  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 for Twitter trends. Our
framework consists of three distinct modules: i) user classifier, ii) hashtag
classifier, and ii) trend analyzer. The user classifier 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
0.91 accuracy in classifying the manipulators. We further test Manipify on the
dataset comprising of 665 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.
Furthermore, we present two case studies of hashtag-wars and anti-state
propaganda to implicate the real-world application of our research.
