Weitere Artikel dieser Ausgabe durch Wischen aufrufen
When a large-scale disaster hits a community, especially a water-related disaster, there is a scarcity of automobiles and a sudden increase in the demand for used cars in the damaged areas. This paper conducts a case study of a recent massive natural disaster, the Great East Japan Earthquake and Tsunami of 2011 to understand those car scarcities and demand in the aftermath of the catastrophe. We analyze the reasons for the increase in demand for used cars and how social media can predict people’s demand for used automobiles. In other words, this paper explores whether social media data can be used as a sensor of socio-economic recovery status in damaged areas during large-scale water-related disaster-recovery phases. For this purpose, we use social media communication as a proxy for estimating indicators of people’s activities in the real world. This study conducts both qualitative analysis and quantitative analysis. For the qualitative research, we carry out semi-structured interviews with used-car dealers in the tsunami-stricken area and unveil why people in the area demanded used cars. For the quantitative analysis, we collected Facebook page communication data and used-car market data before and after the Great East Japan Earthquake and Tsunami of 2011. By combining and analyzing these two types of data, we find that social media communication correlates with people’s activities in the real world. Furthermore, this study suggests that different types of communication on social media have different types of correlations with people’s activities. More precisely, we find that social media communication related to people’s activities for rebuilding and for emotional support is positively correlated with the demand for used cars after the Great East Japan Earthquake and Tsunami. On the other hand, communication about anxiety and information seeking correlates negatively with the demand for used cars.
Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten
Sie möchten Zugang zu diesem Inhalt erhalten? Dann informieren Sie sich jetzt über unsere Produkte:
Cheng, J. W., Mitomo, H., Otsuka, T., & Jeon, S. Y. (2015). The effects of ICT and mass media in post-disaster recovery—a two model case study of the great East Japan Earthquake. Telecommunications Policy,39, 515–532. https://doi.org/10.1016/j.telpol.2015.03.006. CrossRef
Shibuya, Y., & Tanaka, H. (2017) How does a large-scale disaster impact the used-car market? In Proceedings of the 74th conference of Japan economic policy association. Tokyo.
Beigi, G., Hu, X., Maciejewski, R., & Liu, H. (2016). An overview of sentiment analysis in social media and its applications in disaster relief. In W. Pedrycz & S. M. Chen (Eds.), Sentiment analysis and ontology engineering: an environment of computational intelligence (pp. 313–340). New York: Springer Internationa Publishing. CrossRef
Castillo, C. (2016). Big crisis data—social media in disasters and time-critical situations. Cambridge: Cambridge University Press. CrossRef
Yuan, W., Guan, D., Huh, E.-N., & Lee, S. (2013). Harness human sensor networks for situational awareness in disaster reliefs: a survey. IETE Technical Review,30(3), 240–247. CrossRef
Pentland, A. (2015). Social physics: how social networks can make us smarter. New York: Penguin Books.
Gruebner, O., Lowe, S. R., Sykora, M., Shankardass, K., Subramanian, S., & Galea, S. (2017). A novel surveillance approach for disaster mental health. PLoS One,12, 1–15. https://doi.org/10.1371/journal.pone.0181233. CrossRef
Korolov, R,. Peabody, J., Lavoie, A., Das, S., Magdon-Ismail, M., & Wallace, W. (2015) Actions are louder than words in social media. In Proceedings of the 2015 IEEE/ACM international conference on advances in social networks analysis and mining 2015—ASONAM’15 (pp. 292–297). https://doi.org/10.1145/2808797.2809376.
Kelly, S., & Ahmad, K. (2014) Determining levels of urgency and anxiety during a natural disaster: noise, affect, and news in social media. In Proceeding of Lrec 2014—ninth international conference on language resources and evaluation (pp. 70–76). Reykjavik, Iceland.
Nguyen, DA., Abdelzaher, T., Borbash, S., Dang, X., Ganti, R., Singh, A., & Srivatsa, M. (2014) On critical event observability using social networks: a disaster monitoring perspective. In Proceedings 2014 IEEE military communications conference: affordable mission success: meeting the challenge (pp. 1633–1638). Baltimore.
Sakaki, T., Toriumi, F., Uchiyama, K., Matsuo, Y., Shinoda, K., Kazama, K., Kurihara, S., & Noda, I. (2013) The possibility of social media analysis for disaster management. In Proceedings of Humanitarian technology conference (R10-HTC), 2013 IEEE Region 10 (pp. 238–243). Sendai, Japan.
Sakaki, T., Okazaki, M., & Matsuo, Y. (2010) Earthquake shakes twitter users: real-time event detection by social sensors. In Proceedings of the nineteenth international WWW conference (WWW2010) (pp. 851–860). Raleigh, NC: ACM.
Zhang, N. Y., Chen, H. J., Chen, J. Y., & Chen, X. (2016). Social media meets big urban data: a case study of urban waterlogging analysis. Computational Intelligence and Neuroscience. https://doi.org/10.1155/2016/3264587.
de Albuquerque, J. P., Herfort, B., Brenning, A., & Zipf, A. (2015). A geographic approach for combining social media and authoritative data towards identifying useful information for disaster management. International Journal of Geographical Information Science,29, 667–689. https://doi.org/10.1080/13658816.2014.996567. CrossRef
Wang, Q., & Taylor, J. E. (2016). Patterns and limitations of urban human mobility resilience under the influence of multiple types of natural disaster. PLoS One,11(1), e0147299.1–e0147299.14. https://doi.org/10.1371/journal.pone.0147299.
