Using Social Media for Economic Disaster Evaluation: A Systematic Literature Review and Real Case Application
Publication: Natural Hazards Review
Volume 23, Issue 1
Abstract
We propose the usage of social media as the primary way to collect and consolidate disaster data to provide a less costly and faster economic evaluation process. Through a systematic literature review (SLR) of over 406 peer-reviewed papers, this study first offers a taxonomy to show how social media can be applied to economic disaster assessments. Then we deliver an economic assessment to a flooding disaster in Brazil based on text mining of Twitter messages. Finally, we compare our findings with the Brazilian local government’s assessment and the literature. The results prove the feasibility of delivering a multisector disaster economic evaluation report based entirely on social media. The comparison analysis shows that social media is a valuable tool for the design and implementation of public policies, allowing the traceability of the collected information, the identification of unaccounted costs, and inquiries into the amounts and costs estimated by the local government.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some data generated during the study are available in a repository or online in accordance with funder data retention policies. The Case Data Set can be found online at 10.17632/cfxdry2h2x.2.
Acknowledgments
The authors acknowledge the support of the National Council for Scientific and Technological Development (CNPq) [308084/2019-5; 311862/2019-5]; Coordination for the Improvement of Higher Education Personnel (CAPES) [88887091739/2014-01, Finance Code 001]; and the Foundation for Support of Research in the State of Rio de Janeiro (FAPERJ) [211.029/2019; E-26/201.384/2021; E26-203.252/2017; E26-201.251/12021].
References
Alam, F., F. Ofli, and M. Imran. 2018. “Processing social media images by combining human and machine computing during crises.” Int. J. Hum.–Comput. Interact. 34 (4): 311–327. https://doi.org/10.1080/10447318.2018.1427831.
Allaire, M. C. 2016. “Disaster loss and social media: Can online information increase flood resilience?” Water Resour. Res. 52 (9): 7408–7423. https://doi.org/10.1002/2016WR019243.
Andrade, S. C., C. Restrepo-Estrada, A. C. B. Delbem, E. M. Mendiondo, and J. P. de Albuquerque. 2017. “Mining rainfall spatio-temporal patterns in twitter: A temporal approach.” In Societal geo-innovation. Berlin: Springer.
Aulov, O., and M. Halem. 2012. “Human sensor networks for improved modeling of natural disasters.” In Proc., IEEE, 2812–2823. New York: IEEE.
CaseDataSet. 2020. TWITTER_DETAILED_COSTS_PER_SECTOR. Rio de Janeiro, Brazil: Pontifical Catholic Univ. https://doi.org/10.17632/cfxdry2h2x.2.
Cervone, G., E. Sava, Q. Huang, E. Schnebele, J. Harrison, and N. Waters. 2016. “Using Twitter for tasking remote-sensing data collection and damage assessment: 2013 Boulder flood case study.” Int. J. Remote Sens. 37 (1): 100–124. https://doi.org/10.1080/01431161.2015.1117684.
Cheng, J. W., H. Mitomo, T. Otsuka, and S. Y. Jeon. 2015. “The effects of ICT and mass media in post-disaster recovery—A two model case study of the Great East Japan Earthquake.” Telecommun. Policy 39 (6): 515–532. https://doi.org/10.1016/j.telpol.2015.03.006.
CRED (Centre for Research on the Epidemiology of Disasters). 2019. “Natural disasters.” Accessed December 12, 2019. https://emdat.be/sites/default/files/adsr_2018.pdf.
DataRio. 2021. “Rio de Janeiro city map, Brazil.” Accessed November 2, 2021. https://www.data.rio/documents/fd187b5936214e9086be4e2643f36c62/explore.
Eckhardt, D., and A. Leiras. 2018. “A review of required features for a disaster response system on top of a multi-criteria decision: A Brazilian perspective.” Production 28 (5): e20180007. https://doi.org/10.1590/0103-6513.20180007.
Eckhardt, D., A. Leiras, and A. M. T. Thomé. 2019. “Systematic literature review of methodologies for assessing the costs of disasters.” Int. J. Disaster Risk Reduct. 33 (4): 398–416. https://doi.org/10.1016/j.ijdrr.2018.10.010.
Erdik, M., K. Şeşetyan, M. B. Demircioǧlu, C. Zülfikar, U. Hancilar, C. Tüzün, and E. Harmandar. 2014. “Rapid earthquake loss assessment after damaging earthquakes.” Geotech. Geol. Earthquake Eng. 34 (2): 53–95. https://doi.org/10.1007/978-3-319-07118-3_2.
