Overview of the HASOC Subtracks at FIRE 2023: Detection of Hate Spans and Conversational Hate-Speech

Shrey Satapara, Sarah Masud, Hiren Madhu, Md. Aflah Khan, Md. Shad Akhtar, Tanmoy Chakraborty, Sandip Modha, Thomas Mandl

Abstract: The proliferation of hateful content on Social Media has drawn a lot of attention lately, which has prompted practitioners to develop systems that can recognize this kind of content automatically. The evaluation requires reliable benchmark data and evaluation techniques. This paper reports on two sub-tracks related to Hate Speech detection. The task ICHCL provided a dataset in code-mixed Hindi, which includes the conversational context of a tweet. In many cases, including the context is necessary for detecting the hatefulness of the tweet. The best submission reached an F1 measure of 0.8. The task HateNorm (Identification of Tokens Contributing to Explicit Hate in English by Span Detection) introduced a span annotated dataset and required systems to find a sequence of words relevant to the hatefulness of a post. The highest scoring system obtained a macro-F1 of 0.581.