Identifying Persian bots on Twitter; which feature is more important: Account Information or Tweet Contents?

Publish Year: 1401
نوع سند: مقاله ژورنالی
زبان: English
View: 130

This Paper With 10 Page And PDF Format Ready To Download

  • Certificate
  • من نویسنده این مقاله هستم

استخراج به نرم افزارهای پژوهشی:

لینک ثابت به این Paper:

شناسه ملی سند علمی:

JR_ITRC-15-1_004

تاریخ نمایه سازی: 10 اردیبهشت 1402

Abstract:

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 ۹۳.۸۶%, Random Forest is the most accurate classifier among those mentioned above.

Authors

Mojtaba Mazoochi

Information Technology Research Faculty ICT Research Institute Tehran, Iran mazoochi@itrc.ac.ir

Nasrin Asadi

Information Technology Research Faculty ICT Research Institute Tehran, Iran mazoochi@itrc.ac.ir

Farzaneh Rahmani

Information Technology Research Faculty ICT Research Institute Tehran, Iran mazoochi@itrc.ac.ir

Leila Rabiei

Information Technology Research Faculty ICT Research Institute Tehran, Iran