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

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Web and Wireless Geographical Information Systems (W2GIS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,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.

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Notes

  1. 1.

    https://data.worldbank.org/indicator/ST.INT.ARVL?end=2016&start=1995&type=shaded&view=chart.

  2. 2.

    https://www.tourism.jp/en/tourism-database/stats/inbound/.

  3. 3.

    https://www.businessinsider.com/20-percent-of-yelp-reviews-fake-2013-9.

  4. 4.

    http://kyoto-kanko.net/kyoto-spot/kyoto-spot-847/.

  5. 5.

    http://www.internetlivestats.com/twitter-statistics/.

  6. 6.

    https://developer.twitter.com/en/docs/tweets/filter-realtime/overview.

  7. 7.

    https://github.com/mouuff/mtranslate.

  8. 8.

    https://www.argotrans.com/blog/accurate-google-translate-2018/.

  9. 9.

    http://www.stat.go.jp/english/data/mesh/05.html.

  10. 10.

    https://github.com/RaRe-Technologies/gensim.

  11. 11.

    https://www.mturk.com/.

  12. 12.

    https://bl.ocks.org/wannita901/raw/6751411e7208f05ce0a2214b9edde79b/.

  13. 13.

    https://www.chula.ac.th/en/contact/map-and-directions/.

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Acknowledgements

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

Author information

Authors and Affiliations

  1. Chulalongkorn University, 254 Phayathai Road, Pathumwan, Bangkok, Thailand

    Wannita Takerngsaksiri

  2. NAIST, 8916-5 Takayama-cho, Ikoma, Nara, 630-0192, Japan

    Shoko Wakamiya & Eiji Aramaki

Authors
  1. Wannita Takerngsaksiri
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  2. Shoko Wakamiya
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  3. Eiji Aramaki
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Corresponding author

Correspondence to Wannita Takerngsaksiri .

Editor information

Editors and Affiliations

  1. Kyoto Sangyo University, Kyoto, Japan

    Yukiko Kawai

  2. University of Konstanz, Konstanz, Germany

    Sabine Storandt

  3. Kwansei Gakuin University, Sanda, Japan

    Kazutoshi Sumiya

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Takerngsaksiri, W., Wakamiya, S., Aramaki, E. (2019). City Link: Finding Similar Areas in Two Cities Using Twitter Data. In: Kawai, Y., Storandt, S., Sumiya, K. (eds) Web and Wireless Geographical Information Systems. W2GIS 2019. Lecture Notes in Computer Science(), vol 11474. Springer, Cham. https://doi.org/10.1007/978-3-030-17246-6_2

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  • DOI: https://doi.org/10.1007/978-3-030-17246-6_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-17245-9

  • Online ISBN: 978-3-030-17246-6

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