Pars-HaO: Hate and Offensive Language Detection on Persian Tweets Using Machine Learning and Deep Learning

Abstract

<p>As social networks continue to gain widespread popularity, an urgent requirement arises to automatically identify and detect offensive language and hate speech. While there is a wealth of research and datasets available for English in this domain, there is currently a scarcity of research and datasets focused on identifying hate speech and offensive language in Persian text. This article introduces a 3-class dataset named Pars-HAO, consisting of 8013 tweets, to fill the gap in existing research. We collected the dataset by combining comments from pages that are more exposed to hate speech and using a keyword-based approach. Three annotators then labeled the tweets. In this study, we employed a combination of the Convolutional Neural Network (CNN) model and two widely recognized machine learning models, namely Support Vector Machine (SVM) and Logistic Regression (LR), as a baseline. To improve the classification performance, we employed the Hard Voting ensemble learning technique. Experimental results on the Pars-HAO dataset demonstrated that the Hard voting ensemble learning technique yielded the best outcome, achieving a macro F1-score of 68.76%.</p>