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Cited by 35Disaster Management and ResiliencePublic Relations and Crisis CommunicationRisk Perception and Management

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Framing Disaster Risk Perception and Vulnerability in Social Media Communication: A Literature Review

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Morelli, Stefano, Pazzi, Veronica, Nardini, Olga, Bonati, Sara·2022·Sustainability
DOI 10.3390/su14159148
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Abstract

The paper presents the results of a literature review on how social media can impact on disaster risk perception and vulnerability and how these two aspects are interconnected, trying to understand what factors have consequences especially on informational vulnerability. The paper answers to the increasing requests at an international level to move from a technocratic approach to disaster risk management and reduction to a holistic one, where social perspective is integrated. The paper states that this change of paradigm is relevant, especially considering the role that new technologies in communication and information systems are acquiring in disaster risk management and reduction. What emerges from the literature review is that there is a limited scientific production on the topic and further works are desired, to improve knowledge on how new communication and information technologies can impact on vulnerability and risk perception. Furthermore, the two topics are usually discussed separately. However, the role that risk perception can have in increasing or reducing vulnerability deserves to be better discussed.

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Figure 1 The workflow of the procedure applied to search and select papers. Figure 1. The workflow of the procedure applied to search and select papers.
Figure 1 The workflow of the procedure applied to search and select papers. Figure 1. The workflow of the procedure applied to search and select papers.CC-BY · From the PDF
Figure 2 Distribution of the papers production over the time (years) about DRP and SM (in red), vulnerability and SM (in green), and about DRP, vulnerability, and SM (in blue). The number of papers published in the 2022 is referred to the period of January-March. Figure 2. Distribution of the papers production over the time (years) about DRP and SM (in red), vulnerability and SM (in green), and about DRP, vulnerability, and SM (in blue). The number of papers published in the 2022 is referred to the period of January-March. Figure 2. Distribution of the papers production over the time (years) about DRP and SM (in red), vulnerability and SM (in green), and about DRP, vulnerability, and SM (in blue). The number of papers published in the 2022 is referred to the period of January-March.
Figure 2 Distribution of the papers production over the time (years) about DRP and SM (in red), vulnerability and SM (in green), and about DRP, vulnerability, and SM (in blue). The number of papers published in the 2022 is referred to the period of January-March. Figure 2. Distribution of the papers production over the time (years) about DRP and SM (in red), vulnerability and SM (in green), and about DRP, vulnerability, and SM (in blue). The number of papers published in the 2022 is referred to the period of January-March. Figure 2. Distribution of the papers production over the time (years) about DRP and SM (in red), vulnerability and SM (in green), and about DRP, vulnerability, and SM (in blue). The number of papers published in the 2022 is referred to the period of January-March.CC-BY · From the PDF
Figure 3 The spatial distribution of the ( a ) DRP, ( b ) DRP and vulnerability, and ( c ) vulnerability analyzed papers. The 'N/A' category groups those works where it is not possible to define a specific study area (i.e., in the review works). Figure 3. The spatial distribution of the ( a ) DRP, ( b ) DRP and vulnerability, and ( c ) vulnerability analyzed papers. The 'N/A' category groups those works where it is not possible to define a specific study area (i.e., in the review works). Figure 3. The spatial distribution of the ( a ) DRP, ( b ) DRP and vulnerability, and ( c ) vulnerability analyzed papers. The 'N/A' category groups those works where it is not possible to define a specific study area (i.e., in the review works).
Figure 3 The spatial distribution of the ( a ) DRP, ( b ) DRP and vulnerability, and ( c ) vulnerability analyzed papers. The 'N/A' category groups those works where it is not possible to define a specific study area (i.e., in the review works). Figure 3. The spatial distribution of the ( a ) DRP, ( b ) DRP and vulnerability, and ( c ) vulnerability analyzed papers. The 'N/A' category groups those works where it is not possible to define a specific study area (i.e., in the review works). Figure 3. The spatial distribution of the ( a ) DRP, ( b ) DRP and vulnerability, and ( c ) vulnerability analyzed papers. The 'N/A' category groups those works where it is not possible to define a specific study area (i.e., in the review works).CC-BY · From the PDF
Figure 4 ( a ) Kind of hazards and ( b ) SM analyzed in the papers that focus on DRP (in blue), on vulnerability (in orange), and on DRP and Vulnerability in green. The class 'others' in panel ( b ) refers to technologies not specified as mobile technologies or information technologies. Figure 4. ( a ) Kind of hazards and ( b ) SM analyzed in the papers that focus on DRP (in blue), on vulnerability (in orange), and on DRP and Vulnerability in green. The class 'others' in panel ( b ) refers to technologies not specified as mobile technologies or information technologies.
