Volume 15 Issue 3
Jun.  2024
Turn off MathJax
Article Contents
Longfei Zheng, Lei Chen, Fenjie Long, Jianing Liu, Lei Li. Reducing Social Media Attention Inequality in Disasters: The Role of Official Media During Rainstorm Disasters in China[J]. International Journal of Disaster Risk Science, 2024, 15(3): 388-403. doi: 10.1007/s13753-024-00562-w
Citation: Longfei Zheng, Lei Chen, Fenjie Long, Jianing Liu, Lei Li. Reducing Social Media Attention Inequality in Disasters: The Role of Official Media During Rainstorm Disasters in China[J]. International Journal of Disaster Risk Science, 2024, 15(3): 388-403. doi: 10.1007/s13753-024-00562-w

Reducing Social Media Attention Inequality in Disasters: The Role of Official Media During Rainstorm Disasters in China

doi: 10.1007/s13753-024-00562-w
Funds:

This research was supported by the China Postdoctoral Science Foundation (2023M730284), the National Social Science Foundation of China (20BJY178), the National Natural Science Foundation of China (42301185), and the Fundamental Research Funds for the Central Universities (2022NTST17).

  • Accepted Date: 2024-05-19
  • Available Online: 2024-10-26
  • Publish Date: 2024-06-24
  • Unequal social media attention can lead to potentially uneven distribution of disaster-relief funds, resulting in long-term inequality among regions after disasters. This study aimed to measure inequalities in social media attention to regions during disasters and explore the role of official media in reducing such inequality. This is performed by employing social media, official media, and official aggregated statistics regarding China’s rainstorm disasters. Through a set of panel-data regressions and robustness tests, three main conclusions were drawn: (1) There were inequalities among regions regarding social media attention they received during rainstorm disasters. For disasters of the same magnitude, regions with low economic outcome per capita received less attention on social media. (2) Official media can reduce inequality in social media attention during disasters. Official media statements can encourage netizens to pay attention to disaster-stricken areas, and especially the overlooked underdeveloped areas. (3) Of all the measures taken by official media, timely, accurate, and open disclosure of disaster occurrences proved to be the most potent means of leveling the playing field in terms of social media attention; contrarily, promotional or booster-type messages proved futile in this regard. These findings revealed the vulnerabilities within social media landscapes that affect disaster relief response, shedding light on the role of official guidance in mitigating inequalities in social media attention during such crises. Our study advises social media stakeholders and policymakers on formulating more equitable crisis communication strategies to bridge the gap in social media attention and foster a more balanced and just relief process.
  • loading
  • [1]
    Ahmed, A., and S. Sinnappan. 2013. The role of social media during Queensland floods: An empirical investigation on the existence of multiple communities of practice (MCoPs). Pacific Asia Journal of the Association for Information Systems 5(2): Article 2.
    [2]
    Ainuddin, S., D.P. Aldrich, J.K. Routray, S. Ainuddin, and A. Achkazai. 2013. The need for local involvement: Decentralization of disaster management institutions in Baluchistan, Pakistan. International Journal of Disaster Risk Reduction 6: 50-58.
    [3]
    Alam, F., F. Ofli, and M. Imran. 2020. Descriptive and visual summaries of disaster events using artificial intelligence techniques: Case studies of Hurricanes Harvey, Irma, and Maria. Behaviour & Information Technology 39(3): 288-318.
    [4]
    Bignami, D.F., A. Dragoni, and G. Menduni. 2018. Assessing and improving flood and landslide community social awareness and engagement via a web platform: The case of Italy. International Journal of Disaster Risk Science 9(4): 530-540.
    [5]
    Blei, D.M., A.Y. Ng, and M.I. Jordan. 2003. Latent Dirichlet allocation. Journal of Machine Learning Research 3: 993-1022.
    [6]
    Boas, I., C. Chen, H. Wiegel, and G. He. 2020. The role of social media-led and governmental information in China’s urban disaster risk response: The case of Xiamen. International Journal of Disaster Risk Reduction 51: Article 101905.
    [7]
    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. Safety Science 90: 48-61.
    [8]
    Castillo, C. 2016. Big crisis data: Social media in disasters and time-critical situations. Cambridge: Cambridge University Press.
