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An empirical study on Twitter’s use and crisis retweeting dynamics amid Covid-19

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  • Bairong Wang

    (Shanghai Maritime University)

  • Bin Liu

    (Shanghai Maritime University)

  • Qi Zhang

    (Shanghai Maritime University)

Abstract

This study conducts an analysis on topics of the most diffused tweets and retweeting dynamics of crisis information amid Covid-19 to provide insights into how Twitter is used by the public and how crisis information is diffused on Twitter amid this pandemic. Results show that Twitter is first and foremost used as a news seeking and sharing platform with more than $$70\%$$ 70 % of the most diffused tweets being related to news and comments on crisis updates. As for the retweeting dynamics, our results show an almost immediate response from Twitter users, with some first retweets occurring as quickly as within 2 s and the vast majority $$(90\%)$$ ( 90 % ) of them done within 10 min. Nearly $$86\%$$ 86 % of the retweeting processes could have $$75\%$$ 75 % of their retweets finished within 24 h, indicating a 1-day information value of tweets. Distribution of retweeting behaviors could be modeled by Power law, Weibull, and Log normal in this study, but still there are $$20\%$$ 20 % original tweets whose retweeting distributions left unexplained. Results of retweeting community analysis show that following retweeters contribute to nearly $$50\%$$ 50 % of the retweets. In addition, the retweeting contribution of verified Twitter users is significantly $$(P

Suggested Citation

  • Bairong Wang & Bin Liu & Qi Zhang, 2021. "An empirical study on Twitter’s use and crisis retweeting dynamics amid Covid-19," 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 2319-2336, July.
    • Handle: RePEc:spr:nathaz:v:107:y:2021:i:3:d:10.1007_s11069-020-04497-5
    DOI: 10.1007/s11069-020-04497-5
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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. Cynthia Chew & Gunther Eysenbach, 2010. "Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak," PLOS ONE, Public Library of Science, vol. 5(11), pages 1-13, November.
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    Cited by:

    1. Ammar Redza Ahmad Rizal & Shahrina Md Nordin & Wan Fatimah Wan Ahmad & Muhammad Jazlan Ahmad Khiri & Siti Haslina Hussin, 2022. "How Does Social Media Influence People to Get Vaccinated? The Elaboration Likelihood Model of a Person’s Attitude and Intention to Get COVID-19 Vaccines," IJERPH, MDPI, vol. 19(4), pages 1-20, February.

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