Fontainha, T. C., A. Leiras, and R. A. Bandeira. 2017. “Public-private-people relationship stakeholder model for disaster and humanitarian operations.” Int. J. Disaster Risk Reduct. 22 (Jun): 371–386. https://doi.org/10.1016/j.ijdrr.2017.02.004.
Gao, H., G. Barbier, and R. Goolsby. 2011. “Harnessing the crowdsourcing power of social media for disaster relief.” Intell. Syst. 26 (3): 10–14. https://doi.org/10.1109/MIS.2011.52.
Guan, X., and C. Chen. 2014. “Using social media data to understand and assess disasters.” Nat. Hazards 74 (2): 837–850. https://doi.org/10.1007/s11069-014-1217-1.
Haworth, B., and E. Bruce. 2015. “A review of volunteered geographic information for disaster management.” Geogr. Compass 9 (Sep): 237–250. https://doi.org/10.1111/gec3.12213.
Herring, S. C. 1996. Computer-mediated communication: Linguistic, social, and cross-cultural perspectives. Amsterdam, Netherlands: John Benjamins Publishing.
Huang, X., C. Wang, and Z. Li. 2018a. “A near real-time floodmapping approach by integrating social media and post-event satellite imagery.” Ann. GIS 24 (2): 113–123. https://doi.org/10.1080/19475683.2018.1450787.
Huang, X., C. Wang, and Z. Li. 2018b. “Reconstructing flood inundation probability by enhancing near real-time imagery with real-time gauges and tweets.” IEEE Trans. Geosci. Remote Sens. 56 (8): 4691–4701. https://doi.org/10.1109/TGRS.2018.2835306.
IBGE (Instituto Brasileiro de Geografia e Estatística). 2019. “Estatísticas para download.” Accessed November 17, 2019. https://www.ibge.gov.br/estatisticas/downloads-estatisticas.html.
Imran, M., C. Castillo, F. Diaz, and S. Vieweg. 2015. “Processing social media messages in mass emergency: A survey.” ACM Comput. Surv. 47 (38): 1. https://doi.org/10.1145/2771588.
IPCC (Intergovernmental Panel on Climate Change). 2012. “Managing the risks of extreme events and disasters to advance climate change adaptation.” In A special report of working groups I and II of the intergovernmental panel on climate change. Cambridge, UK: Cambridge University Press.
Kibanov, M., G. Stumme, and I. Amin. 2017. “Mining social media to inform peatland fire and haze disaster management.” Soc. Netw. Anal. Min. 7 (1): 30. https://doi.org/10.1007/s13278-017-0446-1.
Kryvasheyeu, Y., H. Chen, N. Obradovich, E. Moro, P. Van Hentenryck, J. Fowler, and M. Cebrian. 2016. “Rapid assessment of disaster damage using social media activity.” Sci. Adv. 2 (3): e1500779. https://doi.org/10.1126/sciadv.1500779.
Kunz, N., L. N. Van Wassenhove, M. Besiou, C. Hambye, and G. Kovács. 2017. “Relevance of humanitarian logistics research: Best practices and way forward.” Int. J. Oper. Prod. Manage. 37 (11): 1585–1599. https://doi.org/10.1108/IJOPM-04-2016-0202.
Leiras, A., I. De, E. Q. Peres, T. R. Bertazzo, and H. T. Y. Yoshizaki. 2014. “Literature review of humanitarian logistics research: Trends and challenges.” J. Humanit. Logist. Supply Chain Manage. 4 (1): 95–130. https://doi.org/10.1108/JHLSCM-04-2012-0008.
Liu, F., and D. Xu. 2018. “Social roles and consequences in using social media in disasters: A structurational perspective.” Inf. Syst. Front. 20 (4): 693–711. https://doi.org/10.1007/s10796-017-9787-6.
López-Cuevas, A., J. Ramírez-Márquez, G. Sanchez-Ante, and K. Barker. 2017. “A community perspective on resilience analytics: A visual analysis of community mood.” Risk Anal. 37 (8): 1566–1579. https://doi.org/10.1111/risa.12788.
