Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Applying summarization techniques for term selection in relevance feedback
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Spam, damn spam, and statistics: using statistical analysis to locate spam web pages
Proceedings of the 7th International Workshop on the Web and Databases: colocated with ACM SIGMOD/PODS 2004
Detecting spam web pages through content analysis
Proceedings of the 15th international conference on World Wide Web
Link spam detection based on mass estimation
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
A reference collection for web spam
ACM SIGIR Forum
Improving web spam classification using rank-time features
AIRWeb '07 Proceedings of the 3rd international workshop on Adversarial information retrieval on the web
Know your neighbors: web spam detection using the web topology
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Combating web spam with trustrank
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Using word similarity to eradicate junk emails
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Boosting web retrieval through query operations
ECIR'05 Proceedings of the 27th European conference on Advances in Information Retrieval Research
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The Web provides its users with abundant information. Unfortunately, when a Web search is performed, both users and search engines are faced with an annoying problem: the presence of misleading Web pages, i.e., spamWeb pages, that are ranked among legitimate Web pages. The mixed results downgrade the performance of search engines and frustrate users who are required to filter out useless information. In order to improve the quality of Web searches, the number of spam pages on the Web must be reduced, if they cannot be eradicated entirely. In this paper, we present a novel approach for identifying spam Web pages that have mismatched titles and bodies and/or low percentage of hidden content. By considering the content of Web pages, we develop a spam-detection tool that is (i) reliable, since we can accurately detect 94% of spam/legitimate Web pages, and (ii) computational inexpensive, since the word-correlation factors used for content analysis are precomputed. We have verified that our spam-detection approach outperforms existing anti-spam methods by an average of 10% in terms of F-measure.