Detecting Spam Blogs: A Machine Learning Approach

Weblogs or blogs are an important new way to publish information, engage in discussions, and form communities on the Internet. The Blogosphere has unfortunately been infected by several varieties of spam-like content. Blog search engines, for example, are inundated by posts from splogs – false blogs with machine generated or hijacked content whose sole purpose is to host ads or raise the PageRank of target sites. We discuss how SVM models based on local and link-based features can be used to detect splogs. We present an evaluation of learned models and their utility to blog search engines; systems that employ techniques differing from those of conventional web search engines. We evaluate the effectiveness of a combination of features, and finally report our informal analysis of a blog search engine index.
Date: July 16, 2006
Book Title: Proceedings of the 21st National Conference on Artificial Intelligence (AAAI 2006)
Type: InProceedings
Publisher: University of Maryland, Baltimore County
Organization: Computer Science and Electrical Engineering
Google scholar: 7aKBgonbm-gJ
Google citations: 25 citations
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Bibtex


@InProceedings{Detecting_Spam_Blogs_A_Machine_Learning_,
  author = "Pranam Kolari and Akshay Java and Tim Finin and Tim Oates and Anupam Joshi",
  title = "{Detecting Spam Blogs: A Machine Learning Approach}",
  month = "July",
  year = "2006",
  organization = " Computer Science and Electrical Engineering",
  booktitle = "Proceedings of the 21st National Conference on Artificial Intelligence (AAAI 2006)",
  publisher = " University of Maryland, Baltimore County",
}