    [9]
    Checker, M. 2007. “But I know it’s true”: Environmental risk assessment, justice, and anthropology. Human Organization 66(2): 112-124.
    [10]
    Cheng, X., G. Han, Y. Zhao, and L. Li. 2019. Evaluating social media response to urban flood disaster: Case study on an East Asian city (Wuhan, China). Sustainability 11(19): Article 5330.
    [11]
    Christensen, T., and P. Lægreid. 2020. Balancing governance capacity and legitimacy: How the Norwegian government handled the COVID-19 crisis as a high performer. Public Administration Review 80(5): 774-779.
    [12]
    Coombs, W.T. 1995. Choosing the right words: The development of guidelines for the selection of the “appropriate” crisis-response strategies. Management Communication Quarterly 8(4): 447-476.
    [13]
    Coombs, W.T. 2007. Protecting organization reputations during a crisis: The development and application of situational crisis communication theory. Corporate Reputation Review 10: 163-176.
    [14]
    Cutter, S.L., K.D. Ash, and C.T. Emrich. 2014. Urban-rural differences in disaster resilience. Annals of the American Association of Geographers 106(6): 1236-1252.
    [15]
    Dahal, L., M.S. Idris, and V. Bravo. 2021. “It helped us, and it hurt us” The role of social media in shaping agency and action among youth in post-disaster Nepal. Journal of Contingencies and Crisis management 29(2): 217-225.
    [16]
    Dargin, J.S., C. Fan, and A. Mostafavi. 2021. Vulnerable populations and social media use in disasters: Uncovering the digital divide in three major US hurricanes. International Journal of Disaster Risk Reduction 54: Article 102043.
    [17]
    Fan, C., M. Esparza, J. Dargin, F. Wu, B. Oztekin, and A. Mostafavi. 2020. Spatial biases in crowdsourced data: Social media content attention concentrates on populous areas in disasters. Computers Environment and Urban Systems 83: Article 101514.
    [18]
    Fang, J., J. Hu, X. Shi, and L. Zhao. 2019. Assessing disaster impacts and response using social media data in China: A case study of 2016 Wuhan rainstorm. International Journal of Disaster Risk Reduction 34: 275-282.
    [19]
    Field, C.B., V. Barros, T.F. Stocker, and D. Qin. 2012. Managing the risks of extreme events and disasters to advance climate change adaptation: Special report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press.
    [20]
    Graham, M.W., E.J. Avery, and S. Park. 2015. The role of social media in local government crisis communications. Public Relations Review 41(3): 386-394.
    [21]
    Granell, C., and F.O. Ostermann. 2016. Beyond data collection: Objectives and methods of research using VGI and geo-social media for disaster management. Computers, Environment and Urban Systems 59: 231-243.
    [22]
    Han, Z., M. Shen, H. Liu, and Y. Peng. 2022. Topical and emotional expressions regarding extreme weather disasters on social media: A comparison of posts from official media and the public. Humanities and Social Sciences Communications 9(1): 1-10.
    [23]
    Holladay, S.J. 2010. Are they practicing what we are preaching? An investigation of crisis communication strategies in the media coverage of chemical accidents. In The handbook of crisis communication, ed. W.T. Coombs, and S.J. Holladay, 159-180. Hoboken: John Wiley & Sons.
    [24]
    Houston, J.B., J. Hawthorne, M.F. Perreault, E.H. Park, M. Goldstein Hode, M.R. Halliwell, S.E. Turner McGowen, and R. Davis et al. 2015. Social media and disasters: A functional framework for social media use in disaster planning, response, and research. Disasters 39(1): 1-22.
    [25]
    Karami, A., V. Shah, R. Vaezi, and A. Bansal. 2020. Twitter speaks: A case of national disaster situational awareness. Journal of Information Science 46(3): 313-324.
    [26]
    Kelman, I. 2015. Climate change and the Sendai Framework for Disaster Risk Reduction. International Journal of Disaster Risk Science 6(1): 117-127.
    [27]
    Kent, J.D., and H.T. Capello Jr. 2013. Spatial patterns and demographic indicators of effective social media content during the Horsethief Canyon fire of 2012. Cartography and Geographic Information Science 40(2): 78-89.
