The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Detecting spam web pages through content analysis
Abstract
In this paper, we continue our investigations of "web spam": the injection of artificially-created pages into the web in order to influence the results from search engines, to drive traffic to certain pages for fun or profit. This paper considers some previously-undescribed techniques for automatically detecting spam pages, examines the effectiveness of these techniques in isolation and when aggregated using classification algorithms. When combined, our heuristics correctly identify 2,037 (86.2%) of the 2,364 spam pages (13.8%) in our judged collection of 17,168 pages, while misidentifying 526 spam and non-spam pages (3.1%).
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Citations
Cites
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Combating Web Spam with TrustRank
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Henzinger, M. R., Motwani, R., & Silverstein, C. (2002) · ACM SIGIR Forum · doi:10.1145/792550.792553 · Cited by 307
Spam, damn spam, and statistics: using statistical analysis to locate spam web pages
Fetterly, D., Manasse, M., & Najork, M. (2004) · Proceedings of the 7th International Workshop on the Web and Databases: colocated with ACM SIGMOD/PODS 2004 · doi:10.1145/1017074.1017077 · Cited by 291
Self-citation
Cited by
Jindal, N., & Liu, B. (2008) · Proceedings of the international conference on Web search and web data mining - WSDM '08 · doi:10.1145/1341531.1341560 · Cited by 1,542
Graph based anomaly detection and description: a survey
Akoglu, L., Tong, H., & Koutra, D. (2015) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-014-0365-y · Cited by 1,504
A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities
Zhou, X., & Zafarani, R. (2021) · ACM Computing Surveys · doi:10.1145/3395046 · Cited by 1,184
Finding Deceptive Opinion Spam by Any Stretch of the Imagination
Ott, M., Choi, Y., Cardie, C., & Hancock, J. T. (2011) · arXiv · doi:10.48550/arxiv.1107.4557 · Cited by 1,008
Spotting fake reviewer groups in consumer reviews
Mukherjee, A., Liu, B., & Glance, N. (2012) · Proceedings of the 21st international conference on World Wide Web · doi:10.1145/2187836.2187863 · Cited by 773
Metadata
- Authors
- Ntoulas, A., Najork, M., Manasse, M., & Fetterly, D.
- Venue
- Proceedings of the 15th international conference on World Wide Web
- Publisher
- ACM
- Published
- 2006-05-23
- Pages
- 83-92
- OpenAlex
- W2066055909
- Cited by
- 608
- References
- 26
- FWCI
- 33.60
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Ntoulas, A., Najork, M., Manasse, M., & Fetterly, D. (2006). Detecting spam web pages through content analysis. Proceedings of the 15th international conference on World Wide Web. https://doi.org/10.1145/1135777.1135794
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
