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

TL;DR

This paper presents a simple, language-agnostic baseline system for hate speech and offensive content detection using character n-grams, outperforming complex deep learning models in less-resourced languages like Hindi and Marathi.

Contribution

The paper introduces a classical supervised approach based solely on character n-grams that achieves strong, language-agnostic performance, especially in low-resource languages, serving as a benchmark for future methods.

Findings

Outperforms many deep learning models in Hindi and Marathi.

Achieves medium performance in English, the resource-rich language.

Provides a strong baseline for evaluating advanced approaches.

Abstract

For automatically identifying hate speech and offensive content in tweets, a system based on a classical supervised algorithm only fed with character n-grams, and thus completely language-agnostic, is proposed by the SATLab team. After its optimization in terms of the feature weighting and the classifier parameters, it reached, in the multilingual HASOC 2021 challenge, a medium performance level in English, the language for which it is easy to develop deep learning approaches relying on many external linguistic resources, but a far better level for the two less resourced language, Hindi and Marathi. It ends even first when performances are averaged over the three tasks in these languages, outperforming many deep learning approaches. These performances suggest that it is an interesting reference level to evaluate the benefits of using more complex approaches such as deep learning or taking into account complementary resources.