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Pars-HAO: Hate Speech and Offensive Language Detection on Persian Social Media Using Ensemble Learning
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  • Mohammad Karami Sheykhlan ,
  • Jana Shafi ,
  • Saeed Kosari ,
  • Saleh Kheiri Abdoljabbar ,
  • Jaber Karimpour
Mohammad Karami Sheykhlan
University of Mohaghegh Ardabili, University of Mohaghegh Ardabili, University of Mohaghegh Ardabili

Corresponding Author:[email protected]

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Jana Shafi
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Saeed Kosari
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Saleh Kheiri Abdoljabbar
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Jaber Karimpour
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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%.