IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i9p5254-d550457.html
   My bibliography  Save this article

Modeling the Influence of Online Social Media Information on Post-Disaster Mobility Decisions

Author

Listed:
  • Takahiro Yabe

    (Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA)

  • P. Suresh C. Rao

    (Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA
    Department of Agronomy, Purdue University, West Lafayette, IN 47907, USA)

  • Satish V. Ukkusuri

    (Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA)

Abstract

Disaster risk management, including response and recovery, are essential elements of sustainable development. With the recent increase in natural hazards, the importance of techniques to understand, model and predict the evacuation and returning behavior of affected individuals is rising. Studies have found that influence from real world social ties affects mobility decisions during disasters. Despite the rapid spread of social media platforms, little has been quantitatively understood about the influence of social ties on online social media on such decisions. Information provided by who at what timing influences users’ decision-making process by how much during disasters? In this study, we answer these research questions by proposing a data-driven framework that can predict post-disaster mobility decisions and simultaneously unravel the influence of various information on online social media. More specifically, our method quantifies the influence of information provided by different types of social media accounts on the peoples’ decisions to return or stay displaced after evacuation. We tested our approach using real world data collected from more than 13 million unique Twitter users during Hurricane Sandy. Experiments verified that we can improve the predictive accuracy of return and displacement behavior, and also quantify the influence of online information. In contrast to popular beliefs, it was found that information posted by the crowd influenced the decisions more than information disseminated by official accounts. Improving our understanding of influence dynamics on online social media could provide policy makers with insights on how to disseminate information on social media more effectively for better disaster response and recovery, which may contribute towards building sustainable urban systems.

