TEET! Tunisian Dataset for Toxic Speech Detection
This addresses the lack of large annotated datasets for NLP in the Tunisian dialect, which is incremental as it builds on existing methods for toxic speech detection.
The authors tackled the problem of toxic speech detection in the Tunisian dialect by creating a new annotated dataset of approximately 10k comments and evaluating it with machine learning and deep learning models.
The complete freedom of expression in social media has its costs especially in spreading harmful and abusive content that may induce people to act accordingly. Therefore, the need of detecting automatically such a content becomes an urgent task that will help and enhance the efficiency in limiting this toxic spread. Compared to other Arabic dialects which are mostly based on MSA, the Tunisian dialect is a combination of many other languages like MSA, Tamazight, Italian and French. Because of its rich language, dealing with NLP problems can be challenging due to the lack of large annotated datasets. In this paper we are introducing a new annotated dataset composed of approximately 10k of comments. We provide an in-depth exploration of its vocabulary through feature engineering approaches as well as the results of the classification performance of machine learning classifiers like NB and SVM and deep learning models such as ARBERT, MARBERT and XLM-R.