Figure 4 ( a ) Kind of hazards and ( b ) SM analyzed in the papers that focus on DRP (in blue), on vulnerability (in orange), and on DRP and Vulnerability in green. The class 'others' in panel ( b ) refers to technologies not specified as mobile technologies or information technologies. Figure 4. ( a ) Kind of hazards and ( b ) SM analyzed in the papers that focus on DRP (in blue), on vulnerability (in orange), and on DRP and Vulnerability in green. The class 'others' in panel ( b ) refers to technologies not specified as mobile technologies or information technologies.CC-BY · From the PDF
Figure 5 Use of the different SM in relation to the four main aspects identified (light green for social aspects, blue for individual aspects, yellow for information/communication flows, and purple for trust) for ( a ) DRP and vulnerability, ( b ) DRP, and ( c ) vulnerability studies. Figure 5. Use of the different SM in relation to the four main aspects identified (light green for social aspects, blue for individual aspects, yellow for information/communication flows, and purple for trust) for ( a ) DRP and vulnerability, ( b ) DRP, and ( c ) vulnerability studies.
Figure 5 Use of the different SM in relation to the four main aspects identified (light green for social aspects, blue for individual aspects, yellow for information/communication flows, and purple for trust) for ( a ) DRP and vulnerability, ( b ) DRP, and ( c ) vulnerability studies. Figure 5. Use of the different SM in relation to the four main aspects identified (light green for social aspects, blue for individual aspects, yellow for information/communication flows, and purple for trust) for ( a ) DRP and vulnerability, ( b ) DRP, and ( c ) vulnerability studies.CC-BY · From the PDF
Figure 6 The mutual connections among the factors that shape and change vulnerability and DRP in relation to the informational vulnerability use of SM. Figure 6. The mutual connections among the factors that shape and change vulnerability and DRP in relation to the informational vulnerability use of SM.
Figure 6 The mutual connections among the factors that shape and change vulnerability and DRP in relation to the informational vulnerability use of SM. Figure 6. The mutual connections among the factors that shape and change vulnerability and DRP in relation to the informational vulnerability use of SM.CC-BY · From the PDF

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Research works printed but not linked: 104

  1. Westgate, 1976, Taking the naturalness out of natural disasters, Nature, 260, 566, 10.1038/260566a0 · DOI
  2. Hewitt, K. (1983). Interpretations of Calamity: From the Viewpoint of Human Ecology, Routledge. [1st ed.].
  3. Peacock, W.G., Morrow, B.H., and Gladwin, H. (1997). Hurricane Andrew: Ethnicity, Gender and the Sociology of Disaster, Routledge. [1st ed.].
  4. Weichselgartner, 2001, Disaster mitigation: The concept of vulnerability revisited, Disaster Prev. Manag., 10, 85, 10.1108/09653560110388609 · DOI
  5. Alexander, D. (2002). Principles of Emergency Planning and Management, Oxford University Press. [1st ed.].
  6. Wisner, B., Blaikie, P.M., Blaikie, P., Cannon, T., and Davis, I. (2004). At Risk: Natural Hazards, People’s Vulnerability and Disasters, Psychology Press. · DOI
  7. Alexander, 1991, Natural disasters: A framework for research and teaching, Disasters, 15, 209, 10.1111/j.1467-7717.1991.tb00455.x · DOI
  8. Eiser, 2012, Risk interpretation and action: A conceptual framework for responses to natural hazards, Int. J. Disaster Risk Reduct., 1, 5, 10.1016/j.ijdrr.2012.05.002 · DOI
  9. Palliyaguru, 2014, Constructing a holistic approach to disaster risk reduction: The significance of focusing on vulnerability reduction, Disasters, 38, 45, 10.1111/disa.12031 · DOI
