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Topics on Twitter and the Excess Demand for Used Cars

  • Yuya ShibuyaEmail author
Chapter

Abstract

In this chapter, the study investigates correlations between excess demand for used cars and topics on Twitter after the Great East Japan Earthquake and Tsunami. Based on the methodology described in Chap.  3, the author conducts Latent Dirichlet Allocation (LDA) with Twitter data after the Great East Japan Earthquake and Tsunami of 2011 and addresses whether topics frequency ratios are correlated with socio-economic activities as reflected in the excess demand for used cars. The findings of this chapter suggest that when there were more communications related to recovery and disaster damages among tweets posted by people who are local to the disaster-stricken area, there may have been more socio-economic activities in the disaster area. In contrast, when there were more communications related to evacuation, there may have been less demand for used cars. Furthermore, among tweets posted by people who are not local to the disaster-stricken area, when there was more communication about going to and supporting the disaster-stricken area, there may have been more socio-economic activities in the disaster-stricken area. The chapter is constructed as the follows: First, the author defines the research topics of this chapter in Sect. 7.1. In Sect. 7.2, the data for this chapter’s analysis will be described. In Sect. 7.3, the model is introduced and the results are shown in Sect. 7.4. Lastly, the author discusses the result in Sect. 7.5 and conclude this chapter in Sect. 7.6.

References

  1. Bica, M., Palen, L., & Bopp, C. (2017). Visual representations of disaster. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing - CSCW’17 (pp. 1262–1276).  https://doi.org/10.1145/2998181.2998212.
  2. Blei, D. (2012). Probabilistic topic models. Communications of the ACM, 55(4), 77.  https://doi.org/10.1145/2133806.2133826.CrossRefGoogle Scholar
  3. Shibuya, Y., & Tanaka, H. (2019). Detecting disaster recovery activities via social media communication topics. In Proceedings of the 16th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2019), Valencia, Spain.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Graduate School of Interdisciplinary Information StudiesUniversity of TokyoTokyoJapan

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