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

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.

[1]  Xin Lu,et al.  Network Structure and Community Evolution on Twitter: Human Behavior Change in Response to the 2011 Japanese Earthquake and Tsunami , 2014, Scientific Reports.

[2]  Sanmay Das,et al.  Actions are louder than words in social media , 2015, 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

[3]  G. Eysenbach,et al.  Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak , 2010, PloS one.

[4]  C. Castillo,et al.  Big Crisis Data: Social Media in Disasters and Time-Critical Situations , 2019 .

[5]  Milton Halem,et al.  Social Media Data Analytics Applied to Hurricane Sandy , 2013, 2013 International Conference on Social Computing.

[6]  Martin Boldt,et al.  Crawling Online Social Networks , 2015, 2015 Second European Network Intelligence Conference.

[7]  Somanath Tripathy,et al.  A trust-based collaborative access control model with policy aggregation for online social networks , 2017, Social Network Analysis and Mining.

[8]  Amit P. Sheth,et al.  Citizen Sensing, Social Signals, and Enriching Human Experience , 2009, IEEE Internet Computing.

[9]  Jinhui Qin,et al.  Real-Time Earthquake Intensity Estimation Using Streaming Data Analysis of Social and Physical Sensors , 2017, Pure and Applied Geophysics.

[10]  Alexander Zipf,et al.  A geographic approach for combining social media and authoritative data towards identifying useful information for disaster management , 2015, Int. J. Geogr. Inf. Sci..

[11]  Satoshi Kurihara,et al.  The possibility of social media analysis for disaster management , 2013, 2013 IEEE Region 10 Humanitarian Technology Conference.

[12]  Gerd Stumme,et al.  Mining social media to inform peatland fire and haze disaster management , 2017, Social Network Analysis and Mining.

[13]  Rigoberto A. Lopez,et al.  The impact of social media conversations on consumer brand choices , 2014, Marketing Letters.

[14]  Melinda Laituri,et al.  On Line Disaster Response Community: People as Sensors of High Magnitude Disasters Using Internet GIS , 2008, Sensors.

[15]  M. Shigematsu,et al.  Using Social Media for Actionable Disease Surveillance and Outbreak Management: A Systematic Literature Review , 2015, PloS one.

[16]  Amber Silver,et al.  The use of Facebook for information seeking, decision support, and self-organization following a significant disaster , 2017 .

[17]  Morten Lykkegaard Christensen,et al.  Membrane filtration device for studying compression of fouling layers in membrane bioreactors , 2017, PloS one.

[18]  Saket Srivastava,et al.  Twitter Sentiment Analysis: How To Hedge Your Bets In The Stock Markets , 2012, ArXiv.

[19]  J. Brownstein,et al.  Social and news media enable estimation of epidemiological patterns early in the 2010 Haitian cholera outbreak. , 2012, The American journal of tropical medicine and hygiene.

[20]  Johan Bollen,et al.  Modeling Public Mood and Emotion: Twitter Sentiment and Socio-Economic Phenomena , 2009, ICWSM.

[21]  J. Fowler,et al.  Rapid assessment of disaster damage using social media activity , 2016, Science Advances.

[22]  Menon Ku.  SARS revisited: managing "outbreaks" with "communications". , 2006 .

[23]  M. Johnson,et al.  Circulating microRNAs in Sera Correlate with Soluble Biomarkers of Immune Activation but Do Not Predict Mortality in ART Treated Individuals with HIV-1 Infection: A Case Control Study , 2015, PloS one.

[24]  Xiangyang Guan,et al.  Using social media data to understand and assess disasters , 2014, Natural Hazards.

[25]  Sandro Galea,et al.  A novel surveillance approach for disaster mental health , 2017, PloS one.

[26]  Yutaka Matsuo,et al.  Earthquake shakes Twitter users: real-time event detection by social sensors , 2010, WWW '10.

[27]  Ghazaleh Beigi,et al.  An Overview of Sentiment Analysis in Social Media and Its Applications in Disaster Relief , 2016, Sentiment Analysis and Ontology Engineering.

[28]  John W. Cheng,et al.  The effects of ICT and mass media in post-disaster recovery - A two model case study of the Great East Japan Earthquake , 2015 .

[29]  John E. Taylor,et al.  Patterns and Limitations of Urban Human Mobility Resilience under the Influence of Multiple Types of Natural Disaster , 2016, PloS one.

[30]  Eui-Nam Huh,et al.  Harness Human Sensor Networks for Situational Awareness in Disaster Reliefs: A Survey , 2013 .

[31]  Antonio Gasbarrini,et al.  Incidence of DAA failure and the clinical impact of retreatment in real-life patients treated in the advanced stage of liver disease: Interim evaluations from the PITER network , 2017, PloS one.

[32]  A. Pentland,et al.  Social Physics: How Social Networks Can Make Us Smarter , 2014 .

[33]  Piet Daas,et al.  Big Data as a Source for Official Statistics , 2015 .

[34]  Xiaoquan Zhang,et al.  AIS Electronic Library (AISeL) , 2017 .

[35]  Mudhakar Srivatsa,et al.  On Critical Event Observability Using Social Networks: A Disaster Monitoring Perspective , 2014, 2014 IEEE Military Communications Conference.

[36]  Huajun Chen,et al.  Social Media Meets Big Urban Data: A Case Study of Urban Waterlogging Analysis , 2016, Comput. Intell. Neurosci..

[37]  Andrew B. Whinston,et al.  Whose and what chatter matters? The effect of tweets on movie sales , 2013, Decis. Support Syst..

[38]  S. Cutter,et al.  Leveraging Twitter to gauge evacuation compliance: Spatiotemporal analysis of Hurricane Matthew , 2017, PloS one.