Article

Spam: It's Not Just for Inboxes Anymore

IEEE Computer Magazine 01/2005; 38:28--34. pp.28--34
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    Article: SpamWall: Heuristic Filter for Web-Spam
    Aasish Kr, Pappu, Animesh Kr, Trivedi, A Abraham, Sugata Sanyal
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    ABSTRACT: Although email spam has been a problem since long time, we have recently witnessed tremendous growth in Web spam, i.e., web pages containing useless contents. These pages interfere with the algorithms that various search engines adopt for indexing and ranking, and should be filtered out. SpamWall analyses the content and structure of web pages based on pre-defined heuristics, and classifies the pages as spam or ham. Its architecture is based on the pipes-and-filters architecture. SpamWall can be used by the search engines to filter out pages that should not be indexed. SpamWall supports manual training using pre-classified corpus. Our implementation of the SpamWall system performed very well. Having trained SpamWall sufficiently, we asked it to crawl the web and classify the pages as it discovers. SpamWall classified about 92% of the pages correctly. In this paper, we describe the detail design of SpamWall.
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    Article: Efficient and Effective Spam Filtering and Re-ranking for Large Web Datasets
    Gordon V. Cormack, Mark D. Smucker, Charles L. A. Clarke
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    ABSTRACT: The TREC 2009 web ad hoc and relevance feedback tasks used a new document collection, the ClueWeb09 dataset, which was crawled from the general Web in early 2009. This dataset contains 1 billion web pages, a substantial fraction of which are spam --- pages designed to deceive search engines so as to deliver an unwanted payload. We examine the effect of spam on the results of the TREC 2009 web ad hoc and relevance feedback tasks, which used the ClueWeb09 dataset. We show that a simple content-based classifier with minimal training is efficient enough to rank the "spamminess" of every page in the dataset using a standard personal computer in 48 hours, and effective enough to yield significant and substantive improvements in the fixed-cutoff precision (estP10) as well as rank measures (estR-Precision, StatMAP, MAP) of nearly all submitted runs. Moreover, using a set of "honeypot" queries the labeling of training data may be reduced to an entirely automatic process. The results of classical information retrieval methods are particularly enhanced by filtering --- from among the worst to among the best.
    04/2010;
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    Conference Proceeding: Fourth international workshop on adversarial information retrieval on the web (AIRWeb 2008)
    Castillo, Carlos, Chellapilla, Kumar, Fetterly, Dennis
    New York, NY, USA; 01/2008
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