The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Know your neighbors: web spam detection using the web topology
Abstract
Web spam can significantly deteriorate the quality of search engine results. Thus there is a large incentive for commercial search engines to detect spam pages efficiently and accurately. In this paper we present a spam detection system that combines link-based and content-based features, and uses the topology of the Web graph by exploiting the link dependencies among the Web pages. We find that linked hosts tend to belong to the same class: either both are spam or both are non-spam. We demonstrate three methods of incorporating the Web graph topology into the predictions obtained by our base classifier: (i) clustering the host graph, and assigning the label of all hosts in the cluster by majority vote, (ii) propagating the predicted labels to neighboring hosts, and (iii) using the predicted labels of neighboring hosts as new features and retraining the classifier. The result is an accurate system for detecting Web spam, tested on a large and public dataset, using algorithms that can be applied in practice to large-scale Web data.
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Baeza-Yates, R. A., & Ribeiro-Neto, B. A. (1999) · Cited by 11,505
Witten, I. H. (2008) · Cited by 9,143
Data mining: practical machine learning tools and techniques with Java implementations
Witten, I. H., & Frank, E. (2002) · ACM SIGMOD Record · doi:10.1145/507338.507355 · Cited by 5,137
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Boldi, P., & Vigna, S. (2004) · Proceedings of the 13th international conference on World Wide Web · doi:10.1145/988672.988752 · Cited by 1,201
Combating Web Spam with TrustRank
Gyöngyi, Z., Garcia-Molina, H., & Pedersen, J. (2004) · Proceedings 2004 VLDB Conference · doi:10.1016/b978-012088469-8.50052-8 · Cited by 1,085
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
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Akoglu, L., Tong, H., & Koutra, D. (2015) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-014-0365-y · Cited by 1,504
What Yelp Fake Review Filter Might Be Doing?
Mukherjee, A., Venkataraman, V., Liu, B., & Glance, N. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v7i1.14389 · Cited by 557
DON’T FOLLOW ME - Spam Detection in Twitter
(2010) · Proceedings of the International Conference on Security and Cryptography · doi:10.5220/0002996201420151 · Cited by 462
Understanding and combating link farming in the twitter social network
Ghosh, S., Viswanath, B., Kooti, F., Sharma, N. K., Korlam, G., Benevenuto, F., Ganguly, N., & Gummadi, K. P. (2012) · Proceedings of the 21st international conference on World Wide Web · doi:10.1145/2187836.2187846 · Cited by 342
Detecting Spam Bots in Online Social Networking Sites: A Machine Learning Approach
Wang, A. H. (2010) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-13739-6_25 · Cited by 257
Metadata
- Authors
- Castillo, C., Donato, D., Gionis, A., Murdock, V., & Silvestri, F.
- Venue
- Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
- Publisher
- ACM
- Published
- 2007-07-23
- Pages
- 423-430
- OpenAlex
- W2130196635
- Cited by
- 310
- References
- 38
- FWCI
- 20.11
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Castillo, C., Donato, D., Gionis, A., Murdock, V., & Silvestri, F. (2007). Know your neighbors: web spam detection using the web topology. Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval. https://doi.org/10.1145/1277741.1277814
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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.
