research-article

Spatial variation in search engine queries

Authors:

Lars Backstrom,

Jasmine NovakAuthors Info & Claims

WWW '08: Proceedings of the 17th international conference on World Wide Web

Pages 357 - 366

https://doi.org/10.1145/1367497.1367546

Published: 21 April 2008 Publication History

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.

References

[1]

E. Amitay, N. Har'El, R. Sivan, A. Soffer. Web-a-where: Geotagging Web content. In SIGIR, pages 273--280, 2004.

Digital Library

[2]

O. Buyukkokten, J. Cho, H. Garcia-Molina, L. Gravano, and N. Shivakumar. Exploiting geographical location information of Web pages. In WebDB (Informal Proceedings), pages 91--96, 1999.

[3]

Y.-Y. Chen, T. Suel, and A. Markowetz. Efficient query processing in geographic Web search engines. In SIGMOD, pages 277--288, 2006.

Digital Library

[4]

J. Ding, L. Gravano, N. Shivakumar. Computing geographical scopes of Web resources. In VLDB, pages 545--556, 2000.

Digital Library

[5]

M. Dubinko, R. Kumar, J. Magnani, J. Novak, P. Raghavan, A. Tomkins. Visualizing tags over time. In WWW, pages 193--202, 2006.

Digital Library

[6]

W. Gao, H. C. Lee, Y. Miao. Geographically focused collaborative crawling. In WWW, pages 287--296, 2006.

Digital Library

[7]

L. Gravano, V. Hatzivassiloglou, and R. Lichtenstein. Categorizing Web queries according to geographical locality. In CIKM, pages 325--333, 2003.

Digital Library

[8]

J. Kleinberg. Temporal dynamics of online information streams. In M. Garofalakis, J. Gehrke, R. Rastogi (eds.) Data Stream Management. Springer, 2008.

[9]

M. Kulldorff. A spatial scan statistic. Communications in Statistics: Theory and Methods, 26(6):1481--1496, 1997.

[10]

B. Lawson and D. G. T. Denison, editors. Spatial Cluster Modelling. Chapman & Hall, 2002.

[11]

B. Martins, M. S. Chaves, M. J. Silva. Assigning geographical scopes to Web pages. In ECIR, pages 564--567, 2005.

Digital Library

[12]

K. S. McCurley. Geospatial mapping and navigation of the Web. In WWW, pages 221--229, 2001.

Digital Library

[13]

Q. Mei, C. Liu, H. Su, and C. Zhai. A probabilistic approach to spatiotemporal theme pattern mining on weblogs. In WWW, pages 533--542, 2006.

Digital Library

[14]

Y. Morimoto, M. Aono, M. E. Houle, and K. S. McCurley. Extracting spatial knowledge from the Web. In SAINT, pages 326--333, 2003.

Digital Library

[15]

D. B. Neill, A. W. Moore, and G. F. Cooper. A Bayesian spatial scan statistic. In NIPS, 2005.

[16]

S. Schockaert and M. D. Cock. Neighborhood restrictions in geographic IR. In SIGIR, pages 167--174, 2007.

Digital Library

[17]

T. Tezuka, T. Kurashima, and K. Tanaka. Toward tighter integration of Web search with a geographic information system. In WWW, pages 277--286, 2006.

Digital Library

[18]

C. Wang, X. Xie, L. Wang, Y. Lu, W.-Y. Ma. Detecting geographic locations from Web resources. In GIR, pages 17--24, 2005.

Digital Library

[19]

L. Wang, C. Wang, X. Xie, J. Forman, Y. Lu, W.-Y. Ma, and Y. Li. Detecting dominant locations from search queries. In SIGIR, pages 424--431, 2005.

