Autor(es)

Juan Manuel Pérez and Franco M Luque and Demian Zayat and Martín Kondratzky and Agustín Moro and Pablo Santiago Serrati and Joaquín Zajac and Paula Miguel and Natalia Debandi and Agustín Gravano and Viviana Cotik

URL Pública

Abstract

Social networks and other digital media deal with huge amounts of user-generated contents where hate speech has become a problematic more and more relevant. A great effort has been made to develop automatic tools for its analysis and moderation, at least in its most threatening forms, such as in violent acts against people and groups protected by law. One limitation of current approaches to automatic hate speech detection is the lack of context. The spotlight on isolated messages, without considering any type of conversational context or even the topic being discussed, severely restricts the available information to determine whether a post on a social network should be tagged as hateful or not. In this work, we assess the impact of adding contextual information to the hate speech detection task. We specifically study a subdomain of Twitter data consisting of replies to digital newspapers posts, which provides a …