  With the growth of social media, the spread of hate speech is also increasing
rapidly. Social media are widely used in many countries. Also Hate Speech is
spreading in these countries. This brings a need for multilingual Hate Speech
detection algorithms. Much research in this area is dedicated to English at the
moment. The HASOC track intends to provide a platform to develop and optimize
Hate Speech detection algorithms for Hindi, German and English. The dataset is
collected from a Twitter archive and pre-classified by a machine learning
system. HASOC has two sub-task for all three languages: task A is a binary
classification problem (Hate and Not Offensive) while task B is a fine-grained
classification problem for three classes (HATE) Hate speech, OFFENSIVE and
PROFANITY. Overall, 252 runs were submitted by 40 teams. The performance of the
best classification algorithms for task A are F1 measures of 0.51, 0.53 and
0.52 for English, Hindi, and German, respectively. For task B, the best
classification algorithms achieved F1 measures of 0.26, 0.33 and 0.29 for
English, Hindi, and German, respectively. This article presents the tasks and
the data development as well as the results. The best performing algorithms
were mainly variants of the transformer architecture BERT. However, also other
systems were applied with good success
