UMBC ebiquity

SVMs for the Blogosphere: Blog Identification and Splog Detection

Authors: Pranam Kolari, Tim Finin, and Anupam Joshi

Book Title: AAAI Spring Symposium on Computational Approaches to Analysing Weblogs

Date: March 27, 2006

Abstract: Weblogs, or blogs have become an important new way to publish information, engage in discussions and form communities. The increasing popularity of blogs has given rise to search and analysis engines focusing on the 'blogosphere'. A key requirement of such systems is to identify blogs as they crawl the Web. While this ensures that only blogs are indexed, blog search engines are also often overwhelmed by spam blogs (splogs). Splogs not only incur computational overheads but also reduce user satisfaction. In this paper we first describe our experiments on blog identification using Support Vector Machines (SVM). We compare results of using different feature sets and introduce new features for blog identification. We then report preliminary results on splog detection and identify future work.

Type: InProceedings

Organization: Computer Science and Electrical Engineering

Publisher: University of Maryland, Baltimore County

Note: Also available as technical report TR-CS-05-13

Tags: blog, splog, blogosphere, categorization, blog, metadata, splog, blog, web spam, learning, spam

Google Scholar: EGVbfbEUYT4J

Number of Google Scholar citations: 112 [show citations]

Number of downloads: 9694

 

Available for download as


size: 100493 bytes

size: 4669952 bytes
 

Related Projects:

Past Projects

 memeta.
 Semantic Discovery: Discovering Complex Relationships in Semantic Web.

Bookmark at: Digg | Del.icio.us | Connotea | CiteULike