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In recent years, the spread of online offensive content has become of great concern, motivating researchers to develop robust systems capable of identifying such content automatically. To carry out a fair evaluation of these systems, several international shared tasks have been organized, providing the community with essential benchmark data and evaluation methods for various languages. Organized since 2019, the HASOC (Hate Speech and Offensive Content Identification) shared task is one of these initiatives. In its fourth iteration, HASOC 2022 included three tasks for English-Hindi codemix, German and Marathi. Tasks 1 and 2 were on conversational hate speech detection. The idea is to detect supporting hate speech, profanity, or other forms of offensiveness depending on the surrounding context of Twitter posts. Task 1 was offered in Hindi-English codemix and German. Task 2 was provided for Hindi-English codemix, and it was focused on further classifying the problematic tweets in conversational hate speech into standalone and contextual hate. This paper presents a brief description of tasks, data, and participation.
Satapara et al. (Fri,) studied this question.
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