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.
Web 2.0 promises rich opportunities for information sharing, electronic commerce, and new modes of social interaction, all centered around the "social Web" of user-contributed content, social annotations, and person-to-person social connections. But the increasing reliance on this "social Web" also places individuals and their computer systems at risk, creating opportunities for malicious participants to exploit the tight social fabric of these networks. With these problems in mind, we propose the SocialTrust framework for tamper-resilient trust establishment in online communities. SocialTrust provides community users with dynamic trust values by (i) distinguishing relationship quality from trust; (ii) incorporating a personalized feedback mechanism for adapting as the community evolves; and (iii) tracking user behavior. We experimentally evaluate the SocialTrust framework using real online social networking data consisting of millions of MySpace profiles and relationships. We find that SocialTrust supports robust trust establishment even in the presence of large-scale collusion by malicious participants. The popularity of social bookmarking sites has made them prime targets for spammers. Many of these systems require an administrator's time and energy to manually filter or remove spam. Here we discuss the motivations of social spam, and present a study of automatic detection of spammers in a social tagging system. We identify and analyze six distinct features that address various properties of social spam, finding that each of these features provides for a helpful signal to discriminate spammers from legitimate users. These features are then used in various machine learning algorithms for classification, achieving over 98 accuracy in detecting social spammers with 2 false positives. These promising results provide a new baseline for future efforts on social spam. We make our dataset publicly available to the research community.