Advertisement

City Link: Finding Similar Areas in Two Cities Using Twitter Data

  • Wannita TakerngsaksiriEmail author
  • Shoko Wakamiya
  • Eiji Aramaki
Conference paper
  • 212 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11474)

Abstract

Today in our increasingly globalized world, the number of people travelling overseas is increasing. A system that helps overseas travelers by providing information related to unfamiliar places has been earnestly sought. This study develops such a system by exploiting user-generated data over a popular social network platform: Twitter. We propose the use of natural language processing (NLP) as a method of estimating location similarity between areas in different cities. Finally, location similarity is visualized on a map. Our experiment is conducted at two popular sightseeing cities: Bangkok, Thailand and Kyoto, Japan. Our evaluation using crowd-sourcing-based 1,000 questionnaires empirically demonstrated that the proposed method can find similar places in the two cities. This result demonstrated the fundamental feasibility of our approach.

Keywords

City similarity Crowd-sourcing Doc2Vec Map visualization Social media 

Notes

Acknowledgements

This work was partly supported by MIC SCOPE #171507010, AMED under Grant Number JP16fk0108119, and JSPS KAKENHI Grant Numbers JP16K16057 and JP16H01722.

References

  1. 1.
    Lynch, K.: The Image of the City. MIT Press, Cambridge (1960)Google Scholar
  2. 2.
    Nold, C.: Greenwich Emotion Map. http://www.emotionmap.net/ (2005)
  3. 3.
    Quercia, D., Schifanella, R., Aiello, L.M.: The shortest path to happiness: recommending beautiful, quiet, and happy routes in the city. In: Proceedings of Conference on Hypertext and Social Media (HyperText) (2014)Google Scholar
  4. 4.
    Quercia, D., Schifanella, R., Aiello, L.M., McLean, K.: Smelly maps: the digital life of urban smellscapes. In: Proceedings of the Ninth International AAAI Conference on Web and Social Media, pp. 327–336 (2015)Google Scholar
  5. 5.
    Aiello, L.M., Schifanella, R., Quercia, D., Aletta, F.: Chatty maps: constructing sound maps of urban areas from social media data. Roy. Soc. Open Sci. 3(3), 1–19 (2016)MathSciNetGoogle Scholar
  6. 6.
    Culotta, A.: Towards detecting influenza epidemics by analyzing Twitter messages. In: Proceedings of the First Workshop on Social Media Analytics, pp. 115–122 (2010)Google Scholar
  7. 7.
    Amador Diaz Lopez, J., Collignon-Delmar, S., Benoit, K., et al.: Predicting the Brexit vote by tracking and classifying public opinion using Twitter data. Stat. Polit. Policy 8(1), 85–104 (2017). Accessed 24 Aug 2018.  https://doi.org/10.1515/spp-2017-0006
  8. 8.
    Gerber, M.S.: Predicting crime using Twitter and Kernel density estimation. Decis. Support Syst. 61, 115–125 (2014).  https://doi.org/10.1016/j.dss.2014.02.003CrossRefGoogle Scholar
  9. 9.
    Tumasjan, A., Sprenger, T.O., Sandner, P.G., Welpe, I.M.: Predicting elections with Twitter: what 140 characters reveal about political sentiment. In: International AAAI Conference on Weblogs and Social Media, Washington, DC (2010)Google Scholar
  10. 10.
    Hahmann, S., Purves, R., Burghardt, D.: Twitter location (sometimes) matters: exploring the relationship between georeferenced tweet content and nearby feature classes. J. Spat. Inf. Sci. Number 9, 1–36 (2014)Google Scholar
  11. 11.
    Preotiuc-Pietro, D., Cranshaw, J., Yano, T.: Exploring venue-based city-to-city similarity measures. In: Proceedings of the Second ACM SIGKDD International Workshop on Urban Computing, Article No. 16 (2013)Google Scholar
  12. 12.
    Kato, M.P., Hiroaki, O., Oyama, S., Tanaka, K.: Search as if you were in your home town: geographic search by regional context and dynamic feature-space selection. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 1541–1544 (2010)Google Scholar
  13. 13.
    Seth, R., Covell, M., Ravichandran, D., Sivakumar, D., Baluja, S.: A tale of two (similar) cities: inferring city similarity through geo-spatial query log analysis. In: Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (2011)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Wannita Takerngsaksiri
    • 1
    Email author
  • Shoko Wakamiya
    • 2
  • Eiji Aramaki
    • 2
  1. 1.Chulalongkorn UniversityBangkokThailand
  2. 2.NAISTIkomaJapan

Personalised recommendations