Think Before RT: An Experimental Study of Abusing Twitter Trends

Abstract

Twitter is one of the most influential Online Social Networks (OSNs), adopted not only by hundreds of millions of users but also by public figures, organizations, news media, and official authorities. One of the factors contributing to this success is the inherent property of the platform for spreading news – encapsulated in short messages that are tweeted from one user to another – across the globe. Today, it is sufficient to just inspect the trending topics in Twitter for figuring out what is happening around the world. Unfortunately, the capabilities of the platform can be also abused and exploited for distributing illicit content or boosting false information, and the consequences of such actions can be really severe: one false tweet was enough for making the stock-market crash for a short period of time in 2013. In this paper, we analyze a large collection of tweets and explore the dynamics of popular trends and other Twitter features in regards to deliberate misuse. We identify a specific class of trend-exploiting campaigns that exhibits a stealthy behavior and hides spam URLs within Google search-result links. We build a spam classifier for both users and tweets, and demonstrate its simplicity and efficiency. Finally, we visualize these spam campaigns and reveal their inner structure.