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With the growth of social media, the spread of hate speech is also increasingrapidly. Social media are widely used in many countries. Also Hate Speech isspreading in these countries. This brings a need for multilingual Hate Speechdetection algorithms. Much research in this area is dedicated to English at themoment. The HASOC track intends to provide a platform to develop and optimizeHate Speech detection algorithms for Hindi, German and English. The dataset iscollected from a Twitter archive and pre-classified by a machine learningsystem. HASOC has two sub-task for all three languages: task A is a binaryclassification problem (Hate and Not Offensive) while task B is a fine-grainedclassification problem for three classes (HATE) Hate speech, OFFENSIVE andPROFANITY. Overall, 252 runs were submitted by 40 teams. The performance of thebest classification algorithms for task A are F1 measures of 0.51, 0.53 and0.52 for English, Hindi, and German, respectively. For task B, the bestclassification algorithms achieved F1 measures of 0.26, 0.33 and 0.29 forEnglish, Hindi, and German, respectively. This article presents the tasks andthe data development as well as the results. The best performing algorithmswere mainly variants of the transformer architecture BERT. However, also othersystems were applied with good success

Overview of the HASOC track at FIRE 2020: Hate Speech and Offensive Content Identification in Indo-European Languages