Volume 15, Issue 1 (Special Issue on AI in ICT 2023)                   2023, 15(1): 35-44 | Back to browse issues page

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Mazoochi M, Asadi N, Rahmani F, Rabiei L. Identifying Persian bots on Twitter; which feature is more important: Account Information or Tweet Contents?. International Journal of Information and Communication Technology Research 2023; 15 (1) : 4
URL: http://ijict.itrc.ac.ir/article-1-534-en.html
Identifying Persian bots on Twitter; which feature is more important: Account Information or Tweet Contents?
Mojtaba Mazoochi 1, Nasrin Asadi2 , Farzaneh Rahmani2 , Leila Rabiei3
1- Information Technology Research Faculty ICT Research Institute Tehran, Iran mazoochi@itrc.ac.ir , mazoochi@itrc.ac.ir
2- Information Technology Research Faculty ICT Research Institute Tehran, Iran mazoochi@itrc.ac.ir
3- Information Technology Research Faculty ICT Research Institute Tehran, Iran
Abstract:   (903 Views)
The spread of internet and smartphones in recent years has led to the popularity and easy accessibility of social networks among users. Despite the benefits of these networks, such as ease of interpersonal communication and providing a space for free expression of opinions, they also provide the opportunity for destructive activities such as spreading false information or using fake accounts for fraud intentions. Fake accounts are mainly managed by bots. So, identifying bots and suspending them could very much help to increase the popularity and favorability of social networks. In this paper, we try to identify Persian bots on Twitter. This seems to be a challenging task in view of the problems pertinent to processing colloquial Persian. To this end, a set of features based on user account information and activity of users added to content features of tweets to classify users by several machine learning algorithms like Random Forest, Logistic Regression and SVM. The results of experiments on a dataset of Persian-language users show the proper performance of the proposed methods. It turns out that, achieving a balanced-accuracy of 93.86%, Random Forest is the most accurate classifier among those mentioned above.
Article number: 4
Keywords: social networks, Twitter, bot detection, classification, Persian language
Full-Text [PDF 780 kb]   (474 Downloads)    
Type of Study: Research | Subject: Information Technology
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