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Closeness and Structure of Friends Help to Estimate User Locations

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

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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References

  1. Amitay, E., Har’El, N., Sivan, R., Soffer, A.: Web-a-where: geotagging web content. In: SIGIR, pp. 273–280. ACM (2004)

    Google Scholar 

  2. Backstrom, L., Sun, E., Marlow, C.: Find me if you can: improving geographical prediction with social and spatial proximity. In: WWW, pp. 61–70. ACM (2010)

    Google Scholar 

  3. Chandra, S., Khan, L., Muhaya, F.B.: Estimating twitter user location using social interactions-a content based approach. In: SocialCom, pp. 838–843. IEEE (2011)

    Google Scholar 

  4. Cheng, Z., Caverlee, J., Lee, K.: You are where you tweet: a content-based approach to geo-locating twitter users. In: CIKM, pp. 759–768. ACM (2010)

    Google Scholar 

  5. Cho, E., Myers, S.A., Leskovec, J.: Friendship and mobility: user movement in location-based social networks. In: SIGKDD, pp. 1082–1090. ACM (2011)

    Google Scholar 

  6. Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Phys. Rev. E70(6), 066111 (2004)

    Article  Google Scholar 

  7. Compton, R., Jurgens, D., Allen, D.: Geotagging one hundred million twitter accounts with total variation minimization (2014). arXiv preprintarxiv:1404.7152

  8. Davis Jr., C.A., Papa, G.L., de Oliveira, D.R.R., de L Arcanjo, F.: Inferring the location of twitter messages based on user relationships. Trans. GIS15(6), 735–751 (2011)

    Article  Google Scholar 

  9. Ho, T.K., Hull, J.J., Srihari, S.N.: Decision combination in multiple classifier systems. IEEE Trans. Pattern Anal. Mach. Intell.16(1), 66–75 (1994)

    Article  Google Scholar 

  10. Huberman, B.A., Romero, D.M., Fang, W.: Social networks that matter: Twitter under the microscope (2008). CoRR, abs/0812.1045

    Google Scholar 

  11. Jurgens, D.: That’s what friends are for: inferring location in online social media platforms based on social relationships. ICWSM13, 273–282 (2013)

    Google Scholar 

  12. Kong, L., Liu, Z., Huang, Y.: Spot: locating social media users based on social network context. In: Proceedings of the VLDB Endowment, vol. 7, (13), pp. 1681–1684 (2014)

    Google Scholar 

  13. Li, R., Wang, S., Deng, H., Wang, R., Chang, K.C.-C.: Towards social user profiling: unified and discriminative influence model for inferring home locations. In: SIGKDD, pp. 1023–1031. ACM (2012)

    Google Scholar 

  14. Mahmud, J., Nichols, J., Drews, C.: Home location identification of twitter users (2014). CoRR, abs/1403.2345

    Google Scholar 

  15. Sadilek, A., Kautz, H., Bigham, J.P.: Finding your friends and following them to where you are. In: WSDM, pp. 723–732. ACM (2012)

    Google Scholar 

  16. Sankaranarayanan, J., Samet, H., Teitler, B.E., Lieberman, M.D., Sperling, J.: Twitterstand: news in tweets. In: ACM SIGSPATIAL, pp. 42–51. ACM (2009)

    Google Scholar 

  17. SocialMediaToday (2013).http://socialmediatoday.com/irfan-ahmad/1854311/twitter-statistics-ipo-infographic

  18. Wang, X.F., Chen, G.: Complex networks: small-world, scale-free and beyond. IEEE Circuits Syst. Mag.3(1), 6–20 (2003)

    Article  Google Scholar 

  19. Wang, X., Liu, H., Zhang, P., Li, B.: Identifying information spreaders in twitter follower networks. Technical report TR-12-001, School of Computing, Informatics, and Decision Systems Engineering, Arizona State University (2012)

    Google Scholar 

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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 toZhi 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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Price includes VAT (Japan)
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