Tagging Fake Profiles in Twitter Using Machine Learning Approach

Abstract

Today, social media data spread more swiftly, which can be beneficial or destructive in various circumstances. Because of their widespread use, OSNs have become a platform for spammers to distribute undesirable content. Many security breaches have been noticed with the increasing frequency of cyber-attacks. Cyber-attacks are making news, and consumers are turning their trust in online social networks against them. These attacks have far-reaching consequences. This necessitates identifying such fictitious users to keep the trust of the social networks’ users intact. In this paper, a method based on machine learning classification algorithms for specifically tagging fake Twitter profiles has been proposed. Fake users are spam profiles that appear to be actual users and are responsible for tarnishing the reputation of legitimate users. Data from about 8 K users have been used to validate the suggested system for this purpose. The accuracy of the proposed technique is around 83.5% with logistic regression classifier.