The DOI matches its Crossref record.
Spatial variation in search engine queries
Abstract
Local aspects of Web search - associating Web content and queries with geography - is a topic of growing interest. However, the underlying question of how spatial variation is manifested in search queries is still not well understood. Here we develop a probabilistic framework for quantifying such spatial variation; on complete Yahoo! query logs, we find that our model is able to localize large classes of queries to within a few miles of their natural centers based only on the distribution of activity for the query. Our model provides not only an estimate of a query's geographic center, but also a measure of its spatial dispersion, indicating whether it has highly local interest or broader regional or national appeal. We also show how variations on our model can track geographically shifting topics over time, annotate a map with each location's "distinctive queries", and delineate the "spheres of influence" for competing queries in the same general domain.
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
Text-Based Twitter User Geolocation Prediction
Han, B., Cook, P., & Baldwin, T. (2014) · Journal of Artificial Intelligence Research · doi:10.1613/jair.4200 · Cited by 324
Metadata
- Authors
- Backstrom, L., Kleinberg, J., Kumar, R., & Novak, J.
- Venue
- Proceedings of the 17th international conference on World Wide Web
- Publisher
- ACM
- Published
- 2008-04-21
- Pages
- 357-366
- OpenAlex
- W2091802992
- Cited by
- 196
- References
- 0
- FWCI
- 9.97
- 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: Spatial variation in search engine queries. https://app.cochat.ai/papers/10.1145/1367497.1367546
- The paper
- Backstrom, L., Kleinberg, J., Kumar, R., & Novak, J. (2008). Spatial variation in search engine queries. Proceedings of the 17th international conference on World Wide Web. https://doi.org/10.1145/1367497.1367546
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.