    [28]
    Kim, D.K.D., and T.P. Madison. 2020. Public risk perception attitude and information-seeking efficacy on floods: A formative study for disaster preparation campaigns and policies. International Journal of Disaster Risk Science 11(5): 592-601.
    [29]
    Kim, H.K., and J. Niederdeppe. 2013. The role of emotional response during an H1N1 influenza pandemic on a college campus. Journal of Public Relations Research 25(1): 30-50.
    [30]
    Li, Y., Y. Chandra, and Y. Fan. 2022. Unpacking government social media messaging strategies during the COVID-19 pandemic in China. Policy & Internet 14(3): 651-672.
    [31]
    Li, Y., J. Shin, J. Sun, H.M. Kim, Y. Qu, and A. Yang. 2021. Organizational sensemaking in tough times: The ecology of NGOs’ COVID-19 issue discourse communities on social media. Computers in Human Behavior 122: Article 106838.
    [32]
    Liu, W., C.-H. Lai, and W.W. Xu. 2018. Tweeting about emergency: A semantic network analysis of government organizations’ social media messaging during hurricane Harvey. Public Relations Review 44(5): 807-819.
    [33]
    Ma, X., W. Liu, X. Zhou, C. Qin, Y. Chen, Y. Xiang, X. Zhang, and M. Zhao. 2020. Evolution of online public opinion during meteorological disasters. Environmental Hazards 19(4): 375-397.
    [34]
    Madianou, M. 2015. Digital inequality and second-order disasters: Social media in the Typhoon Haiyan recovery. Social Media + Society 1(2): Article 2056305115603386.
    [35]
    Mihunov, V.V., N.H. Jafari, K. Wang, N.S. Lam, and D. Govender. 2022. Disaster impacts surveillance from social media with topic modeling and feature extraction: Case of Hurricane Harvey. International Journal of Disaster Risk Science 13(5): 729-742.
    [36]
    Ministry of Water Resources of the People’s Republic of China. 2020. China flood and drought disaster prevention bulletin. Beijing: China Water Resources and Hydropower Press.
    [37]
    Morstatter, F., J. Pfeffer, H. Liu, and K. Carley. 2013. Is the sample good enough? Comparing data from twitter’s streaming api with twitter’s firehose. In Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media, 8-11 July 2013, Cambridge, MA, USA.
    [38]
    Ogie, R.I., R.J. Clarke, H. Forehead, and P. Perez. 2019. Crowdsourced social media data for disaster management: Lessons from the PetaJakarta.org project. Computers Environment and Urban Systems 73: 108-117.
    [39]
    Ogie, R., A. Moore, R. Wickramasuriya, M. Amirghasemi, S. James, and T. Dilworth. 2022. Twitter data from the 2019-20 Australian bushfires reveals participatory and temporal variations in social media use for disaster recovery. Scientific Reports 12(1): Article 16914.
    [40]
    Olsson, E.K. 2014. Crisis communication in public organisations: Dimensions of crisis communication revisited. Journal of Contingencies and Crisis Management 22(2): 113-125.
    [41]
    Palen, L., J. Anderson, M. Bica, and C. Castillo. 2020. Crisis informatics: Human-centered research on tech & crises. https://docs.google.com/document/d/1g6I8Br3vTC04iXVHFTee6arKVld89x9bGvSEeN_NaPU/edit#. Accessed 27 May 2024.
    [42]
    Ragini, J.R., P.M.R. Anand, and V. Bhaskar. 2018. Big data analytics for disaster response and recovery through sentiment analysis. International Journal of Information Management 42: 13-24.
    [43]
    Rajput, A.A., Q. Li, C. Zhang, and A. Mostafavi. 2020. Temporal network analysis of inter-organizational communications on social media during disasters: A study of Hurricane Harvey in Houston. International Journal of Disaster Risk Reduction 46: Article 101622.
    [44]
    Robertson, C., and R. Feick. 2016. Bumps and bruises in the digital skins of cities: Unevenly distributed user-generated content across US urban areas. Cartography and Geographic Information Science 43(4): 283-300.
