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

ENHANCING SPAMMER DETECTION IN ONLINE SOCIAL NETWORKS WITH TRUST-BASED METRICS by Alexander J. Murmann 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.

[1]  Jordi Torres,et al.  Web Customer Modeling for Automated Session Prioritization on High Traffic Sites , 2007, User Modeling.

[2]  Alireza Noruzi.  Google Scholar: The New Generation of Citation Indexes , 2005 .

[3]  Diego Gambetta.  Can We Trust Trust , 2000 .

[4]  Andreas Hotho,et al.  The anti-social tagger: detecting spam in social bookmarking systems , 2008, AIRWeb '08.

[5]  Linton C. Freeman,et al.  Carnegie Mellon: Journal of Social Structure: Visualizing Social Networks Visualizing Social Networks , 2022 .

[6]  William W. Cohen.  Fast Effective Rule Induction , 1995, ICML.

[7]  P. Oscar Boykin,et al.  Leveraging social networks to fight spam , 2005, Computer.

[8]  Qin Zhang,et al.  Online trust forming mechanism: approaches and an integrated model , 2005, ICEC '05.

[9]  Georgia Koutrika,et al.  Fighting Spam on Social Web Sites: A Survey of Approaches and Future Challenges , 2007, IEEE Internet Computing.

[10]  Erik Johnson,et al.  Is a bot at the controls?: Detecting input data attacks , 2007, NetGames '07.

[11]  Radu Sion,et al.  On-the-fly intrusion detection for Web portals , 2003, Proceedings ITCC 2003. International Conference on Information Technology: Coding and Computing.

[12]  C. Lee Giles,et al.  CiteSeer: an automatic citation indexing system , 1998, DL '98.

[13]  Philippe Golle,et al.  Keeping bots out of online games , 2005, ACE '05.

[14]  Audun Jøsang,et al.  A survey of trust and reputation systems for online service provision , 2007, Decis. Support Syst..

[15]  Garcia-MolinaHector,et al.  Fighting Spam on Social Web Sites , 2007 .

[16]  S. Shapiro.  The Social Control of Impersonal Trust , 1987, American Journal of Sociology.

[17]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[18]  Vipin Kumar,et al.  Introduction to Data Mining , 2022, Data Mining and Machine Learning Applications.

[19]  Virgílio A. F. Almeida,et al.  Detecting Spammers and Content Promoters in Online Video Social Networks , 2009, IEEE INFOCOM Workshops 2009.

[20]  P. Blau.  Exchange and Power in Social Life , 1964 .

[21]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[22]  Ray Hunt,et al.  Tightening the net: A review of current and next generation spam filtering tools , 2006, Comput. Secur..

[23]  R. Oliver.  A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions , 1980 .