A Comprehensive Analysis Dashboard for Detecting Similar Saudi Twitter Accounts by Using Stylometric Features

Abstract

Criminals, including terrorists, may use Twitter to communicate and share their ideologies. They often employ multiple accounts for anonymity. While the other accounts hide their identities and use it for different purposes (e.g., communication with unknown criminals), they use one to write tweets revealing their beliefs and spreading evil thoughts (e.g., racism and bullying). Since these multiple accounts will not have the same contents, stakeholders cannot rely on the contents to detect various accounts belonging to the same person. Using stylometric features may help to detect these accounts as they depend on the writing style of a person rather than the content of the words and their meaning. In this paper, we build a model and use stylometric features that differ from the state-of-the-art, such as n-gram for part-of-speech tag, frequency of repeated characters, number of emojis, and more. These features distinguish our approach from existing methods. We evaluated different machine and deep learning classifiers to make comparisons. Our results highlight the effectiveness of our feature set, achieving a remarkable accuracy of 96%. Additionally, our findings indicate that machine learning classifiers exhibit superiority over their deep learning counterparts in the context of this study. Furthermore, we developed a comprehensive dashboard that offers users an in-depth analysis of different Twitter accounts and ranks their similarities. The dashboard serves as a valuable tool for gaining insights into the relationships and similarities among the analyzed Twitter accounts.