Abstract
A large number of Internet users are currently using social networking services (SNS) such as Twitter and Facebook. However, the SNS users are exposed to threats of malicious messages and spams from unwanted sources. It would be useful to have an effective method for detecting spammers or content polluters on social networks. In this paper, we present an efficient method for detecting content polluters on Twitter. Our approach needs only a few feature values for each Twitter user and hence requires a lot less time in the overall mining process. We demonstrate that our approach performs better than the previous approach in terms of the classification accuracy and the mining time.
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http://infolab.tamu.edu/static/users/kyumin/social_honeypot_icwsm_2011.zip
Acknowledgments
The present research has been supported by the Research Grant of Kwangwoon University (No. 60012007197)
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© 2013 Springer Science+Business Media Dordrecht
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Han, J.S., Park, B.J. (2013). Efficient Detection of Content Polluters in Social Networks. In: Kim, K., Chung, KY. (eds) IT Convergence and Security 2012. Lecture Notes in Electrical Engineering, vol 215. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5860-5_119
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DOI: https://doi.org/10.1007/978-94-007-5860-5_119
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