The DOI matches its Crossref record.
Improved algorithms for topic distillation in a hyperlinked environment
Open accessAbstract
This paper addresses the problem of topic distillation on the World Wide Web, namely, given a typical user query to find quality documents related to the query topic.Connectivity analysis has been shown to be useful in identifying high quality pages within a topic specific graph of hyperlinked documents.The essence of our approach is to augment a previous connectivity analysis based algorithm with content analysis.We identify three problems with the existing approach and devise algorithms to tackle them.The results of a user evaluation are reported that show an improvement of precision at 10 documents by at least 45% over pure connectivity analysis.
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
Cited by
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
Haveliwala, T. H. (2002) · Proceedings of the 11th international conference on World Wide Web · doi:10.1145/511446.511513 · Cited by 1,567
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
Bianchini, M., Gori, M., & Scarselli, F. (2005) · ACM Transactions on Internet Technology · doi:10.1145/1052934.1052938 · Cited by 539
Template detection via data mining and its applications
Bar-Yossef, Z., & Rajagopalan, S. (2002) · Proceedings of the 11th international conference on World Wide Web · doi:10.1145/511446.511522 · Cited by 271
Metadata
- Authors
- Bharat, K., & Henzinger, M. R.
- Venue
- Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
- Publisher
- ACM
- Published
- 1998-08
- Pages
- 104-111
- OpenAlex
- W4240407914
- Cited by
- 444
- References
- 0
- FWCI
- 21.08
- Type
- conference paper
- Language
- en
- Open access
- bronze
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: Improved algorithms for topic distillation in a hyperlinked environment. https://app.cochat.ai/papers/10.1145/290941.290972
- The paper
- Bharat, K., & Henzinger, M. R. (1998). Improved algorithms for topic distillation in a hyperlinked environment. Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval. https://doi.org/10.1145/290941.290972
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.