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The widespread of offensive content online such as hate speech poses agrowing societal problem. AI tools are necessary for supporting the moderationprocess at online platforms. For the evaluation of these identification tools,continuous experimentation with data sets in different languages are necessary.The HASOC track (Hate Speech and Offensive Content Identification) is dedicatedto develop benchmark data for this purpose. This paper presents the HASOCsubtrack for English, Hindi, and Marathi. The data set was assembled fromTwitter. This subtrack has two sub-tasks. Task A is a binary classificationproblem (Hate and Not Offensive) offered for all three languages. Task B is afine-grained classification problem for three classes (HATE) Hate speech,OFFENSIVE and PROFANITY offered for English and Hindi. Overall, 652 runs weresubmitted by 65 teams. The performance of the best classification algorithmsfor task A are F1 measures 0.91, 0.78 and 0.83 for Marathi, Hindi and English,respectively. This overview presents the tasks and the data development as wellas the detailed results. The systems submitted to the competition applied avariety of technologies. The best performing algorithms were mainly variants oftransformer architectures.

Overview of the HASOC Subtrack at FIRE 2021: Hate Speech and Offensive Content Identification in English and Indo-Aryan Languages