Suggested Citation

  • Takahiro Yabe & P. Suresh C. Rao & Satish V. Ukkusuri, 2021. "Modeling the Influence of Online Social Media Information on Post-Disaster Mobility Decisions," Sustainability, MDPI, vol. 13(9), pages 1-13, May.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:9:p:5254-:d:550457
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/9/5254/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/9/5254/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yury Kryvasheyeu & Haohui Chen & Esteban Moro & Pascal Van Hentenryck & Manuel Cebrian, 2015. "Performance of Social Network Sensors during Hurricane Sandy," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-19, February.
    2. Hasan, Samiul & Ukkusuri, Satish V., 2011. "A threshold model of social contagion process for evacuation decision making," Transportation Research Part B: Methodological, Elsevier, vol. 45(10), pages 1590-1605.
    3. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    4. Mike Thelwall & Kevan Buckley & Georgios Paltoglou & Di Cai & Arvid Kappas, 2010. "Sentiment strength detection in short informal text," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(12), pages 2544-2558, December.
    5. Mike Thelwall & Kevan Buckley & Georgios Paltoglou & Di Cai & Arvid Kappas, 2010. "Sentiment strength detection in short informal text," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(12), pages 2544-2558, December.
    6. Ji Guo & Hui Liu & Xianhua Wu & Jiong Gu & Shunfeng Song & Yinshan Tang, 2015. "Natural Disasters, Economic Growth and Sustainable Development in China―An Empirical Study Using Provincial Panel Data," Sustainability, MDPI, vol. 7(12), pages 1-18, December.
    7. John C. Whitehead & Bob Edwards & Marieke Van Willigen & John R. Maiolo & Kenneth Wilson & Kevin T. Smith, 2000. "“Heading for Higher Ground: Factors Affecting Real and Hypothetical Hurricane Evacuation Behavior,”," Working Papers 0006, East Carolina University, Department of Economics.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Masahiko Haraguchi & Akihiko Nishino & Akira Kodaka & Maura Allaire & Upmanu Lall & Liao Kuei-Hsien & Kaya Onda & Kota Tsubouchi & Naohiko Kohtake, 2022. "Human mobility data and analysis for urban resilience: A systematic review," Environment and Planning B, , vol. 49(5), pages 1507-1535, June.
    2. Turgut Acikara & Bo Xia & Tan Yigitcanlar & Carol Hon, 2023. "Contribution of Social Media Analytics to Disaster Response Effectiveness: A Systematic Review of the Literature," Sustainability, MDPI, vol. 15(11), pages 1-50, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ma, Jie & Tse, Ying Kei & Wang, Xiaojun & Zhang, Minhao, 2019. "Examining customer perception and behaviour through social media research – An empirical study of the United Airlines overbooking crisis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 127(C), pages 192-205.
    2. Müller-Hansen, Finn & Lee, Yuan Ting & Callaghan, Max & Jankin, Slava & Minx, Jan C., 2022. "The German coal debate on Twitter: Reactions to a corporate policy process," Energy Policy, Elsevier, vol. 169(C).
    3. Daesik Kim & Chung Joo Chung & Kihong Eom, 2022. "Measuring Online Public Opinion for Decision Making: Application of Deep Learning on Political Context," Sustainability, MDPI, vol. 14(7), pages 1-16, March.
    4. Gabriele Ranco & Ilaria Bordino & Giacomo Bormetti & Guido Caldarelli & Fabrizio Lillo & Michele Treccani, 2014. "Coupling news sentiment with web browsing data improves prediction of intra-day price dynamics," Papers 1412.3948, arXiv.org, revised Dec 2015.
    5. Tadić, Bosiljka & Mitrović Dankulov, Marija & Melnik, Roderick, 2023. "Evolving cycles and self-organised criticality in social dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 171(C).
    6. Ping-Yu Hsu & Hong-Tsuen Lei & Shih-Hsiang Huang & Teng Hao Liao & Yao-Chung Lo & Chin-Chun Lo, 2019. "Effects of sentiment on recommendations in social network," Electronic Markets, Springer;IIM University of St. Gallen, vol. 29(2), pages 253-262, June.
    7. Cohen, Scott & Stienmetz, Jason & Hanna, Paul & Humbracht, Michael & Hopkins, Debbie, 2020. "Shadowcasting tourism knowledge through media: Self-driving sex cars?," Annals of Tourism Research, Elsevier, vol. 85(C).
    8. Zhang, Xuetong & Zhang, Weiguo, 2023. "Information asymmetry, sentiment interactions, and asset price," The North American Journal of Economics and Finance, Elsevier, vol. 67(C).
    9. Indy Wijngaards & Martijn Burger & Job van Exel, 2019. "The promise of open survey questions—The validation of text-based job satisfaction measures," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-22, December.
    10. Junegak Joung & Ki-Hun Kim & Kwangsoo Kim, 2021. "Data-Driven Approach to Dual Service Failure Monitoring From Negative Online Reviews: Managerial Perspective," SAGE Open, , vol. 11(1), pages 21582440209, January.
    11. Ema Kušen & Mark Strembeck, 2021. "“Evacuate everyone south of that line” Analyzing structural communication patterns during natural disasters," Journal of Computational Social Science, Springer, vol. 4(2), pages 531-565, November.
    12. Wen Zhang & Daniel R. Fesenmaier, 2018. "Assessing emotions in online stories: comparing self-report and text-based approaches," Information Technology & Tourism, Springer, vol. 20(1), pages 83-95, December.
    13. Sejung Park & Jin-A Choi, 2023. "Comparing public responses to apologies: examining crisis communication strategies using network analysis and topic modeling," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(4), pages 3603-3620, August.
    14. Simon Albrecht & Bernhard Lutz & Dirk Neumann, 2020. "The behavior of blockchain ventures on Twitter as a determinant for funding success," Electronic Markets, Springer;IIM University of St. Gallen, vol. 30(2), pages 241-257, June.
    15. Jun Lee & Adam Jatowt & Kyoung‐Sook Kim, 2021. "Discovering underlying sensations of human emotions based on social media," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(4), pages 417-432, April.
    16. Sergey Smetanin, 2022. "Pulse of the Nation: Observable Subjective Well-Being in Russia Inferred from Social Network Odnoklassniki," Mathematics, MDPI, vol. 10(16), pages 1-38, August.
    17. Widerstedt, Barbro & Månsson, Jonas & Rosdahl, Jonatan, 2018. "A warm welcome? Access to advisory services for men and women," Economic Analysis and Policy, Elsevier, vol. 58(C), pages 100-110.
    18. Heleen Brans & Bert Scholtens, 2020. "Under his thumb the effect of president Donald Trump’s Twitter messages on the US stock market," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-11, March.
    19. Nohel Zaman & David M. Goldberg & Richard J. Gruss & Alan S. Abrahams & Siriporn Srisawas & Peter Ractham & Michelle M.H. Şeref, 2022. "Cross-Category Defect Discovery from Online Reviews: Supplementing Sentiment with Category-Specific Semantics," Information Systems Frontiers, Springer, vol. 24(4), pages 1265-1285, August.
    20. Oleg S. Nagornyy & Olessia Y. Koltsova, 2017. "Mining Media Topics Perceived as Social Problems by Online Audiences: Use of a Data Mining Approach in Sociology," HSE Working papers WP BRP 74/SOC/2017, National Research University Higher School of Economics.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:9:p:5254-:d:550457. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.