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Content features of tweets for effective communication during disasters: A media synchronicity theory perspective

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  • Son, Jaebong
  • Lee, Hyung Koo
  • Jin, Sung
  • Lee, Jintae

Abstract

Users’ ability to retweet information has made Twitter one of the most prominent social media platforms for disseminating emergency information during disasters. However, few studies have examined how Twitter’s features can support the different communication patterns that occur during different phases of disaster events. Based on the literature of disaster communication and Media Synchronicity Theory, we identify distinct disaster phases and the two communication types—crisis communication and risk communication—that occur during those phases. We investigate how Twitter’s representational features, including words, URLs, hashtags, and hashtag importance, influence the average retweet time—that is, the average time it takes for retweet to occur—as well as how such effects differ depending on the type of disaster communication. Our analysis of tweets from the 2013 Colorado floods found that adding more URLs to tweets increases the average retweet time more in risk-related tweets than it does in crisis-related tweets. Further, including key disaster-related hashtags in tweets contributed to faster retweets in crisis-related tweets than in risk-related tweets. Our findings suggest that the influence of Twitter’s media capabilities on rapid tweet propagation during disasters may differ based on the communication processes.

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  • Son, Jaebong & Lee, Hyung Koo & Jin, Sung & Lee, Jintae, 2019. "Content features of tweets for effective communication during disasters: A media synchronicity theory perspective," International Journal of Information Management, Elsevier, vol. 45(C), pages 56-68.
    • Handle: RePEc:eee:ininma:v:45:y:2019:i:c:p:56-68
    DOI: 10.1016/j.ijinfomgt.2018.10.012
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    3. Nicole Olynk Widmar & Kendra Rash & Courtney Bir & Benjamin Bir & Jinho Jung, 2022. "The anatomy of natural disasters on online media: hurricanes and wildfires," 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. 110(2), pages 961-998, January.
    4. Han Zheng & Dion Hoe‐Lian Goh & Edmund Wei Jian Lee & Chei Sian Lee & Yin‐Leng Theng, 2022. "Understanding the effects of message cues on COVID‐19 information sharing on Twitter," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(6), pages 847-862, June.
    5. Li, Xinwei & Xu, Mao & Zeng, Wenjuan & Tse, Ying Kei & Chan, Hing Kai, 2023. "Exploring customer concerns on service quality under the COVID-19 crisis: A social media analytics study from the retail industry," Journal of Retailing and Consumer Services, Elsevier, vol. 70(C).
    6. Abhinav Kumar & Jyoti Prakash Singh & Yogesh K. Dwivedi & Nripendra P. Rana, 2022. "A deep multi-modal neural network for informative Twitter content classification during emergencies," Annals of Operations Research, Springer, vol. 319(1), pages 791-822, December.
    7. Li, Siyu & Huo, Baofeng & Wang, Qiang, 2023. "The impact of buyer-supplier communication on performance: A contingency and configuration approach," International Journal of Production Economics, Elsevier, vol. 257(C).

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