Editorial Type:
Article
Article Type:
Research Article

Public Attention to Natural Hazard Warnings on Social Media in China

Xi Hu School of International Economics and Trade, Nanjing University of Finance and Economics, Nanjing, Jiangsu, China

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Xiujuan Zhang School of Management, University of Science and Technology of China, Hefei, Anhui, China

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Jiuchang Wei State Key Laboratory of Fire Science, and School of Management, University of Science and Technology of China, Hefei, and Center for Crisis Management Research, School of Public Policy and Management, Tsinghua University, Beijing, China

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Print Publication:
01 Jan 2019
DOI:
https://doi.org/10.1175/WCAS-D-17-0039.1
Page(s):
183–197
Restricted access

Abstract

Hazard warning is vital in disaster management. The rapid development of social media allows warning producers and receivers to exchange warning messages effectively and sufficiently. This study investigates the factors that influence public attention to natural hazard warning information on social media. Drawing from the protective action decision model and framing theory, this study classifies antecedents into three groups, namely, hazard information, publisher’s/reader’s characteristics, and frame setting. To test the hypotheses empirically, we select Sina Weibo, the leading social network in China, as the research context. From this platform, 3452 warning messages issued by authorities in the target area are collected. We code each message based on its attributes that are related to our study for linear regression analyses. Results show that all the factors related to publisher’s/reader’s characteristics exert significant effects on public attention. However, the affected range indicated by a warning message and the formality of the message’s language are not significantly related to public attention to the message.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Jiuchang Wei, weijc@ustc.edu.cn

Abstract

Hazard warning is vital in disaster management. The rapid development of social media allows warning producers and receivers to exchange warning messages effectively and sufficiently. This study investigates the factors that influence public attention to natural hazard warning information on social media. Drawing from the protective action decision model and framing theory, this study classifies antecedents into three groups, namely, hazard information, publisher’s/reader’s characteristics, and frame setting. To test the hypotheses empirically, we select Sina Weibo, the leading social network in China, as the research context. From this platform, 3452 warning messages issued by authorities in the target area are collected. We code each message based on its attributes that are related to our study for linear regression analyses. Results show that all the factors related to publisher’s/reader’s characteristics exert significant effects on public attention. However, the affected range indicated by a warning message and the formality of the message’s language are not significantly related to public attention to the message.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Jiuchang Wei, weijc@ustc.edu.cn
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  • Acar, A., and Y. Muraki, 2011: Twitter for crisis communication: Lessons learned from Japan’s tsunami disaster. Int. J. Web Based Commun., 7, 392–402, https://doi.org/10.1504/IJWBC.2011.041206.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Ash, K. D., R. L. Schumann, and G. C. Bowser, 2014: Tornado warning trade-offs: Evaluating choices for visually communicating risk. Wea. Climate Soc., 6, 104–118, https://doi.org/10.1175/WCAS-D-13-00021.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Berren, M. R., A. Beigel, and S. Ghertner, 1980: A typology for the classification of disasters. Commun. Mental Health J., 16, 103–111, https://doi.org/10.1007/BF00778582.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Bonsón, E., S. Royo, and M. Ratkai, 2015: Citizens’ engagement on local governments’ Facebook sites. An empirical analysis: The impact of different media and content types in western Europe. Gov. Inf. Quart., 32, 52–62, https://doi.org/10.1016/j.giq.2014.11.001.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Brantner, C., K. Lobinger, and I. Wetzstein, 2011: Effects of visual framing on emotional responses and evaluations of news stories about the Gaza Conflict 2009. J. Mass Commun. Quart., 88, 523–540, https://doi.org/10.1177/107769901108800304.

    • Search Google Scholar
    • Export Citation

  • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Bubeck, P., W. J. Botzen, and J. C. Aerts, 2012: A review of risk perceptions and other factors that influence flood mitigation behavior. Risk Anal., 32, 1481–1495, https://doi.org/10.1111/j.1539-6924.2011.01783.x.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Cameron, M. A., R. Power, B. Robinson, and J. Yin, 2012: Emergency situation awareness from Twitter for crisis management. Proc. 21st Int. Conf. on World Wide Web, Lyon, France, International World Wide Web Conference Committee, 695–698, https://doi.org/10.1145/2187980.2188183.

