NDLI: Splog detection using self-similarity analysis on blog temporal dynamics

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Splog detection using self-similarity analysis on blog temporal dynamics

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Splog detection using self-similarity analysis on blog temporal dynamics

Content Provider	ACM Digital Library
Author	Tatemura, Junichi Tseng, Belle L. Chi, Yun Lin, Yu-Ru Sundaram, Hari
Abstract	This paper focuses on spam blog (splog) detection. Blogs are highly popular, new media social communication mechanisms. The presence of splogs degrades blog search results as well as wastes network resources. In our approach we exploit unique blog temporal dynamics to detect splogs. There are three key ideas in our splog detection framework. We first represent the blog temporal dynamics using self-similarity matrices defined on the histogram intersection similarity measure of the time, content, and link attributes of posts. Second, we show via a novel visualization that the blog temporal characteristics reveal attribute correlation, depending on type of the blog (normal blogs and splogs). Third, we propose the use of temporal structural properties computed from self-similarity matrices across different attributes. In a splog detector, these novel features are combined with content based features. We extract a content based feature vector from different parts of the blog -- URLs, post content, etc. The dimensionality of the feature vector is reduced by Fisher linear discriminant analysis. We have tested an SVM based splog detector using proposed features on real world datasets, with excellent results (90% accuracy).
Starting Page	1
Ending Page	8
Page Count	8
File Format	PDF
ISBN	9781595937322
DOI	10.1145/1244408.1244410
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2007-05-08
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Temporal dynamics Blogs Topology Regularity Spam Splog detection Self-similarity
Content Type	Text
Resource Type	Article

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