key: cord-0983803-s8gzmcqt authors: Aldekhyyel, Raniah N.; Binkheder, Samar; Aldekhyyel, Shahad N.; Alhumaid, Nuha; Hassounah, Marwah; AlMogbel, Alanoud; Jamal, Amr A. title: The Saudi Ministries Twitter communication strategies during the COVID-19 pandemic: A qualitative content analysis study date: 2022-04-18 journal: Public Health Pract (Oxf) DOI: 10.1016/j.puhip.2022.100257 sha: 77b681b03f800f3aca835392a73ed3bd44fd08c7 doc_id: 983803 cord_uid: s8gzmcqt 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. Worldwide, government communication with the public plays a vital role in responding to pandemics; it directly affects health and social outcomes of populations. Social media has been utilized as a key channel of communication in several countries across the globe. Social media platforms have become very popular among government entities due to their ease of use as a communication channel, ease of accessibility, and real-time updates. [1] [2] [3] While governments' use of their social media accounts, specifically Twitter, to communicate with the public has thrived massively during the COVID-19 pandemic, [4] [5] [6] its use during pandemics and public health emergency of international concern has not been new. [7] [8] [9] The Kingdom of Saudi Arabia (KSA) is an example where the government is heavily investing in social media platforms, namely Twitter, as one of the main channels of communication, due to its popularity among the public. [10] The healthcare digital transformation in KSA [11] supported by the technological infrastructure in the country has created a massive opportunity for the Saudi government to utilize different digital platforms in responding and managing the COVID-19 pandemic. [12] In 2013, the Saudi Ministry of Communications and Information Technology reported that "41% of the online population in Saudi Arabia uses Twitter, a higher percentage than anywhere else in the world". [13] The popularity of using Twitter among the Saudi population, was one of the drives for the Saudi government to utilize Twitter in sending different communication messages during the COVID-19 pandemic. As a result, many research studies were published to examine the use of Twitter in Saudi Arabia during the COVID-19 pandemic. These studies can be divided to those that focus on the analysis of the public use of Twitter, [14] and those that reflect the use of Twitter by governments. [15] Different analysis methods were also published, which focused on analyzing tweets in the Arabic language during the COVID-19 pandemic, including content analysis approaches using manual or machine learning methods. [16] [17] [18] While previous research has described the role of governments in utilizing Twitter to communicate with the public regarding COVID-19, these studies mainly focused on the specific role of the Saudi Ministry of Health (MOH) in responding to the pandemic. To our knowledge this is the first study that examines the use of Twitter by several Saudi governmental bodies that J o u r n a l P r e -p r o o f represent the government cabinet during the COVID-19 pandemic. There are 24 ministries, representing the governmental bodies in KSA. [19] Among the 24 ministries, there were four that scored the top accounts on Twitter, based on the number of followers during the time of our study. [20] Our objective is to examine topics related to COVID-19 posted by Saudi governmental ministries on Twitter, by conducting a qualitative content analysis through the development and application of a topic classification schema. We also aim to understand mechanisms by which the use of Twitter for governmental communication was expected to guide appropriate coordinated actions by utilizing existing health behavior theoretical frameworks. Our study involved a retrospective content analysis of tweets posted by official Saudi ministries on Twitter during the COVID-19 pandemic. We identified the ministries' names and governmental twitter profiles from the Saudi Governmental website [19] and Twitter. [21] We only included the top four ministries based on the number of followers during the time of our study: Ministry of Health, Ministry of Interior, Ministry of Education, and the Ministry of Foreign Affairs. We also included the Ministry of Media in our study due to its unique role in regulating the media and the communications between Saudi Arabia and other countries. We conducted this study in three phases: (1) data extraction, preparation, and transformation, (2) coding schema development and application, and (3) content analysis ( Figure 1 ). J o u r n a l P r e -p r o o f Using the Twitter search API embedded in NodeXL (Social Media Research Foundation), [22] we extracted tweets on November 7, 2020, posted by the five government ministries. With Twitter limitations, which limits the number of tweets per user to a maximum of about 3,200 and a dataset to a