The Journal of Korean Institute of Communications and Information Sciences (한국통신학회논문지)

The Korean Institute of Commucations and Information Sciences (한국통신학회)
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Investigation of Twitter Users' Activity Radius and Home Region in the City: The Case of Las Vegas

트위터 사용자의 도시 내 활동반경과 거주지역의 탐색: 라스베이거스 사례

  • Received : 2017.01.10
  • Accepted : 2017.01.31
  • Published : 2017.02.28
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Abstract

In this study, we collected 200,578,703 geo-tweets and removed the twitter bots. Using the concept of activity radius, Twitter users are classified. Users are also divided first into domestic and overseas, and again domestic ones are divided into locals and non-locals. Statistical characteristics of activity strength and active area of Twitter users are described according to activity radius and home region, and the geographical distribution is presented visually. Through a case study of Las Vegas, we have identified the difference in activity strength and active area by the user's home residence. We expect to derive theories about human mobility by analyzing various cities with the method proposed in this study.

본 연구는 전 세계에서 발생한 200,578,703건의 지오트윗을 수집하여 트윗 봇을 제거한 후, 인간의 도시 내 이동패턴을 분석하였다. 활동반경(Activity Radius)이라는 개념을 이용하여 트위터 사용자를 구분하였으며, 거주지역을 국내와 국외로 구분하고 국내는 다시 시내와 시외로 구분하였다. 그리고 활동반경과 거주지역에 따라 트위터 사용자의 활동성과 활동지역에 대한 통계적 특성을 기술하였고 지리적 분포를 시각적으로 표현하였다. 라스베이거스를 대상으로 하는 사례 분석을 통해, 거주지역에 따른 활동성과 활동지역의 차이를 확인하였다. 향후 본 연구의 방법에 따라 다양한 도시를 대상으로 분석을 수행하면, 인간의 이동성에 대한 다양한 이론을 도출할 수 있을 것이다.

Keywords

References

  1. S. Park, Y. I. Kim, and S. R. Lee, "Hierarchical visualization of cloud-based social network service using fuzzy," J. KICS, vol. 38B, no. 7, pp. 501-511. Jul. 2013. https://doi.org/10.7840/kics.2013.38B.7.501
  2. J. Moon, I. Jang, Y. C. Choe, J. G. Kim, and G. Bock, "Case study of big data-based agri-food recommendation system according to types of customers," J. KICS, vol. 40, no. 5, pp. 903-913, May 2015. https://doi.org/10.7840/kics.2015.40.5.903
  3. J. T. Oh, "Personal environment service and technology based on smart phone," J. KICS, vol. 38C, no. 5, pp. 454-463, May 2013. https://doi.org/10.7840/kics.2013.38C.5.454
  4. J. H. Cho, "Tutorial: Geo-tweet analysis to understand mobility patterns of people," in Proc. Korea Soc. IT Serv. Conf., pp. 419-428, Seoul, Korea, May 2016.
  5. B. Hawelka, I. Sitko, E. Beinat, S. Sobolevsky, P. Kazakopoulos, and C. Ratti, "Geo-located twitter as proxy for global mobility patterns," Cartography and Geographic Inf. Sci., vol. 41, no. 3, pp. 260-271, Feb. 2014. https://doi.org/10.1080/15230406.2014.890072
  6. J. Yin, Y. Gao, Z. Du, and S. Wang, "Exploring multi-scale spatiotemporal twitter user mobility patterns with a visual-analytics approach," ISPRS Int. J. Geo-Inf., vol. 5, no. 10, id. 187, pp. 1-19, Oct. 2016. https://doi.org/10.3390/ijgi5010001
  7. J. H. Cho and I. Seo, "Comparing the spatial mobility of residents and tourists by using teotagged tweets," J. Inf. Technol. Serv., vol. 15, no. 3, pp. 211-221, Sep. 2016.
  8. M. Lenormand, B. Goncalves, A. Tugores, and J. J. Ramasco, "Human diffusion and city influence," J. The Royal Soc. Interface, vol. 12, no. 109, id. 20150473, pp. 1-9, Jul. 2015.
  9. J. Mahmud, J. Nichols, and C. Drews, "Home location identification of twitter users," ACM Trans. Intell. Syst. Technol., vol. 5, no. 3, id. 47, pp. 1-21, Jul. 2014.
  10. Z. Cheng, J. Caverlee, and K. Lee, "You are where you tweet: A content-based approach to geo-locating Twitter use," in Proc. 19th ACM Int. Conf. Inf. and Knowledge Management, pp. 759-768, Toronto, Canada, Oct. 2010.
  11. B. Hecht, L. Hong, B. Suh, and E. H. Chi, "Tweets from Justin Bieber's heart: The dynamics of the 'location' field in user profiles," in Proc. SIGCHI Conf. Human Factors in Comput. Syst., pp. 237-246, Vancouver, Canada, May 2011.
  12. J. Kulshrestha, F. Kooti, A. Nikravesh, and K. P. Gummadi, "Geographic dissection of the Twitter network," in Proc. 6th Int. AAAI Conf. on Weblogs and Social Media, pp. 202-209, Dublin, Ireland, Jun. 2012.
  13. A. Belyi, I. Bojic, S. Sobolevsky, I. Sitko, B. Hawelka, L. Rudikova, A. Kurbatski, and C. Ratti, Global multi-layer network of human mobility (2016), Retrived Jan. 6, 2017, from http://arxiv.org/abs/1601.05532.
  14. N. Hossain, T. Hu, R. Feizi, A. M. White, J. Luo, and H. Kautz, Inferring fine-grained details on user activities and home location from social media: detecting drinking-whiletweeting patterns in communities (2016), Retrived Jan. 6, 2017, from http://arxiv.org/ abs/1603.03181.
  15. C. Robusto, "The cosine-haversine formula," The Am. Math. Monthly, vol. 64, no. 1, pp. 38-40, 1957. https://doi.org/10.2307/2309088
  16. LVCVA, Las Vegas Visitor Profile Study (2015), Retrieved Jan. 6, 2017, from http://www.lvcva.com.

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