Dong, H., Halem, M., & Zhou, SJ. (2013) Social media data analytics applied to hurricane sandy. In Proceedings of 2013 international conference on social computing (Socialcom) (pp. 963–966). Washington. https://doi.org/10.1109/socialcom.2013.152.
Martin, Y., Li, Z. L., & Cutter, S. L. (2017). Leveraging twitter to gauge evacuation compliance: spatiotemporal analysis of Hurricane Matthew. PLoS One,12(7), 7–30. https://doi.org/10.1371/journal.pone.0181701.
Kibanov, M., Stumme, G., Amin, I., & Lee, J. (2017). Aug SI mining social media to inform peatland fire and haze disaster management. Social Network Analysis and Mining,7(1), 7–30. CrossRef
Charles-Smith, L. E., Reynolds, T. L., Cameron, M. A., Conway, M., Lau, E., Olsen, J., et al. (2015). Using social media for actionable disease surveillance and outbreak management: a systematic literature review. PLoS One,10, 1–20. https://doi.org/10.1371/journal.pone.0139701. CrossRef
Chew, C., & Eysenbah, G. (2006). Pandemics in the age of twitter: content analysis of tweets during the 2009 H1N1 outbreak. Annals of the Academy of Medicine, Singapore,35, 361–367. https://doi.org/10.1371/journal.pone.0014118.
Rao, T., & Srivastava, S. (2014) Twitter sentiment analysis: how to hedge your bets in the stock markets. In F. Can, T. Özyer, F. Polat (Eds.), State of the art applications of social network analysis (pp. 227–247). New York: Springer. CrossRef
Bollen, J., Mao, H., & Pepe, A. Modeling public mood and emotion: twitter sentiment and socio-economic phenomena. In Proceedings of the fifth international AAAI conference on weblogs and social media (pp. 450–453). Barcelona.
Rui, H., Liu, Y., & Whinston, A. (2013). Whose and what chatter matters? The effect of tweets on movie sales. Decision Support Systems,55(4), 863–870. CrossRef
Dellarocas, C., Awad, NF., & Zhang, X. (2004) Exploring the value of online reviews to organizations: implications for revenue forecasting and planning. In Proceedings of ICIS 2004. 30. https://aisel.aisnet.org/icis2004/30
Tanaka, H. (2017). Social media and community activities: a quantitative structural analysis focusing on social capital. In H. Tanaka (Ed.), Research of communication to activate regional society (pp. 161–182). Kyoto: Minerva Shobo. (in Japanese).
Silver, A., & Matthews, L. (2017). The use of facebook for information seeking, decision support, and self-organization following a significant disaster. Information, Communication & Society,20(11), 1680–1697. CrossRef
Breuninger, K. (2017) Hurricane Harvey could cause a price surge for used cars. CNBC. https://www.cnbc.com/2017/08/30/hurricane-harvey-could-cause-a-price-surge-for-usedcars.html. Accessed 23 May 2018.
Chee, B. (2017) Hurricanes cause used car prices to increase. Daily news. http://www.nydailynews.com/autos/street-smarts/hurricanes-car-prices-increase-article-1.3551132 Accessed 23 May 2018.
Lamg, S. (2017) Expect used-car prices to skyrocket in the southeast post-hurricane. Car and driver. https://www.caranddriver.com/news/expect-used-car-prices-to-skyrocket-in-thesoutheast-post-hurricane Accessed 23 May 2018.
Asahi Shimbun (2011) Used-car dealers have less cheap used kei cars. Asahi Shimbun (Iwate local version). (in Japanese).
Mainichi Shimbun (2011) The great East Japan Earthquake and Tsunami: the prices of used-car increased in the damaged areas. Mainichi Shimbun. (in Japanese).
Nikkei Sangyo Shimbun (2011) The number of used-cars registered was recorded as the lowest ever. Nikkei Sangyo Shimbun. ( in Japanese).
Yomiuri Shimbun (2011) No used light motor vehicle: the prices increased in the damaged areas. Yomiuri Shimbun. ( in Japanese).
Cabinet Office (2012) Annual economic finance report. http://www5.cao.go.jp/j-j/wp/wpje12/pdf/p02021_3.pdf Accessed 23 May 2018. ( in Japanese).
Tohoku Finance Bureaus (2017) Regions’ trends of consumption. http://www5.cao.go.jp/jj/wp/wp-je12/index.html Accessed 23 May 2018. ( in Japanese).
Shibuya, Y. (2017) Mining social media for disaster management. In Proceedings of IEEE big data workshop for the 2nd international workshop on application of big data for computational social science (pp. 3029–3036). Boston.
Erlandsson, F., Nia, R., Boldt, M., Boldt, M., Johnson, H., & Wu, S. (2015) Crawling online social networks. In Proceedings of network intelligence conference. Karlskrona. https://doi.org/10.1109/enic.2015.10.
- A Statistical Analysis Between Consumer Behavior and a Social Network Service: A Case Study of Used-Car Demand Following the Great East Japan Earthquake and Tsunami of 2011
- Springer Japan
- The Review of Socionetwork Strategies
Print ISSN: 2523-3173
Elektronische ISSN: 1867-3236
Neuer Inhalt/© ITandMEDIA, Best Practices für die Mitarbeiter-Partizipation in der Produktentwicklung/© astrosystem | stock.adobe.com