Digital Library

Cited By

Gupta MJoshi MAgrawal P(2023)Deep Learning Methods for Query Auto CompletionAdvances in Information Retrieval10.1007/978-3-031-28241-6_35(341-348)Online publication date: 16-Mar-2023
https://doi.org/10.1007/978-3-031-28241-6_35
Mostafa AGad WAbdelkader TBadr N(2022)Pre-HLSA: Predicting home location for Twitter users based on sentimental analysisAin Shams Engineering Journal10.1016/j.asej.2021.05.01513:1(101501)Online publication date: Jan-2022
https://doi.org/10.1016/j.asej.2021.05.015
Blekanov IMaksimov ANepiyushchikh DBodrunova S(2022)Geolocation Detection Approaches for User Discussion Analysis in TwitterHCI International 2022 - Late Breaking Papers. Interaction in New Media, Learning and Games10.1007/978-3-031-22131-6_2(16-29)Online publication date: 25-Nov-2022
https://doi.org/10.1007/978-3-031-22131-6_2
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Index Terms

Spatial variation in search engine queries
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Conferences

WWW '08: Proceedings of the 17th international conference on World Wide Web

April 2008

1326 pages

ISBN:9781605580852

DOI:10.1145/1367497

General Chairs:
Jinpeng Huai
Beihang University, China
,
Robin Chen
AT&T Labs, USA
,
Hsiao-Wuen Hon
Microsoft Research Asia, China
,
Yunhao Liu
HK University of Science and Technology, Hong Kong
,
Program Chairs:
Wei-Ying Ma
Microsoft Research Asia, China
,
Andrew Tomkins
Yahoo! Research, USA
,
Xiaodong Zhang
The Ohio State University, USA

Copyright © 2008 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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ACM: Association for Computing Machinery

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 April 2008

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WWW '08

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WWW '08: The 17th International World Wide Web Conference

April 21 - 25, 2008

Beijing, China

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Gupta MJoshi MAgrawal P(2023)Deep Learning Methods for Query Auto CompletionAdvances in Information Retrieval10.1007/978-3-031-28241-6_35(341-348)Online publication date: 16-Mar-2023
https://doi.org/10.1007/978-3-031-28241-6_35
Mostafa AGad WAbdelkader TBadr N(2022)Pre-HLSA: Predicting home location for Twitter users based on sentimental analysisAin Shams Engineering Journal10.1016/j.asej.2021.05.01513:1(101501)Online publication date: Jan-2022
https://doi.org/10.1016/j.asej.2021.05.015
Blekanov IMaksimov ANepiyushchikh DBodrunova S(2022)Geolocation Detection Approaches for User Discussion Analysis in TwitterHCI International 2022 - Late Breaking Papers. Interaction in New Media, Learning and Games10.1007/978-3-031-22131-6_2(16-29)Online publication date: 25-Nov-2022
https://doi.org/10.1007/978-3-031-22131-6_2
Liu YShen WYao ZWang JYang ZYuan X(2021)Named Entity Location Prediction Combining Twitter and WebIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.297326133:11(3618-3633)Online publication date: 1-Nov-2021
https://doi.org/10.1109/TKDE.2020.2973261
Hasni SFaiz S(2021)Word embeddings and deep learning for location prediction: tracking Coronavirus from British and American tweetsSocial Network Analysis and Mining10.1007/s13278-021-00777-511:1Online publication date: 27-Jul-2021
https://doi.org/10.1007/s13278-021-00777-5
Ma HWang W(2021)A Label Propagation Based User Locations Prediction Algorithm in Social NetworkCyber Security10.1007/978-981-33-4922-3_12(165-174)Online publication date: 19-Jan-2021
https://doi.org/10.1007/978-981-33-4922-3_12
Mousset PPitarch YTamine L(2020)End-to-End Neural Matching for Semantic Location Prediction of TweetsACM Transactions on Information Systems10.1145/341514939:1(1-35)Online publication date: 5-Sep-2020
https://dl.acm.org/doi/10.1145/3415149
Lim JNitta NNakamura KBabaguchi N(2019)Constructing Geographic Dictionary from Streaming Geotagged TweetsISPRS International Journal of Geo-Information10.3390/ijgi80502168:5(216)Online publication date: 8-May-2019
https://doi.org/10.3390/ijgi8050216
Liao YSheng YWang J(2019)A Temporally Hierarchical Deployment Architecture for an Enhanced Name Resolution SystemApplied Sciences10.3390/app91428919:14(2891)Online publication date: 19-Jul-2019
https://doi.org/10.3390/app9142891
Bokányi EKondor DVattay G(2019)Scaling in words on TwitterRoyal Society Open Science10.1098/rsos.1900276:10(190027)Online publication date: 2-Oct-2019
https://doi.org/10.1098/rsos.190027
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