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For automatically identifying hate speech and offensive content in tweets, asystem based on a classical supervised algorithm only fed with charactern-grams, and thus completely language-agnostic, is proposed by the SATLab team.After its optimization in terms of the feature weighting and the classifierparameters, it reached, in the multilingual HASOC 2021 challenge, a mediumperformance level in English, the language for which it is easy to develop deeplearning approaches relying on many external linguistic resources, but a farbetter level for the two less resourced language, Hindi and Marathi. It endseven first when performances are averaged over the three tasks in theselanguages, outperforming many deep learning approaches. These performancessuggest that it is an interesting reference level to evaluate the benefits ofusing more complex approaches such as deep learning or taking into accountcomplementary resources.

A simple language-agnostic yet very strong baseline system for hate speech and offensive content identification