Abstract
In this chapter, the study investigates correlations between excess demand for used cars and public sentiment on social media communication after the Great East Japan Earthquake and Tsunami (RQ2). The results of this investigation suggest that there were statistically significant correlations between people’s sentiment on Twitter and the excess demand for used cars. In addition, the results indicate that there were different types of sentiment expressions between people local and not local to the disaster-stricken areas. Moreover, there were different types of sentiment expression on Twitter and those on Facebook pages, and thus public sentiments on Twitter and Facebook Pages have different types of relationships with the excess demand for used cars. The rest of the chapter is constructed as follows: First, the author defines this chapter’s research topics in Sect. 8.1. In Sect. 8.2, the data for this chapter’s analysis will be described. In Sect. 8.3, the model is introduced and the results are shown in Sect. 8.4. The author discuss the results in Sect. 8.5 and conclude this chapter in Sect. 8.6.
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- 1.
This chapter is written based on Shibuya and Tanaka (2019).
- 2.
As of 2017, the author was able to access all public posts on Facebook Pages. Facebook made the changes in their data with a new policy of privacy in 2018. In this study, the author uses the data collected before the changes were made.
- 3.
Because there were only a few Facebook Pages’ posts/comments which used the geo-tagged feature, the author looked into tsunami-stricken cities names’ usage on each post/comment on Facebook Pages. This study used tsunami-stricken cities’ names listed on the “Area of inundation area” by the Geospatial Information Authority of Japan (http://www.gsi.go.jp/kikaku/kikaku60004.html, accessed December 12th, 2018, in Japanese). This study only used tsunami-stricken cities’ names in Miyagi and Iwate prefectures to be consistent with data in other chapters.
- 4.
When polar words appear with denial words in one phrase (e.g., I was not happy, where “happy” is a positive word and “not” is a denial.), the word (e.g., happy) is counted as an opposite polar word (e.g., happy as negative).
References
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Shibuya, Y., & Tanaka, H. (2019). Using social media to detect socio-economic disaster recovery. IEEE Intelligent Systems, 34(3), 29–37. https://doi.org/10.1109/MIS.2019.2918245
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Shibuya, Y. (2020). Public Sentiment and the Excess Demand for Used Cars. In: Social Media Communication Data for Recovery. Springer, Singapore. https://doi.org/10.1007/978-981-15-0825-7_8
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DOI: https://doi.org/10.1007/978-981-15-0825-7_8
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