The Saudi Ministries Twitter communication strategies during the COVID-19 pandemic: A qualitative content analysis study

• Published in: Public health in practice (Oxford, England) Vol. 3; p. 100257
• Main Authors: Aldekhyyel, Raniah N., Binkheder, Samar, Aldekhyyel, Shahad N., Alhumaid, Nuha, Hassounah, Marwah, AlMogbel, Alanoud, Jamal, Amr A.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01-06-2022; Elsevier
• Subjects: COVID-19; Government communication; Original Research; Public health; Saudi Arabia; Twitter; COVID-19; Saudi Arabia; Twitter; Government communication; Public health
• ISSN: 2666-5352; 2666-5352
• DOI: 10.1016/j.puhip.2022.100257

To understand government communication strategies during the COVID-19 pandemic by examining topics related to COVID-19 posted by Saudi governmental ministries on Twitter and situating our findings within existing health behavior theoretical frameworks.
Retrospective content analysis of COVID-19 related tweets.
On November 7th, 2020, we extracted relevant tweets posted by five Saudi governmental ministries. After we extracted the data, we developed and applied a coding schema.
A total of 3,950 tweets were included in our dataset. Topics fell into two groups: disease-related (49.2%) and non-disease related (50.8%). The disease-related group included seven categories: awareness (18.5%), symptom (0.6%), prevention (7.7%), disease transmission (1.9%), treatment (0.3%), testing (3.4%), and reports (16.7%). The non-disease related group included eight categories: lockdown (5.9%), online learning (12.8%), digital platforms (4.3%), empowerment (12.0%), accountability (1.1%), non-disease reports (2.1%), local and international news (10.8%), and general statements (1.9%). Based on the correlation analysis, we found that the top positively correlated categories were: “testing” and “digital platforms” (r = 0.4157), “awareness” and “prevention” (r = 0.3088), “prevention” and “disease transmission” (r = 0.3025), “awareness” and “disease transmission” (r = 0.1685), “symptom” and “testing” (r = 0.1081), “awareness” and “symptom” (r = 0.0812), “symptom” and “digital platforms” (r = 0.0645), and “disease transmission” and “digital platforms” (r = 0.0450), p-values < 0.01. Several health behavior theoretical constructs were linked to our findings.
Integrating behavioral theories in the development of health risk communication should be taken seriously by government communication specialists who manage social media accounts, as these theories help underlining determinants of people's behaviors.