Advertisement

Leveraging Local Interactions for Geolocating Social Media Users

  • Mohammad EbrahimiEmail author
  • Elaheh ShafieiBavani
  • Raymond Wong
  • Fang Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10939)

Abstract

Predicting the geolocation of social media users is one of the core tasks in many applications, such as rapid disaster response, targeted advertisement, and recommending local events. In this paper, we introduce a new approach for user geolocation that unifies users’ social relationships, textual content, and metadata. Our two key contributions are as follows: (1) We leverage semantic similarity between users’ posts to predict their geographic proximity. To achieve this, we train and utilize a powerful word embedding model over millions of tweets. (2) To deal with isolated users in the social graph, we utilize a stacking-based learning approach to predict users’ locations based on their tweets’ textual content and metadata. Evaluation on three standard Twitter benchmark datasets shows that our approach outperforms state-of-the-art user geolocation methods.

Keywords

Geolocation Twitter Local intreractions 

References

  1. 1.
    Ashktorab, Z., Brown, C., Nandi, M., Culotta, A.: Tweedr: mining twitter to inform disaster response. In: ISCRAM 2014 (2014)Google Scholar
  2. 2.
    Cha, M., Gwon, Y., Kung, H.T.: Twitter geolocation and regional classification via sparse coding. In: ICWSM 2015, pp. 582–585 (2015)Google Scholar
  3. 3.
    Cheng, Z., Caverlee, J., Lee, K.: You are where you tweet: a content-based approach to geo-locating twitter users. In: CIKM 2010, pp. 759–768. ACM (2010)Google Scholar
  4. 4.
    Compton, R., Jurgens, D., Allen, D.: Geotagging one hundred million twitter accounts with total variation minimization. In: BigData 2014, pp. 393–401. IEEE (2014)Google Scholar
  5. 5.
    Davis Jr., C.A., Pappa, G.L., Rocha de Oliveira, D.R., Arcanjo, F.L.: Inferring the location of twitter messages based on user relationships. Trans. GIS 15(6), 735–751 (2011)CrossRefGoogle Scholar
  6. 6.
    Ebrahimi, M., ShafieiBavani, E., Wong, R., Chen, F.: Exploring celebrities on inferring user geolocation in twitter. In: Kim, J., Shim, K., Cao, L., Lee, J.-G., Lin, X., Moon, Y.-S. (eds.) PAKDD 2017. LNCS (LNAI), vol. 10234, pp. 395–406. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-57454-7_31CrossRefGoogle Scholar
  7. 7.
    Ebrahimi, M., ShafieiBavani, E., Wong, R., Chen, F.: Twitter user geolocation by filtering of highly mentioned users. JASIST (2018).  https://doi.org/10.1002/asi.24011
  8. 8.
    Eisenstein, J., O’Connor, B., Smith, N.A., Xing, E.P.: A latent variable model for geographic lexical variation. In: EMNLP 2010, pp. 1277–1287. ACL (2010)Google Scholar
  9. 9.
    Ester, M., Kriegel, H.-P., Sander, J., Xu, X., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD 1996, vol. 96, pp. 226–231 (1996)Google Scholar
  10. 10.
    Han, B., Baldwin,T.: Lexical normalisation of short text messages: Makn sens a# twitter. In: ACL-HLT 2011, pp. 368–378. ACL (2011)Google Scholar
  11. 11.
    Han, B., Cook, P., Baldwin, T.: Geolocation prediction in social media data by finding location indicative words. In: COLING 2012, pp. 1045–1062 (2012)Google Scholar
  12. 12.
    Han, B., Cook, P., Baldwin, T.: Text-based twitter user geolocation prediction. Artif. Intell. Res. 49, 451–500 (2014)Google Scholar
  13. 13.
    Han, B., Hugo, A., Rahimi, A., Derczynski, L., Baldwin, T.: Twitter geolocation prediction shared task of the 2016 workshop on noisy user-generated text. In: WNUT 2016, pp. 213–217 (2016)Google Scholar
  14. 14.
    Hecht, B., Hong, L., Suh, B., Chi, E.H.: Tweets from Justin Bieber’s heart: the dynamics of the location field in user profiles. In: ACM SIGCHI 2011, pp. 237–246. ACM (2011)Google Scholar
  15. 15.
    Hong, L., Ahmed, A., Gurumurthy, S., Smola, A.J., Tsioutsiouliklis, K.: Discovering geographical topics in the twitter stream. In: WWW 2012, pp. 769–778. ACM (2012)Google Scholar
  16. 16.