    • Crossref
    • Export Citation

  • Carley, K. M., M. Malik, P. M. Landwehr, J. Pfeffer, and M. Kowalchuck, 2016: Crowd sourcing disaster management: The complex nature of Twitter usage in Padang Indonesia. Saf. Sci., 90, 48–61, https://doi.org/10.1016/j.ssci.2016.04.002.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Casteel, M. A., and J. R. Downing, 2013: How individuals process NWS weather warning messages on their cell phones. Wea. Climate Soc., 5, 254–265, https://doi.org/10.1175/WCAS-D-12-00031.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Chatfield, A. T., and U. Brajawidagda, 2013: Twitter early tsunami warning system: A case study in Indonesia’s natural disaster management. Proc. 46th Hawaii Int. Conf. on System Sciences, Wailea, HI, IEEE, 2050–2060, https://doi.org/10.1109/HICSS.2013.579.

    • Crossref
    • Export Citation

  • Chavez, C., M. A. Repas, and T. Stefaniak, 2010: A new way to communicate with residents: Local government use of social media to prepare for emergencies. ICMA Rep., 16 pp., http://www.unapcict.org/ecohub/a-new-way-to-communicate-with-residents-local-government-use-of-social-media-to-prepare-for-emergencies/at_download/attachment1.

  • Chew, C., and G. Eysenbach, 2010: Pandemics in the age of Twitter: Content analysis of tweets during the 2009 H1N1 outbreak. PLOS ONE, 5, e14118, https://doi.org/10.1371/journal.pone.0014118.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Chong, D., and J. N. Druckman, 2007: Framing theory. Annu. Rev. Polit. Sci., 10, 103–126, https://doi.org/10.1146/annurev.polisci.10.072805.103054.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Chou, C. H., F. M. Zahedi, and H. Zhao, 2014: Ontology-based evaluation of natural disaster management websites: A multistakeholder perspective. Manage. Inf. Syst. Quart., 38, 997–1016, https://doi.org/10.25300/MISQ/2014/38.4.03.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Claeys, A. S., and V. Cauberghe, 2014: What makes crisis response strategies work? The impact of crisis involvement and message framing. J. Bus. Res., 67, 182–189, https://doi.org/10.1016/j.jbusres.2012.10.005.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Comstock, R. D., and S. Mallonee, 2005: Comparing reactions to two severe tornadoes in one Oklahoma community. Disasters, 29, 277–287, https://doi.org/10.1111/j.0361-3666.2005.00291.x.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Demuth, J. L., R. E. Morss, J. K. Lazo, and D. C. Hilderbrand, 2013: Improving effectiveness of weather risk communication on the NWS point-and-click web page. Wea. Forecasting, 28, 711–726, https://doi.org/10.1175/WAF-D-12-00118.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Entman, R. M., 2007: Framing bias: Media in the distribution of power. J. Commun., 57, 163–173, https://doi.org/10.1111/j.1460-2466.2006.00336.x.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Gladwin, H., J. K. Lazo, B. H. Morrow, W. G. Peacock, and H. E. Willoughby, 2009: Social science research needs for the hurricane forecast and warning system. Bull. Amer. Meteor. Soc., 90, 25–30, https://doi.org/10.1175/2008BAMS2606.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Grunig, J. D., and T. Hunt, 1984: Managing Public Relations. Holt, Rinehart and Winston, 550 pp.

  • Guan, W., H. Gao, M. Yang, Y. Li, H. Ma, W. Qian, Z. Cao, and X. Yang, 2014: Analyzing user behavior of the micro-blogging website Sina Weibo during hot social events. Physica A, 395, 340–351, https://doi.org/10.1016/j.physa.2013.09.059.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Hoekstra, S., K. Klockow, R. Riley, J. Brotzge, H. Brooks, and S. Erickson, 2011: A preliminary look at the social perspective of warn-on-forecast: Preferred tornado warning lead time and the general public’s perceptions of weather risks. Wea. Climate Soc., 3, 128–140, https://doi.org/10.1175/2011WCAS1076.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Honan, M., 2011: Watch the Virginia earthquake spread across Twitter. Gizmodo, 25 August, http://gizmodo.com/5834048/watch-the-virginia-earthquake-spread-across-twitter.

  • Höppner, C., R. Whittle, M. Bründl, and M. Buchecker, 2012: Linking social capacities and risk communication in Europe: A gap between theory and practice? Nat. Hazards, 64, 1753–1778, https://doi.org/10.1007/s11069-012-0356-5.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Houston, J. B., B. Pfefferbaum, and C. E. Rosenholtz, 2012: Disaster news: Framing and frame changing in coverage of major U.S. natural disasters, 2000–2010. J. Mass Commun. Quart., 89, 606–623, https://doi.org/10.1177/1077699012456022.

    • Search Google Scholar
    • Export Citation

  • Hu, X., X. Wu, P. Yin, and X. Zheng, 2017: An investigation into consumers’ continued social shopping intentions. Proc. Pacific-Asia Conf. on Information Systems, Kedah, Malaysia, Association for Information Systems, 63, https://aisel.aisnet.org/pacis2017/63/.

