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

Abstract

Objectives: 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.

Study design: Retrospective content analysis of COVID-19 related tweets.

Methods: 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.

Results: 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.

Conclusions: 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.

Keywords: COVID-19; Government communication; Public health; Saudi Arabia; Twitter.

Fig. 1

(A) data extraction, preparation, and transformation and (B) coding schema development and application.

Fig. 2

Total number of likes and retweets among each category.

Fig. 3

Correlation between categories. The correlation matrix shows the correlation between assigned categories. As indicating in the color legend, the positive correlations are shown in blue color, while the negative correlations are shown in red color. The larger size and more intense color of the circle the higher values of correlation coefficients. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)