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The rise in popularity of social networking sites such as Twitter and Facebook has been paralleled by the rise of unwanted, disruptive entities on these networks- — including spammers, malware disseminators, and other content polluters. Inspired by sociologists working to ensure the success of commons and criminologists focused on deterring vandalism and preventing crime, we present the first long-term study of social honeypots for tempting, profiling, and filtering content polluters in social media. Concretely, we report on our experiences via a seven-month deployment of 60 honeypots on Twitter that resulted in the harvesting of 36,000 candidate content polluters. As part of our study, we (1) examine the harvested Twitter users, including an analysis of link payloads, user behavior over time, and followers/following network dynamics and (2) evaluate a wide range of features to investigate the effectiveness of automatic content polluter identification.
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Kyumin Lee
Brian Eoff
James Caverlee
Proceedings of the International AAAI Conference on Web and Social Media
Texas A&M University
Mitchell Institute
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Lee et al. (Tue,) studied this question.
www.synapsesocial.com/papers/6a0922acfebbf018f815f999 — DOI: https://doi.org/10.1609/icwsm.v5i1.14106