Characterizing comment spam in the blogosphere through content analysis


Spams are no longer limited to emails and webpages. The increasing penetration of spam in the form of comments in blogs and social networks has started becoming a nuisance and potential threat. In this work, we explore the challenges posed by this type of spam in the blogosphere with substantial generalization regarding other social media. Thus, we investigate the characteristics of comment spam in blogs based on their content. The framework uses some of the previously explored methods developed to effectively extract the features of the blog spam and also introduces a novel method of active learning from the raw data without requiring training instances. This makes the approach more flexible and realistic for such applications. We also incorporate the concept of co-training for supervised learning to get accurate results. The preliminary evaluation of the proposed framework shows promising results. © 2009 IEEE.

Publication Title

2009 IEEE Symposium on Computational Intelligence in Cyber Security, CICS 2009 - Proceedings