    Hulden, M., Silfverberg, M., Francom, J.: Kernel density estimation for text-based geolocation. In: AAAI 2015, pp. 145–150 (2015)Google Scholar
  17. 17.
    Jayasinghe, G., Jin, B., Mchugh, J., Robinson, B., Wan, S.: Csiro data61 at the WNUT geo shared task. In: WNUT 2016, pp. 218–226 (2016)Google Scholar
  18. 18.
    Jurgens, D.: That’s what friends are for: inferring location in online social media platforms based on social relationships. In: ICWSM 2013, vol. 13, pp. 273–282 (2013)Google Scholar
  19. 19.
    Kusner, M.J., Sun, Y., Kolkin, N.I., Weinberger, K.Q.: From word embeddings to document distances. In: ICML 2015, pp. 957–966 (2015)Google Scholar
  20. 20.
    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 2012, pp. 1023–1031. ACM (2012)Google Scholar
  21. 21.
    Lian, D., Ge, Y., Zhang, F., Yuan, N.J., Xie, X., Zhou, T., Rui, Y.: Content-aware collaborative filtering for location recommendation based on human mobility data. In: ICDM 2015, pp. 261–270. IEEE (2015)Google Scholar
  22. 22.
    Liu, J., Inkpen, D.: Estimating user location in social media with stacked denoising auto-encoders. In: NAACL-HLT 2015, pp. 201–210 (2015)Google Scholar
  23. 23.
    Mahmud, J., Nichols, J., Drews, C.: Where is this tweet from? inferring home locations of twitter users. In: ICWSM 2012, vol. 12, pp. 511–514 (2012)Google Scholar
  24. 24.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: NIPS 2013, pp. 3111–3119 (2013)Google Scholar
  25. 25.
    Miura, Y., Taniguchi, M., Taniguchi, T., Ohkuma, T.: A simple scalable neural networks based model for geolocation prediction in twitter. In: WNUT 2016, pp. 235–239 (2016)Google Scholar
  26. 26.
    Qiang, J., Chen, P., Wang, T., Wu, X.: Topic modeling over short texts by incorporating word embeddings. In: Kim, J., Shim, K., Cao, L., Lee, J.-G., Lin, X., Moon, Y.-S. (eds.) PAKDD 2017. LNCS (LNAI), vol. 10235, pp. 363–374. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-57529-2_29CrossRefGoogle Scholar
  27. 27.
    Rahimi, A., Baldwin, T., Cohn, T.: Continuous representation of location for geolocation and lexical dialectology using mixture density networks. In: EMNLP 2017, pp. 167–176. ACL (2017)Google Scholar
  28. 28.
    Rahimi, A., Cohn, T., Baldwin, T.: Twitter user geolocation using a unified text and network prediction model. In: ACL-IJCNLP 2015, pp. 630–636. ACL (2015)Google Scholar
  29. 29.
    Rahimi, A., Cohn, T., Baldwin, T.: A neural model for user geolocation and lexical dialectology. In: ACL 2017 (2017)Google Scholar
  30. 30.
    Rahimi, A., Vu, D., Cohn, T., Baldwin, T.: Exploiting text and network context for geolocation of social media users. In: NAACL-HLT 2015, pp. 1362–1367. ACL (2015)Google Scholar
  31. 31.
    Roller, S., Speriosu, M., Rallapalli, S., Wing, B., Baldridge, J.: Supervised text-based geolocation using language models on an adaptive grid. In: EMNLP-CONLL 2012, pp. 1500–1510. ACL (2012)Google Scholar
  32. 32.
    Talukdar, P.P., Crammer, K.: New regularized algorithms for transductive learning. In: Buntine, W., Grobelnik, M., Mladenić, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009. LNCS (LNAI), vol. 5782, pp. 442–457. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-04174-7_29CrossRefGoogle Scholar
  33. 33.
    Wang, F., Lu, C.-T., Qu, Y., Yu, P.S.: Collective geographical embedding for geolocating social network users. In: Kim, J., Shim, K., Cao, L., Lee, J.-G., Lin, X., Moon, Y.-S. (eds.) PAKDD 2017. LNCS (LNAI), vol. 10234, pp. 599–611. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-57454-7_47CrossRefGoogle Scholar
  34. 34.
    Wing, B., Baldridge, J.: Hierarchical discriminative classification for text-based geolocation. In: EMNLP 2014, pp. 336–348. ACL (2014)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Mohammad Ebrahimi
    • 1
    • 2
    Email author
  • Elaheh ShafieiBavani
    • 1
    • 2
  • Raymond Wong
    • 1
    • 2
  • Fang Chen
    • 1
    • 2
  1. 1.School of Computer Science and EngineeringUniversity of New South WalesSydneyAustralia
  2. 2.Data61-CSIROSydneyAustralia

Personalised recommendations