Splog detection using self-similarity analysis on blog temporal dynamics
Splog detection using self-similarity analysis on blog temporal dynamics
复制标题
使用博客时间动态自相似分析进行 Splog 检测
DOI：
10.1145/1244408.1244410
复制
发表时间：
2007
影响因子：
2.2
通讯作者：
Belle L. Tseng
中科院分区：
文献类型：
--
作者：
Y. Lin;H. Sundaram;Yun Chi;J. Tatemura;Belle L. Tseng
本文主要研究垃圾博客（splog）检测。博客是一种非常流行的新型社交媒体机制。博客的存在降低了博客搜索结果，也浪费了网络资源。在我们的方法中，我们利用独特的博客时间动态来检测博客。
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).