haters based on general commonalities. On the other hand, stylistic features of individuals are captured by using extractive summarization of the tweets in conjunction with RoBERTa embedding before classifying them using another linear SVM classifier. Experimental results expressed as accuracies 0.67 and 0.80 over English and Spanish test sets respectively show efficacy of our approach in identifying the haters across different languages.