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Twitter is one of the most popular sources for disseminating news and propaganda in the Arab region. Spammers are now creating abusive accounts to distribute adult content in Arabic tweets, which is prohibited by Arabic norms and cultures. Arab governments are facing a massive challenge to detect these accounts. This paper evaluates different machine learning algorithms for detecting abusive accounts with Arabic tweets, using Nave Bayes (NB), Support Vector Machine (SVM), and Decision Tree (J48) classifiers. We are not aware of another existing data set of abusive accounts with Arabic tweets, and this is the first study to investigate this issue. The data set for this analysis was collected based on the top five Arabic swearing words. The results show that the Nave Bayes (NB) classifier with 10 tweets and 100 features has the best performance with 90% accuracy rate.
Abozinadah et al. (Thu,) studied this question.
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