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General Information
    • ISSN: 2382-6185
    • Frequency: Quarterly (2015-2016); semiyearly (Since 2017)
    • DOI: 10.18178/IJKE
    • Editor-in-Chief: Prof. Chen-Huei Chou
    • Executive Editor: Ms. Nina Lee
    • Indexed by: Google Scholar, Crossref, ProQuest
    • E-mail: ijke@ejournal.net
Prof. Chen-Huei Chou
College of Charleston, SC, USA
It is my honor to be the editor-in-chief of IJKE. I will do my best to help develop this journal better.
IJKE 2015 Vol.1(2): 113-119 ISSN: 2382-6185
DOI: 10.7763/IJKE.2015.V1.19

Detection of Abusive Accounts with Arabic Tweets

Ehab A. Abozinadah, Alex V. Mbaziira, and James H. Jones Jr.
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).


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.

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