  • Hyvärinen, O., and E. Saltikoff, 2010: Social media as a source of meteorological observations. Mon. Wea. Rev., 138, 3175–3184, https://doi.org/10.1175/2010MWR3270.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Koenker, R., and G. Bassett Jr., 1982: Robust tests for heteroscedasticity based on regression quantiles. Econometrica, 50, 43–61, https://doi.org/10.2307/1912528.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Lee, K. L., R. J. Meyer, and E. T. Bradlow, 2009: Analyzing risk response dynamics on the web: The case of Hurricane Katrina. Risk Anal., 29, 1779–1792, https://doi.org/10.1111/j.1539-6924.2009.01304.x.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Levin, D. Z., and R. Cross, 2004: The strength of weak ties you can trust: The mediating role of trust in effective knowledge transfer. Manage. Sci., 50, 1477–1490, https://doi.org/10.1287/mnsc.1030.0136.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Levin, I. P., and G. J. Gaeth, 1988: How consumers are affected by the framing of attribute information before and after consuming the product. J. Consum. Res., 15, 374–378, https://doi.org/10.1086/209174.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Li, L., M. F. Goodchild, and B. Xu, 2013: Spatial, temporal, and socioeconomic patterns in the use of Twitter and Flickr. Cartogr. Geogr. Inf. Sci., 40, 61–77, https://doi.org/10.1080/15230406.2013.777139.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Lin, X., K. A. Lachlan, and P. R. Spence, 2016: Exploring extreme events on social media: A comparison of user reposting/retweeting behaviors on Twitter and Weibo. Comput. Human Behav., 65, 576–581, https://doi.org/10.1016/j.chb.2016.04.032.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Lindell, M. K., and R. W. Perry, 2012: The protective action decision model: Theoretical modifications and additional evidence. Risk Anal., 32, 616–632, https://doi.org/10.1111/j.1539-6924.2011.01647.x.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Liu, B. F., J. D. Fraustino, and Y. Jin, 2016: Social media use during disasters: How information form and source influence intended behavioral responses. Commun. Res., 43, 626–646, https://doi.org/10.1177/0093650214565917.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Liu, S. H., H. L. Liao, and J. A. Pratt, 2009: Impact of media richness and flow on e-learning technology acceptance. Comput. Educ., 52, 599–607, https://doi.org/10.1016/j.compedu.2008.11.002.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Ma, X. J., W. X. Wang, Y. C. Lai, and Z. Zheng, 2010: Information explosion on complex networks and control. Eur. Phys. J., 76B, 179–183, https://doi.org/10.1140/epjb/e2010-00208-1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Meyer, R. J., E. J. Baker, K. F. Broad, J. Czajkowski, and B. Orlove, 2014: The dynamics of hurricane risk perception: Real-time evidence from the 2012 Atlantic hurricane season. Bull. Amer. Meteor. Soc., 95, 1389–1404, https://doi.org/10.1175/BAMS-D-12-00218.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Miao, Q., and D. Popp, 2014: Necessity as the mother of invention: Innovative responses to natural disasters. J. Environ. Econ. Manage., 68, 280–295, https://doi.org/10.1016/j.jeem.2014.06.003.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • O’Reilly, T., 2007: What is web 2.0: Design patterns and business models for the next generation of software. Commun. Strategies, 65, 17–37, https://mpra.ub.uni-muenchen.de/id/eprint/4580.

    • Search Google Scholar
    • Export Citation

  • Ripberger, J. T., 2011: Capturing curiosity: Using Internet search trends to measure public attentiveness. Policy Stud. J., 39, 239–259, https://doi.org/10.1111/j.1541-0072.2011.00406.x.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Ripberger, J. T., H. C. Jenkins-Smith, C. L. Silva, D. E. Carlson, and M. Henderson, 2014: Social media and severe weather: Do tweets provide a valid indicator of public attention to severe weather risk communication? Wea. Climate Soc., 6, 520–530, https://doi.org/10.1175/WCAS-D-13-00028.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Rive, G., J. Hare, J. Thomas, and K. Nankivell, 2012: Social media in an emergency: A best practice guide. Wellington Region CDEM Group Rep., 58 pp., https://www.civildefence.govt.nz/assets/Uploads/CDEM-Resilience-Fund/CDEM-Resilience-Fund-2011-03-social-media-in-an-emergency-guide.pdf.

  • Ros-Diego, V. J., and A. Castelló-Martínez, 2011: CSR communication through online social media. Rev. Lat. Comun. Soc., 67, 47–67, https://doi.org/10.4185/RLCS-067-947-047-067.

