Multilingual and Multimodal Hate Speech Analysis in Twitter

Abstract

Automatic hate speech detection has become a crucial task nowadays, due the increase of hate on the Internet and its negative consequences. Therefore, in our PhD we propose the design and implementation of methods for the automatic processing of hate messages. The study is focused on the hate messages on Twitter. The hypothesis on which the research is based is that the prediction of hate speech, considering textual content, can be improved by the combination of features such as the activity and communities of users, as well as the images that can be shared with the tweets. In this way, we intend to develop strategies for the automatic detection of hate with multimodal and also multilingual (both in English and Spanish) approaches. Furthermore, our research includes the study of counter-narrative as an alternative to mitigate the effects of hate speech. To address the problem, we employ deep learning techniques, deepening the study of approaches based on representation with graphs.