maximum of 18,000 tweets per hour, our extracted dataset included a total of 16,209 tweets. To prepare the data for analysis, we removed duplicate tweets (n=838), based on the "unified twitter ID" (a unique ID generated from Twitter associated with every single tweet) resulting in a total of 15,371 tweets. The number of tweets extracted, and the total number of duplicates found and removed representing each ministry included are described in appendix A, We combined the extracted tweets from the five ministries into one dataset, then two researchers (SA and AA) manually reviewed the entire dataset to remove "unrelated" tweets (n=11,421). The exclusion of "unrelated" tweets was determined based on our research objective and scope. Non-Arabic tweets, tweets that only included multimedia (video, image) with no text, or tweets that were not related to COVID-19 were considered "unrelated" and thus removed from our dataset (Appendix A, Table A .2). Following the removal of "unrelated" J o u r n a l P r e -p r o o f tweets, our final research dataset was comprised of 3,950 tweets. Two researchers (RA and SB) independently observed the tweets to examine the type of topics. We then created a random sample, using the RAND function in Excel of 500 tweets to use in our second phase. Our schema was iteratively developed using the entire dataset to determine the main topics representing the messages posted by government accounts to the public during the COVID-19 pandemic. Developing the schema was based on an approach used in other studies to analyze clinical notes within the electronic health record, [31] [32] [33] and microblogs. [9, 10, 34, 35] All researchers and annotators are native Arabic speakers with insider knowledge of cultural language nuances and accent. Our coding schema consisted of two phases. The first phase focused on developing the annotation guidelines and an initial coding schema. This phase was based on the analysis of 50 randomly selected tweets and enhancing the schema through weekly meetings and discussions involving three subject matter experts, from the research team (RA, SB and NA).The initial schema consisted of two groups, which were disease related and non-disease related. Seven categories were identified under the disease-related group and five categories under the nondisease-related group. The second phase involved calculating inter-rater agreement using the Cohen's kappa statistic to ensure consistency in categorizing tweets between two reviewers (RA and SA), and applying the initial version of the schema for 450 randomly selected tweets. When the kappa statistic scored under 0.80, we discussed differences, enhanced the schema, and updated the annotation guidelines. The final version of the schema included two groups; (1) disease-related and (2) non-disease related with a total of 15 categories (Table 1) . Messages focused on sending general awareness information about COVID-19 and correcting false information about the virus. Announcements for upcoming daily media awareness coverage. Reports of symptoms such as fever, cough, diarrhea, and shortness of breath or answers related to these symptoms. Messages related to describing specific preventive measures or the mention of new prevention strategies, including a vaccine. Messages describing how the disease is transmitted, and how to prevent disease transmission after infection, including quarantine measures. Messages regarding treatment of the disease, which include describing clinical trials for treatment. Messages describing testing procedures, follow-up after testing, locations on where to get tested. Reports of daily/weekly/monthly cases, including no reported cases, total cases, recovered and death. Messages and announcements of lockdown directives, lockdown locations, or duration of lockdown or suspension of lockdown, including school, travel, prayer, Hajj and Omrah, formal gatherings, shopping malls, and sports lockdowns. Messages and announcements related to the shift to online learning including procedures, digital platforms, and training students and teachers. Messages and announcements of mobile applications/digital platforms (other than educational platforms) used during the pandemic for testing, locating clinics, permission to leave home during lockdown, and court platform. Messages of public encouragement and gratitude focused on motivating people (ill or healthy) to continue to fight the pandemic and take preventative measures, in the form of a direct message or sharing of a personal story with the aim to encourage the public to gain mastery over their lives and the community. Reports on penalties imposed on regulations violators set by the government, such as social distancing, wearing masks, and curfews. Media reports of general statistics including online learning, support during the pandemic, travel, and general media coverage Update coverage during