Tweet Location Inference Based on Contents and Temporal Association

  • Saki Ueda
  • Yuto Yamaguchi
  • Hiroyuki Kitagawa
  • Toshiyuki Amagasa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9419)


How can we infer a tweet location? Are timestamps of tweets effective for the location inference? In this study, we propose a novel method for tweet location inference based on contents and timestamps of tweets. It is important to infer the locations of tweets for the services related to locations such as recommending restaurants, sending disaster-related information to users, and providing commercial messages to users. This study has two contributions: (1) we propose a novel method to infer tweet locations based on the contents and timestamps of tweets, and (2) we experimentally demonstrate the effectiveness of the proposed method using Twitter data. The experimental results suggest that the proposed method can infer tweet locations more precisely than a baseline that does not take the temporal association into account.


Location inference Twitter 



This research was partly supported by the program “Research and Development on Real World Big Data Integration and Analysis” of the Ministry of Education, Culture, Sports, Science and Technology, Japan.


  1. 1.
    Cheng, Z., Caverlee, J., Lee, K.: You are where you tweet: a content-based approach to geo-locating twitter users. In: Proceedings of the CIKM 2010, pp. 759–768 (2010)Google Scholar
  2. 2.
    Chang, H.-W., Lee, D., Eltaher, M., Lee, J.: @Phillies tweeting from philly? predicting twitter user locations with spatial word usage. In: Proceedings of the ASONAM 2012, pp. 111–118 (2012)Google Scholar
  3. 3.
    Backstrom, L., Sun, E., Marlow, C.: Find me if you can: improving geographical prediction with social and spatial proximity. In: Proceedings of the WWW 2010, pp. 61–70 (2010)Google Scholar
  4. 4.
    Kinsella, S., Murdock, V., O’Hare, N.: ‘I’m eating a sandwich in glasgow’: modeling locations with tweets. In: Proceedings of the SMUC 2011, pp. 61–68 (2011)Google Scholar
  5. 5.
    Zhou, D., Bousquet, O., Lal, T.N., Weston, J., Schölkopf, B.: Learning with local and global consistency. In: Proceeding of the NIPS 16, pp. 321–328 (2003)Google Scholar
  6. 6.
    Ikawa, Y., Enoki, M., Tatsubori, M.: Location inference using microblog messages. In: Proceedings of the WWW 2012, pp. 687–690 (2012)Google Scholar
  7. 7.
    Yamaguchi, Y., Amagasa, T., Kitagawa, H., Ikawa, Y.: Online user location inference exploiting spatiotemporal correlations in social streams. In: Proceedings of the CIKM 2014, pp. 1139–1148 (2014)Google Scholar
  8. 8.
    Eisenstein, J., O’Connor, B., Smith, N.A., Xing, E.P.: A latent variable model for geographic lexical variation. In: Proceedings of th EMNLP 2010, pp. 1277–1287 (2010)Google Scholar
  9. 9.
    Abrol, S., Khan, L.: Tweethood: agglomerative clustering on fuzzy k-closest friends with variable depth for location mining. In: Proceedings of the IEEE Second International Conference on Social Computing (SocialCom 2010), pp. 153–160 (2010)Google Scholar
  10. 10.
    Jurgens, D.: That’s what friends are for: inferring location in online social media platforms based on social relationships. In: Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media, pp. 273–282 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Saki Ueda
    • 1
  • Yuto Yamaguchi
    • 1
  • Hiroyuki Kitagawa
    • 1
  • Toshiyuki Amagasa
    • 1
  1. 1.University of TsukubaTsukubaJapan

Personalised recommendations