  10. Tierney, K. (2019). Disasters: A Sociological Approach, John Wiley & Sons.
  11. Andharia, J. (2020). Thinking about disasters: A call for intersectionality and transdisciplinarity in disaster studies. Disaster Studies, Springer. · DOI
  12. Orru, 2022, Approaches to ‘vulnerability’ in eight European disaster management systems, Disasters, 46, 742, 10.1111/disa.12481 · DOI
  13. McCallum, 2016, Technologies to support community flood disaster risk reduction, Int. J. Disaster Risk Sci., 7, 198, 10.1007/s13753-016-0086-5 · DOI
  14. Reuter, 2018, Fifteen years of social media in emergencies: A retrospective review and future directions for crisis informatics, J. Conting. Crisis Manag., 26, 41, 10.1111/1468-5973.12196 · DOI
  15. Nicholson, 2019, A spatial regression and clustering method for developing place-specific social vulnerability indices using census and social media data, Int. J. Disaster Risk Reduct., 38, 101, 10.1016/j.ijdrr.2019.101224 · DOI
  16. Singh, D., Shams, S., Kim, J., Park, S.J., and Yang, S. (2020). Fighting for Information Credibility: An End-to-End Framework to Identify Fake News during Natural Disasters. ISCRAM 2020 Conference Proceedings, 17th International Conference on Information Systems for Crisis Response and Management, Virginia Tech.
  17. Thomas, D.S., Phillips, B.D., Lovekamp, W.E., and Fothergill, A. (2013). Understanding social vulnerability. Social Vulnerability to Disasters, CRC Press. · DOI
  18. Lewis, J. (2012). The good, the bad and the ugly: Disaster Risk Reduction (DRR) versus Didsaster Risk Creation (DRC). PLoS Curr., 4. · DOI
  19. Uekusa, 2017, Vulnerable and resilient?, Immigrants and refugees in the 2010-2011 Canterbury and Tohoku disasters. Int. J. Disaster Risk Reduct., 22, 355
  20. Schanze, 2006, Flood Damage, Vulnerability and Risk Perception—Challenges for Flood Damage Research, Hazards, Vulnerability and Mitigation Measuresœ, Volume 67, 149, 10.1007/978-1-4020-4598-1_13 · DOI
  21. 2008, Social vulnerability and seismic risk perception. Case study: The historic center of the Bucharest Municipality/Romania, Nat. Hazards, 47, 397, 10.1007/s11069-008-9229-3 · DOI
  22. Buchanan, D., and Bryman, A. (2009). Producing a Systematic Review, Sage Publications Inc.. The Sage Handbook of Organizational Research Methods.
  23. Lee, 2015, Predictors of public climate change awareness and risk perception around the world, Nat. Clim. Change, 5, 1014, 10.1038/nclimate2728 · DOI
  24. Wachinger, 2013, The risk perception Paradox—Implications for governance and communication of natural hazards, Risk Anal., 33, 1049, 10.1111/j.1539-6924.2012.01942.x · DOI · Paper page
  25. Knuth, 2014, Risk Perception, Experience, and Objective Risk: A Cross-National Study with European Emergency Survivors, Risk Anal., 34, 1286, 10.1111/risa.12157 · DOI
  26. Cornia, 2016, Risk cultures and dominant approaches towards disasters in seven European countries, J. Risk Res., 19, 288, 10.1080/13669877.2014.961520 · DOI
  27. Hagen, 2016, European Climate Change Perceptions: Public support for mitigation and adaptation policies, Environ. Policy Gov., 26, 170, 10.1002/eet.1701 · DOI
  28. Gregory, 1997, Social and Cultural Aspects of Natural Hazards, N. Z. Geogr., 53, 47, 10.1111/j.1745-7939.1997.tb00476.x · DOI
  29. Kellens, 2011, An analysis of the public perception of flood risk on the Belgian coast, Risk Anal., 31, 1055, 10.1111/j.1539-6924.2010.01571.x · DOI · Paper page
  30. Slovic, 1987, Perception of Risk, Science, 236, 280, 10.1126/science.3563507 · DOI · Paper page
  31. Fischhoff, 1982, Lay foibles and expert fables in judgements about risk, Am. Stat., 36, 240N255
