A vertical distance-based outlier detection method with local pruning
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Graphs over time: densification laws, shrinking diameters and possible explanations
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Detecting splogs via temporal dynamics using self-similarity analysis
ACM Transactions on the Web (TWEB)
Searching blogs and news: a study on popular queries
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
UMAP'11 Proceedings of the 19th international conference on Advances in User Modeling
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In this work, we propose a novel post-indexing spam-blog (or splog) detection method, which capitalizes on the results returned by blog search engines. More specifically, we analyze the search results of a sequence of temporally-ordered queries returned by a blog search engine, and build and maintain Blog profiles for those blogs whose posts frequently appear in the top-ranked search results. With the blog profiles, 4 splog scoring functions were evaluated using real data collected from a popular blog search engine. Our experiments show that the proposed method could effectively detect splogs with a high accuracy.