Splog Detection using Content, Time and Link Structures
Splog Detection using Content, Time and Link Structures
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DOI：
10.1109/icme.2007.4285079
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发表时间：
2007-07
期刊：
影响因子：
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通讯作者：
Y. Lin;H. Sundaram;Yun Chi;J. Tatemura;Belle L. Tseng
中科院分区：
文献类型：
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作者：
Y. Lin;H. Sundaram;Yun Chi;J. Tatemura;Belle L. Tseng
本文主要研究垃圾博客（splog）的检测。博客是一个非常流行的网络，新媒体的社会化传播机制和垃圾博客破坏了博客的搜索结果，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检测splogs。关键的想法是，splogs在内容和发布时间上表现出高度的时间规律性，以及一致的链接模式。时间内容的规律性检测使用一种新的自相关后的内容。时间结构规律性是使用后时间差分布的熵来确定的，而链接规律性是使用基于HITS的枢纽得分度量来计算的。在真实的世界数据集上进行的实验表明，该算法在splog检测任务上具有良好的效果，准确率达到90%。
This paper focuses on spam blog (splog) detection. Blogs are highly popular, new media social communication mechanisms and splogs corrupt blog search results as well as waste network resources. In our approach we exploit unique blog temporal dynamics to detect splogs. The key idea is that splogs exhibit high temporal regularity in content and post time, as well as consistent linking patterns. Temporal content regularity is detected using a novel autocorrelation of post content. Temporal structural regularity is determined using the entropy of the post time difference distribution, while the link regularity is computed using a HITS based hub score measure. Experiments based on the annotated ground truth on real world dataset show excellent results on splog detection tasks with 90% accuracy.