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Factors influencing retweeting of local news media tweets during Hurricane Irma

Author

Listed:
  • Cole Vaughn

    (Mississippi State University)

  • Kathleen Sherman-Morris

    (Mississippi State University)

  • Philip Poe

    (Mississippi State University)

Abstract

How individuals trust or relate to local broadcast news personalities can help explain how they respond to hazardous weather events. Local media often share weather information on twitter, but research on the way audiences interact or engage with local media via twitter during hurricanes is limited. No research has compared engagement with local weathercasts on Twitter both before and during a hurricane, although engagement before an event may have an influence on engagement during an event. This article examines the evolution and interrelation of the content, timing, and engagement of tweets from local news media Twitter accounts downloaded from the Social Media Tracking and Analysis System at Mississippi State University during the approach, landfall, and dissipation of Hurricane Irma. Retweets were used as the engagement metric in this study. Tweets related to the hurricane showed an overall higher retweet rate, though this result was not universal. The tweets with the greatest retweet rate occurred once the studied location was within the cone of uncertainty but before impacts from Irma arrived. The news accounts run by news stations in larger markets were the main drivers of retweet engagement and tweet frequency fluctuation. The personal Twitter accounts of broadcast meteorologists at local news stations were analyzed for tweets about their personal life a few months prior to Irma’s impact. The accounts with relatively small follower counts showed a weak positive correlation between posting personal content and increased retweet rates in Irma. Those with larger follower counts showed a weak negative correlation between the variables. Results may help local news media make decisions regarding which accounts are likely to reach the greatest number of individuals during a hurricane.

Suggested Citation

  • Cole Vaughn & Kathleen Sherman-Morris & Philip Poe, 2023. "Factors influencing retweeting of local news media tweets during Hurricane Irma," 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. 119(1), pages 583-611, October.
    • Handle: RePEc:spr:nathaz:v:119:y:2023:i:1:d:10.1007_s11069-023-06140-5
    DOI: 10.1007/s11069-023-06140-5
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    References listed on IDEAS

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    1. Kim, Jooho & Bae, Juhee & Hastak, Makarand, 2018. "Emergency information diffusion on online social media during storm Cindy in U.S," International Journal of Information Management, Elsevier, vol. 40(C), pages 153-165.
    2. Kathleen Sherman-Morris, 2010. "Tornado warning dissemination and response at a university campus," 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. 52(3), pages 623-638, March.
    3. Onook Oh & Chanyoung Eom & H. R. Rao, 2015. "Research Note —Role of Social Media in Social Change: An Analysis of Collective Sense Making During the 2011 Egypt Revolution," Information Systems Research, INFORMS, vol. 26(1), pages 210-223, March.
    4. 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.
    5. Bo-Chiuan Su & Li-Wei Wu & Yevvon-Yi-Chi Chang & Ruo-Hao Hong, 2021. "Influencers on Social Media as References: Understanding the Importance of Parasocial Relationships," Sustainability, MDPI, vol. 13(19), pages 1-19, September.
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