Enhancing spammer detection in online social networks with trust-based metrics.

As online social networks acquire larger user bases, they also become more interesting targets for spammers. Spam can take very different forms on social Web sites and cannot always be detected by analyzing textual content. However, the platform\u27s social nature also offers new ways of approaching the spam problem. In this work the possibilities of analyzing a user\u27s direct neighbors in the social graph to improve spammer detection are explored. Special features of social Web sites and their implicit trust relations are utilized to create an enhanced attribute set that categorizes users on the Twitter microblogging platform as spammers or legitimate users

The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race

Recent studies in social media spam and automation provide anecdotal argumentation of the rise of a new generation of spambots, so-called social spambots. Here, for the first time, we extensively study this novel phenomenon on Twitter and we provide quantitative evidence that a paradigm-shift exists in spambot design. First, we measure current Twitter's capabilities of detecting the new social spambots. Later, we assess the human performance in discriminating between genuine accounts, social spambots, and traditional spambots. Then, we benchmark several state-of-the-art techniques proposed by the academic literature. Results show that neither Twitter, nor humans, nor cutting-edge applications are currently capable of accurately detecting the new social spambots. Our results call for new approaches capable of turning the tide in the fight against this raising phenomenon. We conclude by reviewing the latest literature on spambots detection and we highlight an emerging common research trend based on the analysis of collective behaviors. Insights derived from both our extensive experimental campaign and survey shed light on the most promising directions of research and lay the foundations for the arms race against the novel social spambots. Finally, to foster research on this novel phenomenon, we make publicly available to the scientific community all the datasets used in this study.Comment: To appear in Proc. 26th WWW, 2017, Companion Volume (Web Science Track, Perth, Australia, 3-7 April, 2017