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.
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measured
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
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)
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: Understanding Website Behavior based on User Agent. https://app.cochat.ai/papers/10.1145/2911451.2914757
- The paper
- Pham, K., Santos, A., & Freire, J. (2016). Understanding Website Behavior based on User Agent. Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. https://doi.org/10.1145/2911451.2914757
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.