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Rumor response, debunking response, and decision makings of misinformed Twitter users during disasters

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  • Bairong Wang

    (University at Buffalo)

  • Jun Zhuang

    (University at Buffalo)

Abstract

The rapid spread of rumors occurring on social media is a critical problem that poses a great risk to emergency situation navigation, especially during disasters. Many research questions, such as how misinformed users judge potential rumors or how they respond to them, are crucial issues for crisis communication, but have not been extensively studied. This paper fills this gap by originally documenting and studying Twitter users’ rumor and debunking response behaviors during disasters, such as Hurricane Sandy in 2012 and the Boston Marathon bombings in 2013. To this end, two rumors from each disaster and their related tweets are documented for analysis. Users who were misinformed and involved in the rumor topic by posting tweet(s), could respond to a rumor by: (1) spreading (85.86–91.40%), (2) confirmation-seeking (5.39–9.37%), or (3) doubting (0.71–8.75%). However, if the rumor-spreading users were debunked, they would respond by: (1) deleting rumor tweet(s) (2.94–10.00%), (2) clarifying rumor information with a new tweet (0–19.75%), or (3) neither deleting nor clarifying (78.13–97.06%). We conclude that Twitter users perform poorly in rumor detection and rush to spread rumors. The majority of users who spread rumors do not take further action on their Twitter accounts to fix their rumor-spreading behaviors.

Suggested Citation

  • Bairong Wang & Jun Zhuang, 2018. "Rumor response, debunking response, and decision makings of misinformed Twitter users during disasters," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 93(3), pages 1145-1162, September.
    • Handle: RePEc:spr:nathaz:v:93:y:2018:i:3:d:10.1007_s11069-018-3344-6
    DOI: 10.1007/s11069-018-3344-6
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    References listed on IDEAS

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    1. Bairong Wang & Jun Zhuang, 2017. "Crisis information distribution on Twitter: a content analysis of tweets during Hurricane Sandy," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 89(1), pages 161-181, October.
    2. Onook Oh & Manish Agrawal & H. Raghav Rao, 2011. "Information control and terrorism: Tracking the Mumbai terrorist attack through twitter," Information Systems Frontiers, Springer, vol. 13(1), pages 33-43, March.
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    Cited by:

    1. Zhang, Yi & Xu, Jiuping & Nekovee, Maziar & Li, Zongmin, 2022. "The impact of official rumor-refutation information on the dynamics of rumor spread," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    2. Agarwal, Puneet & Aziz, Ridwan Al & Zhuang, Jun, 2022. "Interplay of rumor propagation and clarification on social media during crisis events - A game-theoretic approach," European Journal of Operational Research, Elsevier, vol. 298(2), pages 714-733.
    3. Zhijie Sasha Dong & Lingyu Meng & Lauren Christenson & Lawrence Fulton, 2021. "Social media information sharing for natural disaster response," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(3), pages 2077-2104, July.
    4. Wu, Chunying & Xiong, Xiong & Gao, Ya & Zhang, Jin, 2022. "Does social media distort price discovery? Evidence from rumor clarifications," Research in International Business and Finance, Elsevier, vol. 62(C).
    5. Sabbaghtorkan, Monir & Batta, Rajan & He, Qing, 2020. "Prepositioning of assets and supplies in disaster operations management: Review and research gap identification," European Journal of Operational Research, Elsevier, vol. 284(1), pages 1-19.
    6. Shalini Upadhyay & Nitin Upadhyay, 2023. "Mapping crisis communication in the communication research: what we know and what we don’t know," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-19, December.
    7. Tiezhong Liu & Huyuan Zhang & Hubo Zhang, 2020. "The Impact of Social Media on Risk Communication of Disasters—A Comparative Study Based on Sina Weibo Blogs Related to Tianjin Explosion and Typhoon Pigeon," IJERPH, MDPI, vol. 17(3), pages 1-17, January.
    8. Sungyoon Kim & Wanyun Shao & Jonghun Kam, 2019. "Spatiotemporal patterns of US drought awareness," Palgrave Communications, Palgrave Macmillan, vol. 5(1), pages 1-9, December.
    9. Zhang, Xi & Cheng, Yihang & Chen, Aoshuang & Lytras, Miltiadis & de Pablos, Patricia Ordóñez & Zhang, Renyu, 2022. "How rumors diffuse in the infodemic: Evidence from the healthy online social change in China," Technological Forecasting and Social Change, Elsevier, vol. 185(C).
    10. Gabrielle Turner-McGrievy & Amir Karami & Courtney Monroe & Heather M. Brandt, 2020. "Dietary pattern recognition on Twitter: a case example of before, during, and after four natural disasters," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 103(1), pages 1035-1049, August.
    11. Mingyun Gu & Haixiang Guo & Jun Zhuang & Yufei Du & Lijin Qian, 2022. "Social Media User Behavior and Emotions during Crisis Events," IJERPH, MDPI, vol. 19(9), pages 1-21, April.
    12. Kyle Hunt & Bairong Wang & Jun Zhuang, 2020. "Misinformation debunking and cross-platform information sharing through Twitter during Hurricanes Harvey and Irma: a case study on shelters and ID checks," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 103(1), pages 861-883, August.
    13. Cui, Yapeng & Ni, Shunjiang & Shen, Shifei & Wang, Zhiru, 2020. "Modeling the dynamics of information dissemination under disaster," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    14. Kathrin Eismann, 2021. "Diffusion and persistence of false rumors in social media networks: implications of searchability on rumor self-correction on Twitter," Journal of Business Economics, Springer, vol. 91(9), pages 1299-1329, November.

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