  32. Gough, 2000, Perceptions of risk from natural hazards in two remote New Zealand communities, Australas. J. Disaster Trauma Stud., 2, 1
  33. Bandecchi, 2019, Geo-hydrological and seismic risk awareness at school: Emergency preparedness and risk perception evaluation, Int. J. Disaster Risk Reduct., 40, 101280, 10.1016/j.ijdrr.2019.101280 · DOI
  34. Yong, 2019, Getting Canadians prepared for natural disasters: A multi-method analysis of risk perception, behaviors, and the social environment, Nat. Hazards, 98, 319, 10.1007/s11069-019-03669-2 · DOI
  35. Lai, 2018, Digital disparities and vulnerability: Mobile phone use, information behaviour, and disaster preparedness in Southeast Asia, Disasters, 42, 734, 10.1111/disa.12279 · DOI · Paper page
  36. Hargittai, 2002, Beyond logs and surveys: In-depth measures of people’s web use skills, J. Am. Soc. Inf. Sci. Technol., 53, 1239, 10.1002/asi.10166 · DOI
  37. Wang, 2019, Are vulnerable communities digitally left behind in social responses to natural disasters?, An evidence from Hurricane Sandy with Twitter data. Appl. Geogr., 108, 1
  38. Zou, 2018, Mining Twitter data for improved understanding of disaster resilience, Ann. Am. Assoc. Geogr., 108, 1422
  39. Madianou, 2015, Digital inequality and second-order disasters: Social media in the Typhoon Haiyan recovery, Soc. Media+ Soc., 1, 2056305115603386, 10.1177/2056305115603386 · DOI
  40. Hansson, 2020, Communication-related vulnerability to disasters: A heuristic framework, Int. J. Disaster Risk Reduct., 51, 101931, 10.1016/j.ijdrr.2020.101931 · DOI
  41. Kent, 2015, People with disability and new disaster communications: Access and the social media mash-up, Disabil. Soc., 30, 419, 10.1080/09687599.2015.1021756 · DOI
  42. Samuels, R., and Taylor, J.E. (2019, January 17–19). Applied Methodology for Identifying Hurricane-Induced Social Media Signal Changes in Vulnerable Populations. Proceedings of the ASCE International Conference on Computing in Civil Engineering, Atlanta, GA, USA. · DOI
  43. Cutter, 2008, Temporal and spatial changes in social vulnerability to natural hazards, Proc. Natl. Acad. Sci. USA, 105, 2301, 10.1073/pnas.0710375105 · DOI
  44. Adger, 2009, Nested and teleconnected vulnerabilities to environmental change, Front. Ecol Environ., 7, 150, 10.1890/070148 · DOI
  45. Eakin, 2009, Nested vulnerability: Exploring cross-scale linkages and vulnerability teleconnections in Mexican and Vietnamese coffee systems, Environ. Sci Policy, 12, 398, 10.1016/j.envsci.2008.09.003 · DOI
  46. Fordham, 2007, Social vulnerability and capacity, Nat. Hazards Obs., 23, 1
  47. Clark, 2021, Exploring the impacts of social media and crowdsourcing on disaster resilience. [version 1; peer review: 1 approved with reservations], Open Res. Eur., 1, 60, 10.12688/openreseurope.13721.1 · DOI
  48. Simons, 2015, Standing on the shoulders of giants: Challenges and recommendations of literature search in information systems research, Commun. Assoc. Inf. Syst., 37, 205
  49. Walkling, 2020, Flood risk perceptions and coping capacities among the retired population, with implications for risk communication: A study of residents in a north Wales coastal town, UK, Int. J. Disaster Risk Reduct., 51, 101793, 10.1016/j.ijdrr.2020.101793 · DOI
  50. Reuter, 2016, Emergency services’ attitudes towards social media: A quantitative and qualitative survey across Europe, Int. J. Hum.-Comput. Stud., 95, 96, 10.1016/j.ijhcs.2016.03.005 · DOI
  51. Mehta, 2017, Trust, but verify: Social media models for disaster management, Disasters, 41, 549, 10.1111/disa.12218 · DOI
  52. Dargin, 2021, Vulnerable populations and social media use in disasters: Uncovering the digital divide in three major US hurricanes, Int. J. Disaster Risk Reduct., 54, 102043, 10.1016/j.ijdrr.2021.102043 · DOI
