An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Mining e-mail content for author identification forensics
ACM SIGMOD Record
Machine Learning
Journal of the American Society for Information Science and Technology
Community detection in large-scale social networks
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
Statistical analysis of the social network and discussion threads in slashdot
Proceedings of the 17th international conference on World Wide Web
Efficient aggregation for graph summarization
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
A framework for WWW user activity analysis based on user interest
Knowledge-Based Systems
Topic and role discovery in social networks
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Visualizing authorship for identification
ISI'06 Proceedings of the 4th IEEE international conference on Intelligence and Security Informatics
Community detection based on a semantic network
Knowledge-Based Systems
Topic oriented community detection through social objects and link analysis in social networks
Knowledge-Based Systems
Applying authorship analysis to arabic web content
ISI'05 Proceedings of the 2005 IEEE international conference on Intelligence and Security Informatics
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On virtual spaces, some individuals use multiple usernames or copycat/forge other users (usually called ''sock puppet'') to communicate with others. Those sock puppets are fake identities through which members of Internet community praise or create the illusion of support for the product or one's work, pretending to be a different person. A fundamental problem is how to identify these sock puppets. In this paper, we propose a sock puppet detection algorithm which combines authorship-identification techniques and link analysis. Firstly, we propose an interesting social network model in which links between two IDs are built if they have similar attitudes to most topics that both of them participate in; then, the edges are pruned according a hypothesis test, which consider the impact of their writing styles; finally, the link-based community detection for pruned network is performed. Compared to traditional methods, our approach has three advantages: (1) it conforms to the practical meanings of sock puppet community; (2) it can be applied in online situation; (3) it increases the efficiency of link analysis. In the experimental work, we evaluate our method using real datasets and compared our approach with several previous methods; the results have proved above advantages.