The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Understanding Website Behavior based on User Agent
Abstract
Web sites have adopted a variety of adversarial techniques to prevent web crawlers from retrieving their content. While it is possible to simulate users behavior using a browser to crawl such sites, this approach is not scalable. Therefore, understanding existing adversarial techniques is important to design crawling strategies that can adapt to retrieve the content as efficiently as possible. Ideally, a web crawler should detect the nature of the adversarial policies and select the most cost-effective means to defeat them.
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“The latter may include detection methods that block access to clients suspected of being automatic data collection agents, resulting in an escalation of covert (“stealth”) crawling methods”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Pham et al., 2016
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Cites
Discovery of Web Robot Sessions Based on their Navigational Patterns
Tan, P.-N., & Kumar, V. (2002) · Data Mining and Knowledge Discovery · doi:10.1023/a:1013228602957 · Cited by 268
Web robot detection: A probabilistic reasoning approach
Stassopoulou, A., & Dikaiakos, M. D. (2008) · Computer Networks · doi:10.1016/j.comnet.2008.09.021 · Cited by 102
Cloaking and Redirection: A Preliminary Study.
Wu, B., & Davison, B. D. (2005) · Cited by 99
Cloak and dagger: dynamics of web search cloaking
Wang, D. Y., Savage, S., & Voelker, G. M. (2011) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/2046707.2046763 · Cited by 98
Web robot detection techniques: overview and limitations
Doran, D., & Gokhale, S. S. (2010) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-010-0180-z · Cited by 96
Securing web service by automatic robot detection
Park, K., Pai, V. S., Lee, K., & Calo, S. B. (2006) · Cited by 75
Automated Detection and Fingerprinting of Censorship Block Pages
Jones, B., Lee, T.-W., Feamster, N., & Gill, P. (2014) · ACM Internet Measurement Conference (IMC) · doi:10.1145/2663716.2663722 · Cited by 36
Measuring the web crawler ethics
Giles, C. L., Sun, Y., & Councill, I. G. (2010) · International World Wide Web Conference (WWW) · doi:10.1145/1772690.1772824 · Cited by 33
A larger scale study of robots.txt
Kolay, S., D’Alberto, P., Dasdan, A., & Bhattacharjee, A. (2008) · International World Wide Web Conference (WWW) · doi:10.1145/1367497.1367711 · Cited by 16
Research works printed but not linked: 1
- 10.5555/1267359.1267382 · doi:10.5555/1267359.1267382
Cited by
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kiciman, E. (2016) · doi:10.2139/ssrn.2886526 · Cited by 763
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kıcıman, E. (2019) · Frontiers in Big Data · doi:10.3389/fdata.2019.00013 · Cited by 763
Metadata
- Authors
- Pham, K., Santos, A., & Freire, J.
- Affiliations
- New York University, New York, NY, USA
- Venue
- Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval
- Publisher
- ACM
- Published
- 2016-07-07
- Pages
- 1053-1056
- Cited by
- 14
- References
- 9
- Funders
- National Science Foundation (CI-EN-1405927,CNS-1229185)
- Defense Advanced Research Projects Agency (FA8750-14-2-0236)
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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.
