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.
The quality of user-generated content varies drastically from excellent to abuse and spam. As the availability of such content increases, the task of identifying high-quality content sites based on user contributions --social media sites -- becomes increasingly important. Social media in general exhibit a rich variety of information sources: in addition to the content itself, there is a wide array of non-content information available, such as links between items and explicit quality ratings from members of the community. In this paper we investigate methods for exploiting such community feedback to automatically identify high quality content. As a test case, we focus on Yahoo! Answers, a large community question answering portal that is particularly rich in the amount and types of content and social interactions available in it. We introduce a general classification framework for combining the evidence from different sources of information, that can be tuned automatically for a given social media type and quality definition. In particular, for the community question answering domain, we show that our system is able to separate high-quality items from the rest with an accuracy close to that of humans With the rise of web 2.0 there is an ever-expanding source of interesting media because of the proliferation of usergenerated content. However, mixed in with this is a large amount of noise that creates a proverbial “needle in the haystack” when searching for relevant content. Although there is hope that the rich network of interwoven metadata may contain enough structure to eventually help sift through this noise, currently many sites serve up only the “most popular” things. Identifying only the most popular items can be useful, but doing so fails to take into account the famous “long tail” behavior of the web—the notion that the collective effect of small, niche interests can outweigh the market share of the few blockbuster (i.e. most-popular) items—thus providing only content that has mass appeal and masking the interests of the idiosyncratic many. YouTube, for example, hosts over 40 million videos— enough content to keep one occupied for more than 200 years. Are there intelligent tools to search through this information-rich environment and identify interesting and relevant content? Is there a way to identify emerging trends or “hot topics” in addition to indexing the long tail for content that has real value?