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

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


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.


City similarity Crowd-sourcing Doc2Vec Map visualization Social media 



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


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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

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