Exploiting contextualized word representations to profile haters on Twitter

In this paper, we present our submission to the Profiling Haters on Twitter shared task at PAN@CLEF2021. The task aims at analyzing Twitter feeds of users in two languages, English and Spanish, in order to determine whether these users spread hate speech on social media. For English, we propose an approach which exploits contextualized word embeddings and a statistical feature extraction method, in order to find words which are used in different contexts by haters and non-haters, and we use these words as features to train a classifier. For Spanish, on the other hand, we take advantage of BERT sequence representations, using the average of the sequence representations of all tweets from a user as a feature to train a model for classifying users into haters and non-haters.