  53. Reuter, 2019, The impact of risk cultures: Citizens’ perception of social media use in emergencies across Europe, Technol. Forecast. Soc. Chang., 148, 119724, 10.1016/j.techfore.2019.119724 · DOI
  54. Howard, 2017, “They’ll tell us when to evacuate”: The experiences and expectations of disaster-related communication in vulnerable groups, Int. J. Disaster Risk Reduct., 22, 139, 10.1016/j.ijdrr.2017.03.002 · DOI
  55. Moorthy, 2018, Disaster communication in managing vulnerabilities, J. Komun. Malays. J. Commun., 34, 51
  56. Vieweg, S., Hughes, A.L., Starbird, K., and Palen, L. (2010, January 10–15). Microblogging During Two Natural Hazards Events: What Twitter May Contribute to Situational Awareness. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Atlanta, GA, USA. · DOI
  57. Alexander, 2014, Social Media in Disaster Risk Reduction and Crisis Management, Sci. Eng. Ethics, 20, 717, 10.1007/s11948-013-9502-z · DOI
  58. Velev, 2012, Use of social media in natural disaster management, Int. Proc. Econ. Dev. Res., 39, 41
  59. Kent, 2013, Spatial patterns and demographic indicators of effective social media content during the Horsethief Canyon fire of 2012, Cartogr. Geogr. Inf. Sci., 40, 78, 10.1080/15230406.2013.776727 · DOI
  60. Veer, 2016, Sharing cathartic stories online: The internet as a means of expression following a crisis event, J. Consum. Behav., 15, 314, 10.1002/cb.1569 · DOI
  61. Cutter, 2020, Using geotagged tweets to track population movements to and from Puerto Rico after Hurricane Maria, Popul. Environ., 42, 4, 10.1007/s11111-020-00338-6 · DOI
  62. Jurgens, 2018, The effect of social media on the dynamics of (self) resilience during disasters: A literature review, J. Conting. Crisis Manag., 26, 79, 10.1111/1468-5973.12212 · DOI · Paper page
  63. Geng, 2021, Spatial–temporal differences in disaster perception and response among new media users and the influence factors: A case study of the Shouguang Flood in Shandong province, Nat. Hazards, 105, 2241, 10.1007/s11069-020-04398-7 · DOI
  64. Silver, 2016, The use of Facebook for information seeking, decision support, and self-organization following a significant disaster, Inf. Commun. Soc., 20, 1680, 10.1080/1369118X.2016.1253762 · DOI
  65. Kaufhold, 2019, Avoiding chaotic use of social media before, during, and after emergencies: Design and evaluation of citizens’ guidelines, J. Conting. Crisis Manag., 27, 198, 10.1111/1468-5973.12249 · DOI
  66. Tauzer, E., Borbor-Cordova, M.J., Mendoza, J., De La Cuadra, T., Cunalata, J., and Stewart-Ibarra, A.M. (2019). A participatory community case study of periurban coastal flood vulnerability in southern Ecuador. PLoS ONE, 14. · DOI
  67. Yue, 2021, Assessing wild fire risk in the United States using social media data, J. Risk Res., 24, 972, 10.1080/13669877.2019.1569098 · DOI
  68. Bec, 2021, Risk perceptions and emotional stability in response to Cyclone Debbie: An analysis of Twitter data, J. Risk Res., 24, 721, 10.1080/13669877.2019.1673798 · DOI
  69. Wu, 2021, Extracting disaster information based on Sina Weibo in China: A case study of the 2019 Typhoon Lekima, Int. J. Disaster Risk Reduct., 60, 102304, 10.1016/j.ijdrr.2021.102304 · DOI
  70. Earle, 2011, Twitter earthquake detection: Earthquake monitoring in a social world, Ann. Geophys., 54, 708
  71. Chatfield, A.T., Reddick, C.G., Inan, D.I., and Brajawidagda, U. (2014, January 18–21). E-government, social media, and risk perception communication at the edge of disaster: Findings from the Mt. Sinabung eruption in Indonesia. Proceedings of the 15th Annual International Conference on Digital Government Research, Aguascalientes, Mexico. · DOI
