Detecting Hate speech in Arabic Literahire Tweets

Middle Eastern people are one of the largest populations on Twitter. In recent years, more Arabic writers, especially young ones, have used Twitter to publish their literary works to a wider audience. This research focused on two literary genres: prose and poetry, to detect hate speech in Arabic literary texts that were published in Twitter platform. Arabic tweets wm be scraped, pre-processed, and classified into hate or non-hate tweets using five different machine learning algorithms; namely: Support Vector Machines, Naive Bayes, Random Forest, Gradient Boosted Decision Trees, and Extra Tree Classmer in three different scenarios: unbalance, undersampling data, and over-sampling data. We compare the performance of these algorithms based on four evaluation metrics; namely: accuracy, precision, recall, and Fl-score. Our results show that the RF algorithm produces 95.45% accuracy, 98.63% precision, 92.17% recall, and 95.29% F1-score; hence, we decided to display RF classified tweets on a website called (ميراث الأدباء), that means Wnters’ Legacy in the English language, to preserve and enable readers to easily access Arabic literature tweets.