    • Search Google Scholar
    • Export Citation

  • Sakaki, T., M. Okazaki, and Y. Matsuo, 2010: Earthquake shakes Twitter users: Real-time event detection by social sensors. Proc. 19th Int. Conf. on World Wide Web, Raleigh, NC, International World Wide Web Conference Committee, 851–860.

    • Crossref
    • Export Citation

  • Scheufele, D. A., 1999: Framing as a theory of media effects. J. Commun., 49, 103–122, https://doi.org/10.1111/j.1460-2466.1999.tb02784.x.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Scheufele, D. A., and D. Tewksbury, 2007: Framing, agenda setting, and priming: The evolution of three media effects models. J. Commun., 57, 9–20, https://doi.org/10.1111/j.0021-9916.2007.00326.x.

    • Search Google Scholar
    • Export Citation

  • Schumacher, R. S., D. T. Lindsey, A. B. Schumacher, J. Braun, S. D. Miller, and J. L. Demuth, 2010: Multidisciplinary analysis of an unusual tornado: Meteorology, climatology, and the communication and interpretation of warnings. Wea. Forecasting, 25, 1412–1429, https://doi.org/10.1175/2010WAF2222396.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Semetko, H. A., and P. M. Valkenburg, 2000: Framing European politics: A content analysis of press and television news. J. Commun., 50, 93–109, https://doi.org/10.1111/j.1460-2466.2000.tb02843.x.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Simmons, K. M., and D. Sutter, 2008: Tornado warnings, lead times, and tornado casualties: An empirical investigation. Wea. Forecasting, 23, 246–258, https://doi.org/10.1175/2007WAF2006027.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • 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).

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Stafford, T. F., M. R. Stafford, and L. L. Schkade, 2004: Determining uses and gratifications for the Internet. Decis. Sci., 35, 259–288, https://doi.org/10.1111/j.00117315.2004.02524.x.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Stapa, S. H., and A. H. Shaari, 2012: Understanding online communicative language features in social networking environment. GEMA, 12, 817–830, http://www.ukm.my/ppbl/Gema/gemacurrentissues.html.

    • Search Google Scholar
    • Export Citation

  • Tang, Z., L. Zhang, F. Xu, and H. Vo, 2015: Examining the role of social media in California’s drought risk management in 2014. Nat. Hazards, 79, 171–193, https://doi.org/10.1007/s11069-015-1835-2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Tumasjan, A., T. O. Sprenger, P. G. Sandner, and I. M. Welpe, 2010: Predicting elections with Twitter: What 140 characters reveal about political sentiment. Proc. Fourth Int. AAAI Conf. on Weblogs and Social Media, Washington, DC, AAAI, 178–185.

  • Tversky, A., and D. Kahneman, 1981: The framing of decisions and the psychology of choice. Science, 211, 453–458, https://doi.org/10.1126/science.7455683.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Vieweg, S., A. L. Hughes, K. Starbird, and L. Palen, 2010: Microblogging during two natural hazards events: What Twitter may contribute to situational awareness. Proc. SIGCHI Conf. on Human Factors in Computing Systems, Atlanta, GA, ACM, 1079–1088.

    • Crossref
    • Export Citation

  • Weber, R. P., 1990: Basic Content Analysis. 2nd ed. Sage Publications, 96 pp.

    • Crossref
    • Export Citation

  • Wei, J., J. Xu, and D. Zhao, 2015: Public engagement with firms on social media in China. J. Inf. Sci., 41, 624–639, https://doi.org/10.1177/0165551515586712.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wei, J., F. Wang, and M. K. Lindell, 2016: The evolution of stakeholders’ perceptions of disaster: A model of information flow. J. Assoc. Inf. Sci. Technol., 67, 441–453, https://doi.org/10.1002/asi.23386.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Westerman, D., P. R. Spence, and B. Van Der Heide, 2014: Social media as information source: Recency of updates and credibility of information. J. Comput. Mediat. Commun., 19, 171–183, https://doi.org/10.1111/jcc4.12041.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wogalter, M. S., 2006: Handbook of Warnings. CRC Press, 864 pp.

    • Crossref
    • Export Citation

  • Wong, T. F., and Y. Y. Yan, 2002: Perceptions of severe weather warnings in Hong Kong. Meteor. Appl., 9, 377–382, https://doi.org/10.1017/S1350482702003110.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Zhang, F., and Coauthors, 2007: An in-person survey investigating public perceptions of and responses to Hurricane Rita forecasts along the Texas Coast. Wea. Forecasting, 22, 1177–1190, https://doi.org/10.1175/2007WAF2006118.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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