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Splog detection using content, time and link structures (2007)
| Content Provider | CiteSeerX |
|---|---|
| Author | Lin, Yu-Ru Sundaram, Hari Chi, Yun Tatemura, Jun Tseng, Belle |
| Description | 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. 1 IEEE International Conference on Multimedia and Expo 2007 |
| File Format | |
| Language | English |
| Publisher Date | 2007-01-01 |
| Access Restriction | Open |
| Subject Keyword | Waste Network Resource Post Time Hub Score Measure Real World Dataset Splog Detection Task Unique Blog Temporal Dynamic Splog Detection Blog Search Result Spam Blog Ground Truth Splogs Exhibit High Temporal Regularity Post Time Difference Distribution Link Structure New Medium Social Communication Mechanism Key Idea Excellent Result Link Regularity Temporal Content Regularity Post Content Novel Autocorrelation Temporal Structural Regularity |
| Content Type | Text |
| Resource Type | Article |
