Master of Science (MS)
Information Technology.; Artificial Intelligence.; Computer Science.
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's social nature also offers new ways of approaching the spam problem. In this work the possibilities of analyzing a user's 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.
Murmann, Alexander J., "Enhancing spammer detection in online social networks with trust-based metrics." (2009). Master's Theses. Paper 3985.