  The proliferation of hate speech and offensive comments on social media has
become increasingly prevalent due to user activities. Such comments can have
detrimental effects on individuals' psychological well-being and social
behavior. While numerous datasets in the English language exist in this domain,
few equivalent resources are available for Persian language. To address this
gap, this paper introduces two offensive datasets. The first dataset comprises
annotations provided by domain experts, while the second consists of a large
collection of unlabeled data obtained through web crawling for unsupervised
learning purposes. To ensure the quality of the former dataset, a meticulous
three-stage labeling process was conducted, and kappa measures were computed to
assess inter-annotator agreement. Furthermore, experiments were performed on
the dataset using state-of-the-art language models, both with and without
employing masked language modeling techniques, as well as machine learning
algorithms, in order to establish the baselines for the dataset using
contemporary cutting-edge approaches. The obtained F1-scores for the
three-class and two-class versions of the dataset were 76.9% and 89.9% for
XLM-RoBERTa, respectively.
