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  1. Proceedings of the 3rd international workshop on Adversarial information retrieval on the web (AIRWeb '07)
  2. Improving web spam classifiers using link structure
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Splog detection using self-similarity analysis on blog temporal dynamics
Using spam farm to boost PageRank
Combating spam in tagging systems
Improving web spam classification using rank-time features
Extracting link spam using biased random walks from spam seed sets
New metrics for reputation management in P2P networks
Improving web spam classifiers using link structure
A large-scale study of link spam detection by graph algorithms
Computing trusted authority scores in peer-to-peer web search networks
Transductive link spam detection
Measuring similarity to detect qualified links
A taxonomy of JavaScript redirection spam
Web spam detection via commercial intent analysis

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Improving web spam classifiers using link structure

Content Provider ACM Digital Library
Author Suel, Torsten Gan, Qingqing
Abstract Web spam has been recognized as one of the top challenges in the search engine industry [14]. A lot of recent work has addressed the problem of detecting or demoting web spam, including both content spam [16, 12] and link spam [22, 13]. However, any time an anti-spam technique is developed, spammers will design new spamming techniques to confuse search engine ranking methods and spam detection mechanisms. Machine learning-based classification methods can quickly adapt to newly developed spam techniques. We describe a two-stage approach to improve the performance of common classifiers. We first implement a classifier to catch a large portion of spam in our data. Then we design several heuristics to decide if a node should be relabeled based on the preclassified result and knowledge about the neighborhood. Our experimental results show visible improvements with respect to precision and recall.
Starting Page 17
Ending Page 20
Page Count 4
File Format PDF
ISBN 9781595937322
DOI 10.1145/1244408.1244412
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2007-05-08
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Link analysis Classification Machine learning Web mining Search engines Web spam detection
Content Type Text
Resource Type Article
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