  72. Kongthon, 2014, The role of social media during a natural disaster: A case study of the 2011 Thai Flood, Int. J. Innov. Technol. Manag., 11, 1440012
  73. Zhang, 2021, Revealing Unfairness in social media contributors’ attention to vulnerable urban areas during disasters, Int. J. Disaster Risk Reduct., 58, 102160, 10.1016/j.ijdrr.2021.102160 · DOI
  74. Zhang, 2018, A cyberGIS-enabled multi-criteria spatial decision support system: A case study on flood emergency management, Int. J. Digit. Earth, 12, 1364, 10.1080/17538947.2018.1543363 · DOI
  75. Chen, 2021, Rapid damage assessment following natural disasters through information integration, Nat. Hazards Rev., 22, 04021043, 10.1061/(ASCE)NH.1527-6996.0000504 · DOI
  76. Wu, Z., Shen, Y., and Wang, H. (2019). Assessing urban areas’ vulnerability to flood disaster based on text data: A case study in Zhengzhou city. Sustainability, 11. · DOI
  77. Wu, 2020, Quantitative assessment of urban flood disaster vulnerability based on text data: Case study in Zhengzhou, Water Supply, 20, 408, 10.2166/ws.2019.171 · DOI
  78. Bhavaraju, 2019, Quantitative analysis of social media sensitivity to natural disasters, Int. J. Disaster Risk Reduct., 39, 101251, 10.1016/j.ijdrr.2019.101251 · DOI
  79. Fan, 2020, Spatial biases in crowdsourced data: Social media content attention concentrates on populous areas in disasters, Comput. Environ. Urban Syst., 83, 101514, 10.1016/j.compenvurbsys.2020.101514 · DOI
  80. Checker, 2017, Stop FEMA Now: Social media, activism and the sacrificed citizen, Geoforum, 79, 124, 10.1016/j.geoforum.2016.07.004 · DOI
  81. Fadaee, 2014, The occupy movement and the politics of vulnerability, Globalizations, 11, 777, 10.1080/14747731.2014.916552 · DOI
  82. Schmeltz, 2013, Lessons from hurricane sandy: A community response in Brooklyn, New York, J. Urban Health, 90, 799, 10.1007/s11524-013-9832-9 · DOI
  83. Shklovski, 2010, Technology adoption and use in the aftermath of Hurricane Katrina in New Orleans, Am. Behav. Sci., 53, 1228, 10.1177/0002764209356252 · DOI
  84. Martín, Y., Li, Z., and Cutter, S.L. (2017). Leveraging Twitter to gauge evacuation compliance: Spatiotemporal analysis of Hurricane Matthew. PLoS ONE, 12. · DOI
  85. Sarker, M.N.I., Wu, M., Chanthamith, B., and Ma, C. (2020). Resilience Through Big Data: Natural Disaster Vulnerability Context. International Conference on Management Science and Engineering Management, Springer. · DOI
  86. Wisner, B., Blaikie, P., Cannon, T., and Davis, I. (2014). At Risk: Natural Hazards, People’s Vulnerability, and Disasters, Routledge. [2nd ed.]. · DOI
  87. Bryant, 1998, Power, knowledge and political ecology in the third world: A review, Prog. Phys. Geogr., 22, 79, 10.1177/030913339802200104 · DOI
  88. Harrison, 2019, Challenges in the adoption of crisis crowdsourcing and social media in Canadian emergency management, Gov. Inf. Q., 36, 501, 10.1016/j.giq.2019.04.002 · DOI
  89. Zhuang, 2021, Mechanism of WeChat’s Impact on Public Risk Perception During COVID-19, Risk Manag. Healthc. Policy, 14, 4223, 10.2147/RMHP.S328175 · DOI
  90. Weyrich, 2021, Using serious games to evaluate the potential of social media information in early warning disaster management, Int. J. Disaster Risk Reduct., 56, 102053, 10.1016/j.ijdrr.2021.102053 · DOI
  91. Mohanty, 2021, A multi-modal approach towards mining social media data during natural disasters-a case study of hurricane irma, Int. J. Disaster Risk Reduct., 54, 102032, 10.1016/j.ijdrr.2020.102032 · DOI
  92. Hassan, M.S., Al Halbusi, H., Najem, A., Razali, A., and Williams, K.A. (2022). Impact of risk perception on trust in government and self-efficiency during COVID-19 pandemic: Does social media content help users adopt preventative measures?. Res. Sq., preprint. · DOI
