Location Prediction in Social Networks

  • Rong Liu
  • Guanglin Cong
  • Bolong Zheng
  • Kai Zheng
  • Han SuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10988)


User locations in social networks are needed in many applications which utilize location information to recommend local news and places of interest to users, as well as detect and alert emergencies around users. However, considering individual privacy, only a small portion users share their location on social networks. Thus, to predict the fine-grained locations of user tweets, we present a joint model containing three sub models: content-based model, social relationship based model and behavior habit based model. In the content-based model, we filter out those location-independent tweets and use deep learning algorithm to mine the relationship between semantics and locations. User trajectory similarity measure is used to build a social graph for users, and historical check-ins is used to provide users’ daily activity habits. We conduct experiments using tweets collected from Shanghai during one year. The result shows that our joint model perform well, especially the content-based model. We find that our approach improves accuracy compared to the state-of-the-art location prediction algorithm.


  1. 1.
    Chandra, S., Khan, L., Muhaya, F.B.: Estimating Twitter user location using social interactions-a content based approach. In: IEEE Third International Conference on Privacy, Security, Risk and Trust, pp. 838–843 (2012)Google Scholar
  2. 2.
    Cheng, Z., Caverlee, J., Lee, K.: You are where you tweet: a content-based approach to geo-locating Twitter users, vol. 19, no. 4, pp. 759–768 (2010)Google Scholar
  3. 3.
    Crandall, D.J., Backstrom, L., Huttenlocher, D., Kleinberg, J.: Mapping the world’s photos. In: International Conference on World Wide Web, pp. 761–770 (2009)Google Scholar
  4. 4.
    Ichiye, T., Karplus, M.: Collective motions in proteins: a covariance analysis of atomic fluctuations in molecular dynamics and normal mode simulations. Proteins Struct. Funct. Bioinf. 11(3), 205 (1991)CrossRefGoogle Scholar
  5. 5.
    Kearney, J.K., Hansen, S.: Stream editing for animation (1990)Google Scholar
  6. 6.
    Kim,Y.: Convolutional neural networks for sentence classification. CoRR, abs/1408.5882 (2014)Google Scholar
  7. 7.
    Kim, Y., Chiu, Y., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. CoRR, abs/1405.3515 (2014)Google Scholar
  8. 8.
    Kong, L., Liu, Z., Huang, Y.: SPOT: locating social media users based on social network context. VLDB Endow. 7, 1681–1684 (2014)CrossRefGoogle Scholar
  9. 9.
    Lee, K., Ganti, R.K., Srivatsa, M., Liu, L.: When Twitter meets foursquare: tweet location prediction using foursquare. In: International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, pp. 198–207 (2014)Google Scholar
  10. 10.
    Lin, S.K., Sheng-Zhi, L.I., Qiao, J.Z., Yang, D.: Markov location prediction based on user mobile behavior similarity clustering. J. Northeast. Univ. (2016)Google Scholar
  11. 11.
    Backstrom, L., Sun, E., Marlow, C.: Find me if you can: improving geographical prediction with social and spatial proximity. In: International Conference on World Wide Web, pp. 61–70 (2010)Google Scholar
  12. 12.
    Gasparini, M.: Markov chain Monte Carlo in practice. Technometrics 39(3), 338 (1997)CrossRefGoogle Scholar
  13. 13.
    Robinson, M.: The temporal development of collision cascades in the binary collision approximation. Nucl. Inst. Methods Phys. Res. B 48(1–4), 408–413 (1990)CrossRefGoogle Scholar
  14. 14.
    Sadilek, A., Kautz, H., Bigham, J. P.: Finding your friends and following them to where you are. In: ACM International Conference on Web Search and Data Mining, pp. 723–732 (2012)Google Scholar
  15. 15.
    Serdyukov, P., Murdock, V., Zwol, R.V.: Placing flickr photos on a map, pp. 484–491 (2009)Google Scholar
  16. 16.
    Xu, D., Yang, S.: Location prediction in social media based on contents and graphs. In: International Conference on Communication Systems Network Technologies, pp. 1177–1181 (2014)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Rong Liu
    • 1
  • Guanglin Cong
    • 1
  • Bolong Zheng
    • 2
    • 3
  • Kai Zheng
    • 1
  • Han Su
    • 1
    Email author
  1. 1.Big Data Reaserch CenterUniversity of Electronic Science and Technology of ChinaChengduChina
  2. 2.School of Data and Computer ScienceSun Yat-sen UniversityGuangzhouChina
  3. 3.Department of Computer ScienceAalborg UniversityAalborgDenmark

Personalised recommendations