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Flores, Alejandro Quiroz, Liza, Farhana 
ORCID: https://orcid.org/0000-0003-4854-5619, Quteineh, Husam and Czarnecka, Barbara
(2021)
Variation in the timing of Covid-19 communication across universities in the UK.
PLoS One, 16 (2).
ISSN 1932-6203
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Abstract
During the Covid-19 pandemic, universities in the UK used social media to raise awareness and provide guidance and advice about the disease to students and staff. We explain why some universities used social media to communicate with stakeholders sooner than others. To do so, we identified the date of the first Covid-19 related tweet posted by each university in the country and used survival models to estimate the effect of university-specific characteristics on the timing of these messages. In order to confirm our results, we supplemented our analysis with a study of the introduction of coronavirus-related university webpages. We find that universities with large numbers of students are more likely to use social media and the web to speak about the pandemic sooner than institutions with fewer students. Universities with large financial resources are also more likely to tweet sooner, but they do not introduce Covid-19 webpages faster than other universities. We also find evidence of a strong process of emulation, whereby universities are more likely to post a coronavirus-related tweet or webpage if other universities have already done so.
| Item Type: | Article |
|---|---|
| Additional Information: | Data Availability: All Stata data and do-files are available at the Harvard Dataverse with DOI: 10.7910/DVN/2KR6YF. Funding Information: AQF, FL, and HQ, would like to acknowledge the support of the Business and Local Government Data Research Centre (ES/ S007156/1) funded by the Economic and Social Research Council (ESRC) for undertaking this work. https://esrc.ukri.org The funding body did not play a role in study design, data collection, analysis, decision to publish, or preparation of the manuscript. |
| Uncontrolled Keywords: | general ,/dk/atira/pure/subjectarea/asjc/1000 |
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Medicine and Health Sciences > Research Centres > Business and Local Government Data Research Centre (former - to 2023) Faculty of Science > Research Groups > Data Science and AI |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 26 Sep 2024 16:30 |
| Last Modified: | 10 Dec 2024 01:45 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/96824 |
| DOI: | 10.1371/journal.pone.0246391 |
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