This articleintroduces a 3-class dataset named Pars-HAO, consisting of 8013 tweets, to fill the gap in existing research. We collected the datasetby combining comments from pages that are more exposed to hate speech and using a keyword-based approach. Three annotatorsthen labeled the tweets. In this study, we employed a combination of the Convolutional Neural Network (CNN) model and two widelyrecognized machine learning models, namely Support Vector Machine (SVM) and Logistic Regression (LR), as a baseline. To improvethe classification performance, we employed the Hard Voting ensemble learning technique. Experimental results on the Pars-HAOdataset demonstrated that the Hard voting ensemble learning technique yielded the best outcome, achieving a macro F1-score of68.76%. https://www.techrxiv.org/articles/preprint/Pars-HAO_Hate_Speech_and_Offensive_Language_Detection_on_Persian_Social_Media_Using_Ensemble_Learning/24106617 Share this: Click to print (Opens in new window) Print Click to share on Facebook (Opens in new window) Facebook Click to share on LinkedIn (Opens in new window) LinkedIn Click to share on Reddit (Opens in new window) Reddit Click to share on WhatsApp (Opens in new window) WhatsApp Click to share on Bluesky (Opens in new window) Bluesky Click to email a link to a friend (Opens in new window) Email Like this:Like Loading... Post navigation Lowenstein Clinic Proposes Framework to Moderate Indirect Hate Speech Online (Yale Law School) Free Resources on Countering Extremism and Hate Speech, September 2023 (II/II)