Splog Filtering Based on Writing Consistency
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
A co-classification framework for detecting web spam and spammers in social media web sites
Proceedings of the 18th ACM conference on Information and knowledge management
A user-oriented splog filtering based on a machine learning
BlogTalk'08/09 Proceedings of the 2008/2009 international conference on Social software: recent trends and developments in social software
A Self-Supervised Approach to Comment Spam Detection Based on Content Analysis
International Journal of Information Security and Privacy
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This paper reports the estimated number of spam blogs in order to assess their current state in the blogosphere. To extract spam blogs, I developed a traversal method among co-citation clusters of blogs from a spam seed. Spam seeds were collected in terms of high out-degree and spam keyword. According to the experiment, a mixed seed set composed of high out-degree and spam keyword seeds is more effective than individual seed sets in terms of F-Measure. In conclusion, mixed seeds from different methods are effective in improving the F-Measure results of spam extraction with co-citation clusters.