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Closeness and Structure of Friends Help to Estimate User Locations

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Database Systems for Advanced Applications (DASFAA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9643))

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Abstract

A tremendous amount of information is being shared every day on social media sites such as Facebook, Twitter or Google+. However, only a small portion of users provide their location information, which can be helpful in targeted advertising and many other services.Current methods in location estimation using social relationships consider social friendship as a simple binary relationship. However, social closeness between users and structure of friends have strong implications on geographic distances. In this paper, we introduce new measures to evaluate the social closeness between users and structure of friends. We propose models that use them for location estimation. Compared with the models which take the friend relation as a binary feature, social closeness can help identify which friend of a user is more important and friend structure can help to determine significance level of locations, thus improving the accuracy of the location estimation models. A confidence iteration method is further introduced to improve estimation accuracy and overcome the problem of scarce location information. We evaluate our methods on two different datasets, Twitter and Gowalla. The results show that our model can improve the estimation accuracy by 5 %–20 % compared with state-of-the-art friend-based models.

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Notes

  1. 1.

    http://www.pewinternet.org/2013/09/12/location-based-services/.

  2. 2.

    https://dev.twitter.com/rest/public.

  3. 3.

    https://developers.google.com/maps/documentation/geocoding/intro.

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Acknowledgments

This work is supported in part by USDOD. We would like to thank the scientists from USDOD, Dr. James Kang and Dr. Joshua Trampier, for their insights and detailed feedback on this work.

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Authors and Affiliations

  1. Computer Science and Engineering, University of North Texas, Denton, TX, USA

    Zhi Liu & Yan Huang

Authors
  1. Zhi Liu
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  2. Yan Huang
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Corresponding author

Correspondence to Zhi Liu .

Editor information

Editors and Affiliations

  1. Georgia Institute of Technology , Atlanta, Georgia, USA

    Shamkant B. Navathe

  2. University of Texas at Dallas , Richardson, Texas, USA

    Weili Wu

  3. University of Minnesota , Minneapolis, Minnesota, USA

    Shashi Shekhar

  4. Renmin University , Beijing, China

    Xiaoyong Du

  5. Fudan University , Shanghai, China

    Sean X. Wang

  6. Rutgers, The State University of New Jer , New Brunswick, New Jersey, USA

    Hui Xiong

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Liu, Z., Huang, Y. (2016). Closeness and Structure of Friends Help to Estimate User Locations. In: Navathe, S., Wu, W., Shekhar, S., Du, X., Wang, S., Xiong, H. (eds) Database Systems for Advanced Applications. DASFAA 2016. Lecture Notes in Computer Science(), vol 9643. Springer, Cham. https://doi.org/10.1007/978-3-319-32049-6_3

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  • DOI: https://doi.org/10.1007/978-3-319-32049-6_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-32048-9

  • Online ISBN: 978-3-319-32049-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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