  93. Endsley, 1995, Toward a theory of situation awareness in dynamic system, Hum. Factors, 37, 32, 10.1518/001872095779049543 · DOI · Paper page
  94. Stanton, 2001, Situational awareness and safety, Saf. Sci., 39, 189, 10.1016/S0925-7535(01)00010-8 · DOI
  95. 2004, Perceptions of hazard and risk on Santorini, J. Volcanol. Geotherm. Res., 137, 285, 10.1016/j.jvolgeores.2004.06.002 · DOI
  96. Gregg, 2004, The perception of volcanic Risk in Kona communities from Mauna Loa and Hualalai volcanoes, Hawai’i, J. Volcanol. Geotherm. Res., 130, 179, 10.1016/S0377-0273(03)00288-9 · DOI
  97. Cheng, 2016, Cultivation effects of mass and social media on perceptions and behavioural intentions in post-disaster recovery–The case of the 2011 Great East Japan Earthquake, Telemat. Inform., 33, 753, 10.1016/j.tele.2015.12.001 · DOI · Paper page
  98. Krukar, 2018, Risk perception during urban cycling: An assessment of crowdsourced and authoritative data, Accid. Anal. Prev., 121, 109, 10.1016/j.aap.2018.09.009 · DOI
  99. 2000, Risk perception and the media, J. Risk Res., 3, 31, 10.1080/136698700376699 · DOI · Paper page
  100. Xiao, 2015, Understanding social media data for disaster management, Nat. Hazards, 79, 1663, 10.1007/s11069-015-1918-0 · DOI
  101. Li, 2013, Spatial, temporal, and socioeconomic patterns in the use of Twitter and Flickr, Cartogr. Geogr. Inf. Sci., 40, 61, 10.1080/15230406.2013.777139 · DOI
  102. Viswanath, 1996, The knowledge gap hypothesis: Twenty-five years later, Ann. Int. Commun. Assoc., 19, 187
  103. Chatfield, A.T., and Brajawidagda, U. (2013, January 7–10). Twitter early tsunami warning system: A case study in Indonesia’s natural disaster management. Proceedings of the 2013 46th Hawaii International Conference on System Sciences, Wailea, HI, USA. · DOI
  104. Wu, 2019, Vulnerability assessment of urban socio⁃ecological systems in coastal zones under the influence of typhoons: Big data perspective, Acta Ecol. Sin., 39, 7079

Cited by

Metadata

Authors
Morelli, S., Pazzi, V., Nardini, O., & Bonati, S.
Affiliations
  • University of Urbino (IT)
  • University of Trieste (IT)
  • University of Florence (IT)
Corresponding author
Morelli, Stefano
ORCID
Venue
Sustainability
Publisher
MDPI AG
Published
2022-07-26
Volume
14
Issue
15
Pages
9148
ISSN
2071-1050 (ISSN-L)
DOI
10.3390/su14159148
OpenAlex
W4288068979
Cited by
35
References
2
Citations by year
2026: 7 · 2025: 13 · 2024: 9 · 2023: 5 · 2022: 1
FWCI
7.66
Type
journal article
Language
en
Open access
Licence
Locations
  • Sustainability (publishedVersion, cc-by)
  • ArTS Archivio della ricerca di Trieste (University of Trieste https://www.units.it/) (submittedVersion, cc-by)
  • Florence Research (University of Florence) (submittedVersion, other-oa)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • CINECA IRIS Institutial Research Information System (University of Genoa) (submittedVersion, other-oa)
  • CINECA IRIS Institutional Research information system (University of Urbino) (submittedVersion, cc-by)
Funders
European Union’s Horizon 2020 Research and Innovation Programme (883490)
Topics
  • Social Sciences › Sociology and Political Science › Disaster Management and Resilience
  • Social Sciences › Communication › Public Relations and Crisis Communication
  • Social Sciences › Sociology and Political Science › Risk Perception and Management
Keywords
disaster risk perception, vulnerability, social media, disaster risk management, disaster risk reduction, communication technology, holistic approach, societal perspective

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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