TITLE: TEET! Tunisian Dataset for Toxic Speech Detection 

ABSTRACT: 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.
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.
TEET! Tunisian Dataset for Toxic Speech Detection
http://arxiv.org/abs/2110.05287v1