Study of Static Classification of Social Spam Profiles in MySpace

Authors

  • Danesh Irani Georgia Institute of Technology
  • Steve Webb Georgia Institute of Technology
  • Calton Pu Georgia Institute of Technology

DOI:

https://doi.org/10.1609/icwsm.v4i1.14017

Keywords:

AAAI, social networks, myspace classification, spam, social spam

Abstract

Reaching hundreds of millions of users, major social networks have become important target media for spammers. Although practical techniques such as collaborative filters and behavioral analysis are able to reduce spam, they have an inherent lag (to collect sufficient data on the spammer) that also limits their effectiveness. Through an experimental study of over 1.9 million MySpace profiles, we make a case for analysis of static user profile content, possibly as soon as such profiles are created. We compare several machine learning algorithms in their ability to distinguish spam profiles from legitimate profiles. We found that a C4.5 decision tree algorithm achieves the highest accuracy (99.4%) of finding rogue profiles, while naïve Bayes achieves a lower accuracy (92.6%). We also conducted a sensitivity analysis of the algorithms w.r.t. features which may be easily removed by spammers.

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Published

2010-05-16

How to Cite

Irani, D., Webb, S., & Pu, C. (2010). Study of Static Classification of Social Spam Profiles in MySpace. Proceedings of the International AAAI Conference on Web and Social Media, 4(1), 82-89. https://doi.org/10.1609/icwsm.v4i1.14017