Manipify: An Automated Framework for Detecting Manipulators in Twitter Trends

The rapid adoption of online social media platforms has transformed the way of communication and interaction. On these platforms, discussions in the form of trending topics provide a glimpse of events happening around the world in real-time. Also, these trends are used for political campaigns, public awareness, and brand promotions. Consequently, these trends are sensitive to manipulation by malicious users who aim to mislead the mass audience. In this article, we identify and study the characteristics of users involved in the manipulation of Twitter trends in Pakistan. We propose “Manipify”—a framework for automatic detection and analysis of malicious users in Twitter trends. Our framework consists of three distinct modules: (1) user classifier, (2) hashtag classifier, and (3) trend analyzer. The user classifier module introduces a novel approach to automatically detect manipulators using tweet content and user behaviour features. Also, the module classifies human and bot users. Next, the hashtag classifier categorizes trending hashtags into six categories assisting in examining manipulators behaviour across different categories. Finally, the trend analyzer module examines users, hashtags, and tweets for hashtag reach, linguistic features, and user behaviour. Our user classifier module achieves 0.92 and 0.98 accuracy in classifying manipulators and bots, respectively. We further test Manipify on the dataset comprising 652 trending hashtags with 5.4 million tweets and 1.9 million users. The analysis of trends reveals that the trending panel is mostly dominated by political hashtags. In addition, our results show a higher contribution of human accounts in trend manipulation as compared to bots