Matthias, O., I. Fouweather, I. Gregory, and A. Vernon. 2017. “Making sense of big data—Can it transform operations management?” Int. J. Oper. Prod. Manage. 37 (1): 37–55. https://doi.org/10.1108/IJOPM-02-2015-0084.
MDR (Ministério do Desenvolvimento Regional). 2019. “Secretaria Nacional de Proteção e Defesa Civil. Brasília.” Accessed October 21, 2019. https://www.mdr.gov.br/protecao-e-defesa-civil.
Mendoza, M., B. Poblete, and I. Valderrama. 2019. “Nowcasting earthquake damages with Twitter.” EPJ Data Sci. 8 (1): 3. https://doi.org/10.1140/epjds/s13688-019-0181-0.
Meyer, V., N. Becker, V. Markantonis, R. Schwarze, J. C. J. M. van den Bergh, L. M. Bouwer, and C. Viavattene. 2013. “Review article: Assessing the costs of natural hazards—State of the art and knowledge gaps.” Nat. Hazards Earth Syst. Sci. 13 (5): 1351–1373. https://doi.org/10.5194/nhess-13-1351-2013.
Ministerio da Saúde. 2019. “Saúde de A a Z. Brasília.” Accessed December 12, 2019. https://saude.gov.br/saude-de-a-z/leptospirose.
Moher, D., L. Shamseer, and M. Clarke. 2015. “Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement.” Syst. Rev. 4 (Apr): 1. https://doi.org/10.1186/2046-4053-4-1.
Mulrow, C. D. 1994. “Systematic reviews: Rationale for systematic reviews.” BMJ 309 (6954): 597–599. https://doi.org/10.1136/bmj.309.6954.597.
Nilsang, S., C. Yuangyai, and C. Cheng. 2019. “Locating an ambulance base by using social media: A case study in Bangkok.” Ann. Oper. Res. 283 (1): 497–516. https://doi.org/10.1007/s10479-018-2918-8.
Panteras, G., and G. Cervone. 2018. “Enhancing the temporal resolution of satellite-based flood extent generation using crowdsourced data for disaster monitoring.” Int. J. Remote Sens. 39 (5): 1459–1474. https://doi.org/10.1080/01431161.2017.1400193.
Phillips, L., C. Dowling, K. Shaffer, N. Hodas, and S. Volkova. 2017. “Using social media to predict the future: A systematic literature review.” Preprint, submitted June 19, 2017. https://arxiv.org/abs/1706.06134v1.
Pierpoint, L. 2011. “Fukushima, Facebook and feeds: Informing the public in a digital era.” Electron. J. 24 (6): 53–58. https://doi.org/10.1016/j.tej.2011.06.001.
Resch, B., F. Usländer, and C. Havas. 2017. “Combining machine-learning topic models and spatiotemporal analysis of social media data for disaster footprint and damage assessment.” Cartogr. Geogr. Inf. Sci. 45 (4): 362–376. https://doi.org/10.1080/15230406.2017.1356242.
Restrepo-Estrada, C., S. C. de Andrade, N. Abe, M. C. Fava, E. M. Mendiondo, and J. P. de Albuquerque. 2018. “Geo-social media as a proxy for hydrometeorological data for streamflow estimation and to improve flood monitoring.” Comput. Geosci. 111 (Feb): 148–158. https://doi.org/10.1016/j.cageo.2017.10.010.
Reynard, D., and M. Shirgaokar. 2019. “Harnessing the power of machine learning: Can Twitter data be useful in guiding resource allocation decisions during a natural disaster?” Transp. Res. Part D Transp. Environ. 77 (45): 449–463. https://doi.org/10.1016/j.trd.2019.03.002.
Rickard, L. N., Z. J. Yang, J. P. Schuldt, G. M. Eosco, C. W. Scherer, and R. A. Daziano. 2017. “Sizing up a superstorm: Exploring the role of recalled experience and attribution of responsibility in judgments of future hurricane risk.” Risk Anal. 37 (12): 2334–2349. https://doi.org/10.1111/risa.12779.
S2iD. 2021. “The Brazilian integrated disaster information system.” Accessed December 1, 2021. https://s2id.mi.gov.br/paginas/index.xhtml.
Sachdeva, S., S. McCaffrey, and D. Locke. 2017. “Social media approaches to modeling wildfire smoke dispersion: Spatiotemporal and social scientific investigations.” Inf. Commun. Soc. 20 (8): 1146–1161. https://doi.org/10.1080/1369118X.2016.1218528.
