Twitter is used for a variety of reasons, including informa- tion dissemination, marketing, political organizing and to spread propaganda, spamming, promotion, conversations, and so on. Characterizing these activities and categoriz- ing associated user generated content is a challenging task. We present a information-theoretic approach to classica- tion of user activity on Twitter. We focus on tweets that contain embedded URLs and study their collective 'retweet- ing' dynamics. We identify two features, time-interval and user entropy, which we use to classify retweeting activity. We achieve good separation of dierent activities using just these two features and are able to categorize content based on the collective user response it generates. We have identi- ed ve distinct categories of retweeting activity on Twitter: automatic robotic activity, newsworthy information dissem- ination, advertising and promotion, campaigns, and para- sitic advertisement. In the course of our investigations, we have shown how Twitter can be exploited for promotional and spam-like activities. The content-independent, entropy- based activity classication method is computationally e- cient, scalable and robust to sampling and missing data. It has many applications, including automatic spam-detection, trend identication, trust management, user-modeling, so- cial search and content classication on online social media.
We study several longstanding questions in media communications research, in the context of the microblogging service Twitter, regarding the production, flow, and consumption of information. To do so, we exploit a recently introduced feature of Twitter known as "lists" to distinguish between elite users - by which we mean celebrities, bloggers, and representatives of media outlets and other formal organizations - and ordinary users. Based on this classification, we find a striking concentration of attention on Twitter, in that roughly 50 of URLs consumed are generated by just 20K elite users, where the media produces the most information, but celebrities are the most followed. We also find significant homophily within categories: celebrities listen to celebrities, while bloggers listen to bloggers etc; however, bloggers in general rebroadcast more information than the other categories. Next we re-examine the classical "two-step flow" theory of communications, finding considerable support for it on Twitter. Third, we find that URLs broadcast by different categories of users or containing different types of content exhibit systematically different lifespans. And finally, we examine the attention paid by the different user categories to different news topics.