HaMor at the Profiling Hate Speech Spreaders on Twitter

In this paper we describe the Hate and Morality (HaMor) submission for the Profiling Hate Speech Spreaders on Twitter task at PAN 2021. We ranked as the 19th position - over 66 participating teams - according to the averaged accuracy value of 73% reached by our proposed models over the two languages. We obtained the 43th higher accuracy for English (62%) and the 2nd higher accuracy for Spanish (84%). We proposed four types of features for inferring users attitudes just from the text in their messages: HS detection, users morality, named entities, and communicative behaviour. The results of our experiments are promising and will lead to future investigations of these features in a finer grained perspective.