The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Spam, damn spam, and statistics: using statistical analysis to locate spam web pages
Abstract
The increasing importance of search engines to commercial web sites has given rise to a phenomenon we call "web spam", that is, web pages that exist only to mislead search engines into (mis)leading users to certain web sites. Web spam is a nuisance to users as well as search engines: users have a harder time finding the information they need, and search engines have to cope with an inflated corpus, which in turn causes their cost per query to increase. Therefore, search engines have a strong incentive to weed out spam web pages from their index.We propose that some spam web pages can be identified through statistical analysis: Certain classes of spam pages, in particular those that are machine-generated, diverge in some of their properties from the properties of web pages at large. We have examined a variety of such properties, including linkage structure, page content, and page evolution, and have found that outliers in the statistical distribution of these properties are highly likely to be caused by web spam.This paper describes the properties we have examined, gives the statistical distributions we have observed, and shows which kinds of outliers are highly correlated with web spam.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
(1999) · Cited by 12,854
Broder, A., Kumar, R., Maghoul, F., Raghavan, P., Rajagopalan, S., Stata, R., Tomkins, A., & Wiener, J. (2000) · Computer Networks · doi:10.1016/s1389-1286(00)00083-9 · Cited by 2,810
Syntactic clustering of the Web
Broder, A. Z., Glassman, S. C., Manasse, M. S., & Zweig, G. (1997) · Computer Networks and ISDN Systems · doi:10.1016/s0169-7552(97)00031-7 · Cited by 1,343
Self-citationThe Evolution of the Web and Implications for an Incremental Crawler
Cho, J. J., & García-Molina, H. (2000) · Cited by 499
Challenges in web search engines
Henzinger, M. R., Motwani, R., & Silverstein, C. (2002) · ACM SIGIR Forum · doi:10.1145/792550.792553 · Cited by 307
A large-scale study of the evolution of web pages
Fetterly, D., Manasse, M., Najork, M., & Wiener, J. (2003) · Proceedings of the twelfth international conference on World Wide Web - WWW '03 · doi:10.1145/775152.775246 · Cited by 189
Self-citationWho links to whom: mining linkage between Web sites
Bharat, K., Chang, B.-W., Henzinger, M., & Ruhl, M. (2002) · Proceedings 2001 IEEE International Conference on Data Mining · doi:10.1109/icdm.2001.989500 · Cited by 166
On the evolution of clusters of near-duplicate Web pages
Fetterly, D., Manasse, M. S., & Najork, M. (2004) · doi:10.1109/laweb.2003.1250280 · Cited by 153
Self-citationRecognizing Nepotistic Links on the Web
Davison, B. D. (2000) · Cited by 152
Amitay, E., Carmel, D., Darlow, A., Lempel, R., & Soffer, A. (2003) · ACM Conference on Hypertext and Social Media (HT) · doi:10.1145/900051.900060 · Cited by 108
Cited by
Detecting spam web pages through content analysis
Ntoulas, A., Najork, M., Manasse, M., & Fetterly, D. (2006) · Proceedings of the 15th international conference on World Wide Web · doi:10.1145/1135777.1135794 · Cited by 608
Self-citationKnow your neighbors: web spam detection using the web topology
Castillo, C., Donato, D., Gionis, A., Murdock, V., & Silvestri, F. (2007) · Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/1277741.1277814 · Cited by 310
Detecting spammers and content promoters in online video social networks
Benevenuto, F., Rodrigues, T., Almeida, V., Almeida, J., & Gonçalves, M. (2009) · Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/1571941.1572047 · Cited by 220
Understanding User Behavior Through Log Data and Analysis
Dumais, S., Jeffries, R., Russell, D. M., Tang, D., & Teevan, J. (2014) · Ways of Knowing in HCI · doi:10.1007/978-1-4939-0378-8_14 · Cited by 131
(2008) · Cited by 38
Metadata
- Authors
- Fetterly, D., Manasse, M., & Najork, M.
- Venue
- Proceedings of the 7th International Workshop on the Web and Databases: colocated with ACM SIGMOD/PODS 2004
- Publisher
- ACM
- Published
- 2004-06-17
- Pages
- 1-6
- OpenAlex
- W1987365175
- Cited by
- 291
- References
- 11
- FWCI
- 18.68
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Spam, damn spam, and statistics: using statistical analysis to locate spam web pages. https://app.cochat.ai/papers/10.1145/1017074.1017077
- The paper
- Fetterly, D., Manasse, M., & Najork, M. (2004). 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. https://doi.org/10.1145/1017074.1017077
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