Seuring, S., and S. Gold. 2012. “Conducting content-analysis based literature reviews in supply chain management.” Supply Chain Manage. Int. J. 17 (5): 544–555. https://doi.org/10.1108/13598541211258609.
Shan, S., F. Zhao, Y. Wei, and M. Liu. 2019. “Disaster management 2.0: A real-time disaster damage assessment model based on mobile social media data—A case study of Weibo (Chinese Twitter).” Saf. Sci. 115 (Apr): 393–413. https://doi.org/10.1016/j.ssci.2019.02.029.
Shanley, L., R. Burns, S. Bastian, and R. Edward. 2013. “Tweeting up a storm: The promise and perils of crisis mapping.” Photogramm. Eng. Remote Sensing 79 (10): 865–879. https://doi.org/10.1016/j.ssci.2019.02.029.
Statista. 2019. “Most popular social networks worldwide as of May 2019, ranked by number of active users.” Accessed June 3, 2019. https://www.statista.com/statistics/272014/global-social-networks-ranked-by-number-of-users.
Sutton, A. J. 2013. Handbook of research synthesis and meta-analysis, 2nd ed., 436–451. New York: Russel Sage Foundation.
Tagliacozzo, S., and M. Magni. 2016. “Communicating with communities during post-disaster reconstruction: An initial analysis.” Nat. Hazards 84 (Apr): 2225–2242. https://doi.org/10.1007/s11069-016-2550-3.
Thomé, A. M. T., L. F. Scavarda, and A. J. Scavarda. 2016. “Conducting systematic literature review in operations management.” Prod. Plan. Control 27 (5): 408–420. https://doi.org/10.1080/09537287.2015.1129464.
Torraco, R. J. 2005. “Writing integrative literature reviews: Guidelines and examples.” Hum. Resour. Dev. Rev. 4 (3): 356–367. https://doi.org/10.1177/1534484305278283.
Vieweg, S., C. Castillo, and M. Imran. 2014. “Integrating social media communications into the rapid assessment of sudden onset disasters.” In Social informatics. Berlin: Springer.
Wang, Y., Q. Wang, and J. E. Taylor. 2017. “Aggregated responses of human mobility to severe winter storms: An empirical study.” PLoS One 12 (12): e0188734. https://doi.org/10.1371/journal.pone.0188734.
World Bank. 2013. Post-disaster needs assessment guidelines. Washington, DC: World Bank.
Wu, D., and Y. Cui. 2018. “Disaster early warning and damage assessment analysis using social media data and geo-location information.” Decis. Support Syst. 111 (Jul): 48–59. https://doi.org/10.1016/j.dss.2018.04.005.
Yabe, T., and S. V. Ukkusuri. 2019. “Integrating information from heterogeneous networks on social media to predict post-disaster returning behavior.” J. Comput. Sci. 32 (Mar): 12–20. https://doi.org/10.1016/j.jocs.2019.02.002.
Yan, Y., M. Eckle, C.-L. Kuo, B. Herfort, H. Fan, and A. Zipf. 2017. “Monitoring and assessing post-disaster tourism recovery using geotagged social media data.” ISPRS Int. J. Geo-Inf. 6 (5): 144. https://doi.org/10.3390/ijgi6050144.
Yang, T., J. Xie, G. Li, N. Mou, Z. Li, C. Tian, and J. Zhao. 2019. “Social media big data mining and spatio-temporal analysis on public emotions for disaster mitigation.” ISPRS Int. J. Geo-Inf. 8 (1): 29. https://doi.org/10.3390/ijgi8010029.
Yuan, F., and R. Liu. 2018. “Feasibility study of using crowdsourcing to identify critical affected areas for rapid damage assessment: Hurricane Matthew case study.” Int. J. Disaster Risk Reduct. 28 (Jun): 758–767. https://doi.org/10.1016/j.ijdrr.2018.02.003.
Information & Authors
Information
Published In
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Jun 3, 2021
Accepted: Oct 13, 2021
Published online: Dec 2, 2021
Published in print: Feb 1, 2022
Discussion open until: May 2, 2022
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Ahmet ÖZDEMİR, Turgut ŞAHİNÖZ, Toplumda Afet Farkındalığı Oluşturmaya Yönelik Kullanılan Araçlar, Afet ve Risk Dergisi, 10.35341/afet.1083976, (2022).