Abstract—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 Naïve 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 Naïve
Bayes (NB) classifier with 10 tweets and 100 features has the
best performance with 90% accuracy rate.
Index Terms—Arabic text classification, machine learning,
pornographic spam, social network abuse.
The authors are with Computer Science Department, George Mason
University, Fairfax, VA 22030 USA (e-mail: eabozina@gmu.edu,
ambaziir@gmu.edu, jjonesu@gmu.edu).
[PDF]
Cite: Ehab A. Abozinadah, Alex V. Mbaziira, and James H. Jones Jr., "Detection of Abusive Accounts with Arabic Tweets," International Journal of Knowledge Engineering vol. 1, no. 2, pp. 113-119, 2015.
