Skip to main content

Location2Vec: Generating Distributed Representation of Location by Using Geo-tagged Microblog Posts

  • Conference paper
  • First Online:
Social Informatics (SocInfo 2018)

Abstract

This paper proposes a method to represent the characteristics of a place (i.e., use of the venue, atmosphere of the area) by using geo-tagged microblog posts around the place. It enables a vector representation of a location similar to the distributed representation of a term in Word2Vec. Our method uses a simple neural network that is trained through the task of estimating the terms that appear in tweets posted from the area. The effectiveness of our method is illustrated through an experiment of a comparison of similar locations in Tokyo and Kyoto.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Aggarwal, C.C., Abdelzaher, T.: Social sensing. In: Aggarwal, C. (eds.) Managing and mining sensor data, pp. 237–297. Springer, Heidelberg (2013)

    Google Scholar 

  2. Ahmed, A., Hong, L., Smola, A.J.: Hierarchical geographical modeling of user locations from social media posts. In: Proceedings of the 22Nd International Conference on World Wide Web, pp. 25–36, WWW 2013. ACM, New York (2013). https://doi.org/10.1145/2488388.2488392

  3. Alcorn, M.A.: (batter|pitcher)2vec: statistic-free talent modeling with neural player embeddings. In: MIT Sloan Sports Analytics Conference, p. 5435 (2016)

    Google Scholar 

  4. Canh, T.V., Gertz, M.: A spatial LDA model for discovering regional communities. In: 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013), pp. 162–168, August 2013. https://doi.org/10.1109/ASONAM.2013.6785703

  5. Dhingra, B., Zhou, Z., Fitzpatrick, D., Muehl, M., Cohen, W.W.: Tweet2Vec: character-based distributed representations for social media. In: The 54th Annual Meeting of the Association for Computational Linguistics, p. 269 (2016)

    Google Scholar 

  6. Doran, D., Gokhale, S., Dagnino, A.: Human sensing for smart cities. In: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 1323–1330, ASONAM 2013. ACM, New York (2013). https://doi.org/10.1145/2492517.2500240

  7. Giridhar, P., Wang, S., Abdelzaher, T., Al Amin, T., Kaplan, L.: Social fusion: integrating Twitter and Instagram for event monitoring. In: 2017 IEEE International Conference on Autonomic Computing (ICAC), pp. 1–10. IEEE (2017)

    Google Scholar 

  8. Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864. ACM (2016)

    Google Scholar 

  9. Kamath, K.Y., Caverlee, J., Lee, K., Cheng, Z.: Spatio-temporal dynamics of online memes: a study of geo-tagged tweets, pp. 667–678 (2013)

    Google Scholar 

  10. Kato, M.P., Ohshima, H., Oyama, S., Tanaka, K.: Query by analogical example: relational search using web search engine indices. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, pp. 27–36. ACM (2009)

    Google Scholar 

  11. Kurashima, T., Iwata, T., Hoshide, T., Takaya, N., Fujimura, K.: Geo topic model: joint modeling of user’s activity area and interests for location recommendation. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, pp. 375–384. ACM (2013)

    Google Scholar 

  12. Lee, R., Sumiya, K.: Measuring geographical regularities of crowd behaviors for twitter-based geo-social event detection. In: Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Location Based Social Networks, pp. 1–10. ACM (2010)

    Google Scholar 

  13. Liu, Y., et al.: Social sensing: a new approach to understanding our socioeconomic environments. Ann. Assoc. Am. Geogr. 105(3), 512–530 (2015)

    Article  Google Scholar 

  14. Madjiheurem, S., Qu, L., Walder, C.: Chord2Vec: learning musical chord embeddings. In: Proceedings of the Constructive Machine Learning Workshop at 30th Conference on Neural Information Processing Systems (NIPS 2016), Barcelona, Spain (2016)

    Google Scholar 

  15. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)

  16. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)

    Google Scholar 

  17. Moody, C.E.: Mixing Dirichlet topic models and word embeddings to make lda2vec. CoRR abs/1605.02019 (2016). http://arxiv.org/abs/1605.02019

  18. Ristoski, P., Paulheim, H.: RDF2Vec: RDF graph embeddings for data mining. In: Groth, P., et al. (eds.) ISWC 2016. LNCS, vol. 9981, pp. 498–514. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46523-4_30

    Chapter  Google Scholar 

  19. Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes Twitter users: real-time event detection by social sensors, pp. 851–860 (2010)

    Google Scholar 

  20. Seki, Y.: Use of Twitter for analysis of public sentiment for improvement of local government service. In: 2016 IEEE International Conference on Smart Computing (SMARTCOMP), pp. 1–3, May 2016. https://doi.org/10.1109/SMARTCOMP.2016.7501726

  21. Sheng, X., Tang, J., Xiao, X., Xue, G.: Sensing as a service: challenges, solutions and future directions. IEEE Sens. J. 13(10), 3733–3741 (2013)

    Article  Google Scholar 

  22. Vosoughi, S., Vijayaraghavan, P., Roy, D.: Tweet2Vec: learning tweet embeddings using character-level CNN-LSTM encoder-decoder. In: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, pp. 1041–1044. ACM (2016)

    Google Scholar 

  23. Wakamiya, S., Jatowt, A., Kawai, Y., Akiyama, T.: Analyzing global and pairwise collective spatial attention for geo-social event detection in microblogs. In: Proceedings of the 25th International Conference Companion on World Wide Web, pp. 263–266, WWW 2016 Companion, International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland (2016). https://doi.org/10.1145/2872518.2890551

  24. Wang, Y., Wang, T., Tsou, M.H., Li, H., Jiang, W., Guo, F.: Mapping dynamic urban land use patterns with crowdsourced geo-tagged social media (Sina-Weibo) and commercial points of interest collections in Beijing, China. Sustainability 8(11), 1202 (2016)

    Article  Google Scholar 

  25. Yan, B., Janowicz, K., Mai, G., Gao, S.: From ITDL to Place2Vec: reasoning about place type similarity and relatedness by learning embeddings from augmented spatial contexts. In: Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 35:1–35:10, SIGSPATIAL 2017. ACM, New York (2017). https://doi.org/10.1145/3139958.3140054

  26. Yin, Z., Cao, L., Han, J., Zhai, C., Huang, T.: Geographical topic discovery and comparison. In: Proceedings of the 20th International Conference on World Wide Web, pp. 247–256. ACM (2011)

    Google Scholar 

Download references

Acknowledgements

This work was supported by JSPS KAKENHI Grant Numbers JP18K18161, JP17K17832, JP18KT0097, JP16H02906, JP16H01756, JP17H00762, JP18H03243.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yoshiyuki Shoji .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Shoji, Y., Takahashi, K., Dürst, M.J., Yamamoto, Y., Ohshima, H. (2018). Location2Vec: Generating Distributed Representation of Location by Using Geo-tagged Microblog Posts. In: Staab, S., Koltsova, O., Ignatov, D. (eds) Social Informatics. SocInfo 2018. Lecture Notes in Computer Science(), vol 11186. Springer, Cham. https://doi.org/10.1007/978-3-030-01159-8_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-01159-8_25

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01158-1

  • Online ISBN: 978-3-030-01159-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics