Contextualizing Disaster Phases Using Social Media Data: Hurricane Risk Visualizations during the Forecast and Warning Phase of Hurricane Irma (2024)

  • Alley, R. B., K. A. Emanuel, and F. Zhang, 2019: Advances in weather prediction. Science, 363, 342–344, https://doi.org/10.1126/science.aav7274.

  • Anderson, J., and Coauthors, 2016: Far far away in Far Rockaway: Responses to risks and impacts during Hurricane Sandy through first-person social media narratives. Proc. ISCRAM 2016 Conf., Rio Be Janeiro, Brazil, ISCRAM, http://idl.iscram.org/files/jenningsanderson/2016/1388_JenningsAnderson_etal2016.pdf.

  • Balog-Way, D., K. McComas, and J. Besley, 2020: The evolving field of risk communication. Risk Anal., 40, 2240–2262, https://doi.org/10.1111/risa.13615.

  • Bauer, P., A. Thorpe, and G. Brunet, 2015: The quiet revolution of numerical weather prediction. Nature, 525, 47–55, https://doi.org/10.1038/nature14956.

  • Bica, M., J. L. Demuth, J. E. Dykes, and L. Palen, 2019: Communicating hurricane risks: Multi-method examination of risk imagery diffusion. CHI’19: Proc. 2019 CHI Conf. on Human Factors in Computing Systems, Scotland, United Kingdom, Association for Computing Machinery, 1–13, https://doi.org/10.1145/3290605.3300545.

  • Bica, M., J. Weinberg, and L. Palen, 2020: Achieving accuracy through ambiguity: The interactivity of risk communication in severe weather events. Comput. Supported Coop. Work, 29, 587–623, https://doi.org/10.1007/s10606-020-09380-2.

  • Bica, M., L. Palen, J. Henderson, J. Spinney, J. Weinberg, and E. R. Nielsen, 2023: “Can’t think of anything more to do”: Public displays of power, privilege, and surrender in social media disaster monologues. Hum. Comput. Interact., 38, 300–321, https://doi.org/10.1080/07370024.2021.1982390.

  • Bostrom, A., R. E. Morss, J. K. Lazo, J. L. Demuth, H. Lazarus, and R. Hudson, 2016: A mental models study of hurricane forecast and warning production, communication, and decision-making. Wea. Climate Soc., 8, 111–129, https://doi.org/10.1175/WCAS-D-15-0033.1.

  • Bostrom, A., R. E. Morss, J. Vickery, J. L. Demuth, N. Hadjimichael, E. K. Laidlaw, and H. Lazrus, 2022: Modernizing the NWS tropical cyclone product suite by evaluating NWS partner decisions and information needs, Part 2: Surveys with broadcast meteorologists and emergency managers. NOAA Rep., 196 pp., https://repository.library.noaa.gov/view/noaa/47957.

  • Broad, K., A. Leiserowitz, J. Weinkle, and M. Steketee, 2007: Misinterpretations of the “cone of uncertainty” in Florida during the 2004 hurricane season. Bull. Amer. Meteor. Soc., 88, 651–668, https://doi.org/10.1175/BAMS-88-5-651.

  • Brooks, M. E., and Coauthors, 2017: glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J., 9, 378–400, https://doi.org/10.32614/RJ-2017-066.

  • Brotzge, J., and W. Donner, 2013: The tornado warning process: A review of current research, challenges, and opportunities. Bull. Amer. Meteor. Soc., 94, 1715–1733, https://doi.org/10.1175/BAMS-D-12-00147.1.

  • Cangialosi, J. P., E. Blake, M. DeMaria, A. Penny, A. Latto, E. Rappaport, and V. Tallapragada, 2020: Recent progress in tropical cyclone intensity forecasting at the National Hurricane Center. Wea. Forecasting, 35, 1913–1922, https://doi.org/10.1175/WAF-D-20-0059.1.

  • Carr, R. H., B. Montz, K. Maxfield, S. Hoekstra, K. Semmens, and E. Goldman, 2016: Effectively communicating risk and uncertainty to the public: Assessing the National Weather Service’s flood forecast and warning tools. Bull. Amer. Meteor. Soc., 97, 1649–1665, https://doi.org/10.1175/BAMS-D-14-00248.1.

  • Demuth, J. L., 2023: Dynamic risks and risk perceptions: Using longitudinal panel surveys to capture perishable social science data during hurricanes. 18th Symp. on Societal Applications: Policy, Research and Practice, Denver, CO, Amer. Meteor. Soc., 6A, https://ams.confex.com/ams/103ANNUAL/meetingapp.cgi/Paper/423712.

  • Demuth, J. L., B. H. Morrow, and J. K. Lazo, 2009: Weather forecast uncertainty information: An exploratory study with broadcast meteorologists. Bull. Amer. Meteor. Soc., 90, 1614–1618, https://doi.org/10.1175/2009BAMS2787.1.

  • Demuth, J. L., R. E. Morss, B. H. Morrow, and J. K. Lazo, 2012: Creation and communication of hurricane risk information. Bull. Amer. Meteor. Soc., 93, 1133–1145, https://doi.org/10.1175/BAMS-D-11-00150.1.

  • Demuth, J. L., and Coauthors, 2018: “Sometimes da #beachlife ain’t always da wave”: Understanding people’s evolving hurricane risk communication, risk assessments, and responses using Twitter narratives. Wea. Climate Soc., 10, 537–560, https://doi.org/10.1175/WCAS-D-17-0126.1.

  • Drake, L., 2012: Scientific prerequisites to comprehension of the tropical cyclone forecast: Intensity, track, and size. Wea. Forecasting, 27, 462–472, https://doi.org/10.1175/WAF-D-11-00041.1.

  • Green, J. A., 2021: Too many zeros and/or highly skewed? A tutorial on modelling health behaviour as count data with Poisson and negative binomial regression. Health Psychol. Behav. Med., 9, 436–455, https://doi.org/10.1080/21642850.2021.1920416.

  • Gulacsik, G., S. L. Joslyn, J. Robinson, and C. Qin, 2022: Do advisories, warnings, or color coding matter to risk perception and precautionary decisions? Wea. Climate Soc., 14, 569–583, https://doi.org/10.1175/WCAS-D-21-0070.1.

  • Houston, J. B., and Coauthors, 2015: Social media and disasters: A functional framework for social media use in disaster planning, response, and research. Disasters, 39 (1), 1–22, https://doi.org/10.1111/disa.12092.

  • Hughes, A. L., L. A. A. St. Denis, L. Palen, and K. M. Anderson, 2014: Online public communications by police and fire services during the 2012 Hurricane Sandy. Proc. SIGCHI Conf. on Human Factors in Computing Systems, Toronto, ON, Canada, Association for Computing Machinery, 1505–1514, https://doi.org/10.1145/2556288.2557227.

  • Joslyn, S. L., and J. E. LeClerc, 2012: Uncertainty forecasts improve weather-related decisions and attenuate the effects of forecast error. J. Exp. Psychol. Appl., 18, 126–140, https://doi.org/10.1037/a0025185.

  • Kent, M. L., and M. Taylor, 2021: Fostering dialogic engagement: Toward an architecture of social media for social change. Soc. Media Soc., 7 (1), 1–10, https://doi.org/10.1177/2056305120984462.

  • Klockow-McClain, K. E., R. A. McPherson, and R. P. Thomas, 2020: Cartographic design for improved decision making: Trade-offs in uncertainty visualization for tornado threats. Ann. Amer. Assoc. Geogr., 110, 314–333, https://doi.org/10.1080/24694452.2019.1602467.

  • Kogan, M., L. Palen, and K. M. Anderson, 2015: Think local, retweet global: Retweeting by the geographically-vulnerable during Hurricane Sandy. Proc. 18th ACM Conf. on Computer Supported Cooperative Work and Social Computing, Vancouver, BC, Canada, Association for Computing Machinery, 981–993, https://doi.org/10.1145/2675133.2675218.

  • Krippendorff, K., 2011: Computing Krippendorff’s alpha-reliability. Working Paper, 10 pp., https://repository.upenn.edu/asc_papers/43/.

  • Lazrus, H., O. Wilhelmi, R. Morss, J. Henderson, and A. Dietrich, 2020: Information as intervention: How hurricane risk communication interacted with vulnerability and capacities in Superstorm Sandy. Int. J. Mass Emerg. Disasters, 38, 89–120, https://doi.org/10.1177/028072702003800106.

  • MacEachren, A. M., A. Robinson, S. Hopper, S. Gardner, R. Murray, M. Gahegan, and E. Hetzler, 2005: Visualizing geospatial information uncertainty: What we know and what we need to know. Cartogr. Geogr. Inf. Sci., 32, 139–160, https://doi.org/10.1559/1523040054738936.

  • Mileti, D. S., and J. H. Sorensen, 1990: Communication of emergency public warnings: A social science perspective and state-of-the-art assessment. FEMA Tech. Rep. ORNL 6609, 162 pp., https://doi.org/10.2172/6137387.

  • Millet, B., A. P. Carter, K. Broad, A. Cairo, S. D. Evans, and S. J. Majumdar, 2020: Hurricane risk communication: Visualization and behavioral science concepts. Wea. Climate Soc., 12, 193–211, https://doi.org/10.1175/WCAS-D-19-0011.1.

  • Millet, B., Q. Ding, S. J. Majumdar, A. Cairo, B. McNoldy, S. D. Evans, and K. Broad, 2022: Perception and comprehension of the hurricane threats and impacts graphics. Proc. Hum. Factors Ergon. Soc. Annu. Meet., 66, 1711–1715, https://doi.org/10.1177/1071181322661341.

  • Morrow, B. H., J. K. Lazo, J. Rhome, and J. Feyen, 2015: Improving storm surge risk communication: Stakeholder perspectives. Bull. Amer. Meteor. Soc., 96, 35–48, https://doi.org/10.1175/BAMS-D-13-00197.1.

  • Morss, R. E., and Coauthors, 2017: Hazardous weather prediction and communication in the modern information environment. Bull. Amer. Meteor. Soc., 98, 2653–2674, https://doi.org/10.1175/BAMS-D-16-0058.1.

  • Morss, R. E., J. Vickery, H. Lazrus, J. Demuth, and A. Bostrom, 2022a: Improving tropical cyclone forecast communication by understanding NWS partners’ decision timelines and forecast information needs. Wea. Climate Soc., 14, 783–800, https://doi.org/10.1175/WCAS-D-21-0170.1.

  • Morss, R. E., J. Vickery, H. Lazrus, J. L. Demuth, and A. Bostrom, 2022b: Modernizing the NWS tropical cyclone product suite by evaluating NWS partner decisions and information needs—Part 1: Interviews with broadcast meteorologists and emergency managers. NOAA Rep., 57 pp., https://repository.library.noaa.gov/view/noaa/47956.

  • National Governors’ Association, 1979: Comprehensive Emergency Management: A Governor’s Guide. Defense Civil Preparedness Agency, 65 pp.

  • Neal, D. M., 1997: Reconsidering the phases of disaster. Int. J. Mass Emerg. Disasters, 15, 239–264, https://doi.org/10.1177/028072709701500202.

  • NHC, 2023: Definition of the NHC track forecast cone. NOAA/NWS, accessed 18 April 2023, https://www.nhc.noaa.gov/aboutcone.shtml.

  • Olson, M. K., J. Sutton, S. C. Vos, R. Prestley, S. L. Renshaw, and C. T. Butts, 2019: Build community before the storm: The National Weather Service’s social media engagement. J. Contingencies Crisis Manage., 27, 359–373, https://doi.org/10.1111/1468-5973.12267.

  • Palen, L., and K. M. Anderson, 2016: Crisis informatics—New data for extraordinary times. Science, 353, 224–225, https://doi.org/10.1126/science.aag2579.

  • Palen, L., S. Vieweg, S. B. Liu, and A. L. Hughes, 2009: Crisis in a networked world: Features of computer-mediated communication in the April 16, 2007, Virginia Tech event. Soc. Sci. Comput. Rev., 27, 467–480, https://doi.org/10.1177/0894439309332302.

  • Perreault, M. F., J. B. Houston, and L. Wilkins, 2014: Does scary matter? Testing the effectiveness of new National Weather Service tornado warning messages. Commun. Stud., 65, 484–499, https://doi.org/10.1080/10510974.2014.956942.

  • Prestley, R., and R. Morss, 2023: “Hurricane Irma,” in Analysis of tweets posted by professional sources of weather forecast and warning information during tropical cyclone events, version 1. DesignSafe-CI, https://doi.org/10.17603/ds2-jfsc-8902.

  • Prestley, R., M. K. Olson, S. C. Vos, and J. Sutton, 2020: Machines, monsters, and coffin corners: Broadcast meteorologists’ use of figurative and intense language during hurricane Harvey. Bull. Amer. Meteor. Soc., 101, E1329–E1339, https://doi.org/10.1175/BAMS-D-19-0205.1.

  • Procopio, C. H., and S. T. Procopio, 2007: Do you know what it means to miss New Orleans? Internet communication, geographic community, and social capital in crisis. J. Appl. Commun. Res., 35, 67–87, https://doi.org/10.1080/00909880601065722.

  • Radford, L., J. C. Senkbeil, and M. Rockman, 2013: Suggestions for alternative tropical cyclone warning graphics in the USA. Disaster Prev. Manage., 22, 192–209, https://doi.org/10.1108/DPM-06-2012-0064.

  • Rane, H., and S. Salem, 2012: Social media, social movements and the diffusion of ideas in the Arab uprisings. J. Int. Commun., 18, 97–111, https://doi.org/10.1080/13216597.2012.662168.

  • Ruginski, I. T., and Coauthors, 2016: Non-expert interpretations of hurricane forecast uncertainty visualizations. Spat. Cognit. Comput., 16, 154–172, https://doi.org/10.1080/13875868.2015.1137577.

  • Sherman-Morris, K., 2005: Tornadoes, television and trust—A closer look at the influence of the local weathercaster during severe weather. Global Environ. Change, 6B, 201–210, https://doi.org/10.1016/j.hazards.2006.10.002.

  • Sherman-Morris, K., K. B. Antonelli, and C. C. Williams, 2015: Measuring the effectiveness of the graphical communication of hurricane storm surge threat. Wea. Climate Soc., 7, 69–82, https://doi.org/10.1175/WCAS-D-13-00073.1.

  • Sherman-Morris, K., T. Pechacek, D. J. Griffin, and J. Senkbeil, 2020: Tornado warning awareness, information needs and the barriers to protective action of individuals who are blind. Int. J. Disaster Risk Reduct., 50, 101709, https://doi.org/10.1016/j.ijdrr.2020.101709.

  • Shivers-Williams, C. A., and K. E. Klockow-McClain, 2021: Geographic scale and probabilistic forecasts: A trade-off for protective decisions? Nat. Hazards, 105, 2283–2306, https://doi.org/10.1007/s11069-020-04400-2.

  • Silver, A., 2019: Public attention to risks, hazards, and disasters: A retrospective literature review and proposed conceptual model. Risks Hazards Crisis Public Policy, 10, 294–310, https://doi.org/10.1002/rhc3.12165.

  • Sorensen, J. H., 2000: Hazard warning systems: Review of 20 years of progress. Nat. Hazards Rev., 1, 119–125, https://doi.org/10.1061/(ASCE)1527-6988(2000)1:2(119).

  • Spiegelhalter, D., M. Pearson, and I. Short, 2011: Visualizing uncertainty about the future. Science, 333, 1393–1400, https://doi.org/10.1126/science.1191181.

  • Starbird, K., and L. Palen, 2010: Pass it on?: Retweeting in mass emergency. Proc. Seventh Int. ISCRAM Conf., Seattle, WA, ISCRAM, http://idl.iscram.org/files/starbird/2010/970_Starbird+Palen2010.pdf.

  • St. Denis, L. A., L. Palen, and K. Anderson, 2014: Mastering social media: An analysis of Jefferson County’s communications during the 2013 Colorado floods. Proc. 11th Int. ISCRAM Conf., University Park, PA, ISCRAM, 737–746, http://idl.iscram.org/files/hiltz/2014/581_Hiltz_etal2014.pdf.

  • Sutton, J., and L. M. Fischer, 2021: Understanding visual risk communication messages: An analysis of visual attention allocation and think-aloud response to tornado graphics. Wea. Climate Soc., 13, 173–188, https://doi.org/10.1175/WCAS-D-20-0042.1.

  • Sutton, J., E. S. Spiro, B. Johnson, S. Fitzhugh, B. Gibson, and C. T. Butts, 2014: Warning tweets: Serial transmission of messages during the warning phase of a disaster event. Inf. Commun. Soc., 17, 765–787, https://doi.org/10.1080/1369118X.2013.862561.

  • Sutton, J., C. B. Gibson, N. E. Phillips, E. S. Spiro, C. League, B. Johnson, S. M. Fitzhugh, and C. T. Butts, 2015a: A cross-hazard analysis of terse message retransmission on Twitter. Proc. Natl. Acad. Sci. USA, 112, 14 793–14 798, https://doi.org/10.1073/pnas.1508916112.

  • Sutton, J., C. B. Gibson, E. S. Spiro, C. League, S. M. Fitzhugh, and C. T. Butts, 2015b: What it takes to get passed on: Message content, style, and structure as predictors of retransmission in the Boston Marathon bombing response. PLOS ONE, 10, e0134452, https://doi.org/10.1371/journal.pone.0134452.

  • Sutton, J., S. C. Vos, M. W. Wood, and M. Turner, 2018: Designing effective tsunami messages: Examining the role of short messages and fear in warning response. Wea. Climate Soc., 10, 75–87, https://doi.org/10.1175/WCAS-D-17-0032.1.

  • Sutton, J., S. L. Renshaw, S. C. Vos, M. K. Olson, R. Prestley, C. B. Gibson, and C. T. Butts, 2019: Getting the word out, rain or shine: The impact of message features and hazard context on message passing online. Wea. Climate Soc., 11, 763–776, https://doi.org/10.1175/WCAS-D-19-0021.1.

  • Sutton, J., L. Fischer, L. E. James, and S. E. Sheff, 2020: Earthquake early warning message testing: Visual attention, behavioral responses, and message perceptions. Int. J. Disaster Risk Reduct., 49, 101664, https://doi.org/10.1016/j.ijdrr.2020.101664.

  • Sutton, J., L. Fischer, and M. W. Wood, 2021: Tornado warning guidance and graphics: Implications of the inclusion of protective action information on perceptions and efficacy. Wea. Climate Soc., 13, 1003–1014, https://doi.org/10.1175/WCAS-D-21-0097.1.

  • Trujillo-Falcón, J. E., and Coauthors, 2022: ¿Aviso o Alerta? Developing effective, inclusive, and consistent watch and warning translations for U.S. Spanish speakers. Bull. Amer. Meteor. Soc., 103, E2791–E2803, https://doi.org/10.1175/BAMS-D-22-0050.1.

  • Vos, S. C., J. Sutton, Y. Yu, S. L. Renshaw, M. K. Olson, C. B. Gibson, and C. T. Butts, 2018: Retweeting risk communication: The role of threat and efficacy. Risk Anal., 38, 2580–2598, https://doi.org/10.1111/risa.13140.

  • Williams, C. A., and G. M. Eosco, 2021: Is a consistent message achievable? Defining “message consistency” for weather enterprise researchers and practitioners. Bull. Amer. Meteor. Soc., 102, E279–E295, https://doi.org/10.1175/BAMS-D-18-0250.1.

  • Williams, C. A., A. Grundstein, and J. So, 2022: An examination of the SPC’s convective outlook products and risk category system among members of the public. NOAA Rep., 55 pp., https://repository.library.noaa.gov/view/noaa/44639.

  • Wisner, B., P. Blaikie, T. Cannon, and I. Davis, 2004: At Risk: Natural Hazards, People’s Vulnerability and Disasters. Routledge, 471 pp.

  • Witt, J. K., Z. M. Labe, A. C. Warden, and B. A. Clegg, 2023: Visualizing uncertainty in hurricane forecasts with animated risk trajectories. Wea. Climate Soc., 15, 407–424, https://doi.org/10.1175/WCAS-D-21-0173.1.

  • Wolbers, J., S. Kuipers, and A. Boin, 2021: A systematic review of 20 years of crisis and disaster research: Trends and progress. Risks Hazards Crisis Public Policy, 12, 374–392, https://doi.org/10.1002/rhc3.12244.

  • Wukich, C., 2016: Government social media messages across disaster phases. J. Contingencies Crisis Manage., 24, 230–243, https://doi.org/10.1111/1468-5973.12119.

  • Zheng, H., D. H.-L. Goh, E. W. J. Lee, C. S. Lee, and Y.-L. Theng, 2022: Understanding the effects of message cues on COVID-19 information sharing on Twitter. J. Assoc. Inf. Sci. Technol., 73, 847–862, https://doi.org/10.1002/asi.24587.

    • Contextualizing Disaster Phases Using Social Media Data: Hurricane Risk Visualizations during the Forecast and Warning Phase of Hurricane Irma (2024)
    Top Articles
    T-Shirts bedrucken, T-Shirt Druck online | Spreadshirt
    Online Eigenes T-Shirt designen & T-Shirt bedrucken
    Routing Number For Rally Credit Union
    True West 2017-12 - PDF Free Download
    Micro Disasters: The Case of Serial Killer Jeffrey Dahmer
    What Jeffrey Dahmer's Childhood Was Actually Like - Grunge
    Milk And Mocha Bear Gifs
    L9110 Dual Channel Stepper Motor Drive Board for Arduino - Eckstein S, 3,60 €
    Busted Newspaper Jefferson County KY Mugshots
    18 suspects charged in Moore County drug bust
    Papaskwrt
    Albany Times Union Obituaries Today's Date
    Latest Posts
    Purdue Pharma: Wie ein Konzern die Opioidkrise in den USA auslöste
    Herren T-Shirts online bei ABOUT YOU kaufen
    Article information

    Author: Ray Christiansen

    Last Updated:

    Views: 5991

    Rating: 4.9 / 5 (49 voted)

    Reviews: 80% of readers found this page helpful

    Author information

    Name: Ray Christiansen

    Birthday: 1998-05-04

    Address: Apt. 814 34339 Sauer Islands, Hirtheville, GA 02446-8771

    Phone: +337636892828

    Job: Lead Hospitality Designer

    Hobby: Urban exploration, Tai chi, Lockpicking, Fashion, Gunsmithing, Pottery, Geocaching

    Introduction: My name is Ray Christiansen, I am a fair, good, cute, gentle, vast, glamorous, excited person who loves writing and wants to share my knowledge and understanding with you.