    [45]
    Roitman, J., S. AngeliAguiton, L. Cornilleau, and L. Cabane. 2020. Anti-crisis: Thinking with and against crisis excerpt from interview with Janet Roitman. Journal of Cultural Economy 13(6): 772-778.
    [46]
    Samaddar, S., M. Murase, and N. Okada. 2014. Information for disaster preparedness: A social network approach to rainwater harvesting technology dissemination. International Journal of Disaster Risk Science 5(1): 95-109.
    [47]
    Samuels, R., and J.E. Taylor. 2019. Applied methodology for identifying hurricane-induced social media signal changes in vulnerable populations. In Computing in civil engineering 2019: Smart cities, sustainability, and resilience, ed. Y.K. Cho, F. Leite, A. Behzadan, and C. Wang, 523-530. Reston: American Society of Civil Engineers.
    [48]
    Shao, C., X. Guan, J. Sun, M. Cole, and G. Liu. 2022. Social media interactions between government and the public: A Chinese case study of government WeChat official accounts on information related to COVID-19. Frontiers in Psychology 13: Article 5046.
    [49]
    Spence, A., W. Poortinga, C. Butler, and N.F. Pidgeon. 2011. Perceptions of climate change and willingness to save energy related to flood experience. Nature Climate Change 1(1): 46-49.
    [50]
    Sun, L., and A. Faas. 2018. Social production of disasters and disaster social constructs: An exercise in disambiguation and reframing. Disaster Prevention and Management: An International Journal 27(5): 623-635.
    [51]
    Takahashi, B., E.C. Tandoc Jr., and C. Carmichael. 2015. Communicating on Twitter during a disaster: An analysis of tweets during Typhoon Haiyan in the Philippines. Computers in Human Behavior 50: 392-398.
    [52]
    Tang, J., S. Yang, Y. Liu, K. Yao, and G. Wang. 2022. Typhoon risk perception: A case study of Typhoon Lekima in China. International Journal of Disaster Risk Science 13(2): 261-274.
    [53]
    Tierney, K. 2020. The social roots of risk: Producing disasters, promoting resilience. Redwood City: Stanford University Press.
    [54]
    Vieweg, S.E. 2012. Situational awareness in mass emergency: A behavioral and linguistic analysis of microblogged communications. Boulder: University of Colorado Boulder.
    [55]
    White, J.D., and K.-W. Fu. 2012. Who do you trust? Comparing people-centered communications in disaster situations in the United States and China. Journal of Comparative Policy Analysis: Research and Practice 14(2): 126-142.
    [56]
    Willson, G., V. Wilk, R. Sibson, and A. Morgan. 2021. Twitter content analysis of the Australian bushfires disaster 2019-2020: Futures implications. Journal of Tourism Futures 7(3): 350-355.
    [57]
    Wu, G., and C. Pan. 2022. Audience engagement with news on Chinese social media: A discourse analysis of the People’s Daily official account on WeChat. Discourse & Communication 16(1): 129-145.
    [58]
    Xiao, Y., Q. Huang, and K. Wu. 2015. Understanding social media data for disaster management. Natural Hazards 79: 1663-1679.
    [59]
    Yudarwati, G.A., I.A. Putranto, and K.M. Delmo. 2022. Examining the Indonesian government’s social media use for disaster risk communication. Asian Journal of Communication 32(1): 1-20.
    [60]
    Zeemering, E.S. 2021. Functional fragmentation in city hall and Twitter communication during the COVID-19 Pandemic: Evidence from Atlanta, San Francisco, and Washington, DC. Government Information Quarterly 38(1): Article 101539.
    [61]
    Zhong, B., Y. Huang, and Q. Liu. 2021. Mental health toll from the coronavirus: Social media usage reveals Wuhan residents’ depression and secondary trauma in the COVID-19 outbreak. Computers in Human Behavior 114: Article 106524.
    [62]
    Zou, L., N.S. Lam, S. Shams, H. Cai, M.A. Meyer, S. Yang, K. Lee, S.-J. Park, and M.A. Reams. 2019. Social and geographical disparities in Twitter use during Hurricane Harvey. International Journal of Digital Earth 12(11): 1300-1318.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (10) PDF downloads(0) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return