A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Less is More: Active Learning with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Support Vector Machine Active Learning with Application sto Text Classification
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Discovering informative content blocks from Web documents
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Automatically collecting, monitoring, and mining japanese weblogs
Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters
Automatic Identification of Informative Sections of Web Pages
IEEE Transactions on Knowledge and Data Engineering
Spam double-funnel: connecting web spammers with advertisers
Proceedings of the 16th international conference on World Wide Web
Splog detection using self-similarity analysis on blog temporal dynamics
AIRWeb '07 Proceedings of the 3rd international workshop on Adversarial information retrieval on the web
Analysing features of Japanese splogs and characteristics of keywords
AIRWeb '08 Proceedings of the 4th international workshop on Adversarial information retrieval on the web
An empirical study on selective sampling in active learning for splog detection
Proceedings of the 5th International Workshop on Adversarial Information Retrieval on the Web
Comparing similarity of HTML structures and affiliate IDs in splog analysis
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications
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Spam blogs or splogs are blogs hosting spam posts, created using machine generated or hijacked content for the sole purpose of hosting advertisements or increasing the number of inlinks of target sites. Among those splogs, this paper focuses on detecting a group of splogs which are estimated to be created by an identical spammer. We especially show that similarities of html structures among those splogs created by an identical spammer contribute to improving the performance of splog detection. In measuring similarities of html structures, we extract a list of blocks (minimum unit of content) from the DOM tree of a html file. We show that the html files of splogs estimated to be created by an identical spammer tend to have similar DOM trees and this tendency is quite effective in splog detection.