the pandemic, including coverage of Hajj, initiatives taken by different organizations to support government actions, coverage of the G20 Virtual Summit. Messages that present a general information statement. The same two annotators (RA and SA) jointly annotated the remaining 3,300 tweets. The annotators assigned one or more categories from the 15 categories according to the contextual information for each tweet. Table 2 demonstrates examples of Arabic tweets and our translation of the tweets into the English language, assigned to their respective groups and categories. The Kingdom has taken an approach of expanded coronavirus screening, which is early examination that aims to reach the community and treat them before they reach hospitals in cases of illness and disease J o u r n a l P r e -p r o o f #ToReturnSafely, one must adhere to wearing a mask when leaving the house, leaving a safe distance between them and others, and whoever deliberately violates the precautionary and preventive measures will be fined #WHO: The Kingdom's decision regarding Hajj is an example of the difficult measures that countries must take to make health a priority J o u r n a l P r e -p r o o f After we completed annotating the entire dataset, counts and percentages were calculated representing each category's frequency within the dataset. Percentages describe each category's percentage from the total number of category occurrences among each group and 15 categories. We conducted a correlation analysis "Pearson" using R statistical language to examine the relationship between categories, excluding the category "general statements" due to the diversity and nature of these tweets. We performed further analysis utilizing a theoretical approach by examining tweets found under the "empowerment" category, as it pertains to public health. We specifically selected the "empowerment" category, as empowerment communication statements during pandemics are an essential element in risk communication, based on the social constructionist risk model approach, "whereby risk is seen to be interrelated with sociocultural context". [23] Due to the type of tweets within the "empowerment" category, rather than utilize one specific health behavior theory we used the following health behavior theories: the Health Belief Model, the Theory of Planned Behavior, and the Social Cognitive Theory. [24] [25] [26] [27] Non-disease related topics were slightly more than disease-related topics. "Awareness" was the highest disease-related category found in our dataset (n=842, 37.7%), followed by "reports" (n=761, 34%). Among the non-disease related topics, tweets that focused on "online learning" was the highest category (n=580, 25.1%), followed by the "empowerment" category (n=544, 23.6%) ( Table 3 ). The total number of likes and retweets within each category are illustrated in Figure 2 . The highest total likes were Tweets under the "disease related reports" category, while the highest total retweets were under the "empowerment" category. J o u r n a l P r e -p r o o f Multiple categories may be assigned to a single tweet. Most of the tweets (n=3,428) were assigned a single category from our coding schema. A total of (n=521) tweets were assigned two or more categories (Table A. 3). Based on the correlation analysis between categories, we found that the top positively correlated categories with p-values < 0.01 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) (Figure 3 ). Theoretical based analysis showed tweets under the "empowerment" category were found to mainly be linked to the following theoretical constructs: perceived benefits, perceived severity, perceived behavioral control, observational learning, incentive motivation, selfefficacy, collective efficacy, and facilitation. [24] [25] [26] [27] It is to be noted that in some tweets, more than one construct was linked to the same Tweet (Table 4 ). To those infected with Coronavirus or those in contact with them, and to everyone who is in quarantine: we are with you at every moment, we follow up with your health, determine the location of your quarantine, communicate with you and send you alerts, through the application: Tatamman (Be assured) *some examples are associated with multiple categories. Our work examined the use of Twitter by Saudi Arabian ministries, as one of the public communication methods utilized during the COVID-19 pandemic. Through our analysis of these collectively instructed messages that covered a wide range of disease and non-disease-related topics, we found "awareness" and "reports" were the most common topics found in the diseaserelated group, followed by "online-learning" and "empowerment" found in the non-diseaserelated group, with much less information around "symptom" and "treatment" in our dataset. The prevalence of communication messages related to disease awareness, disease reports, and empowerment appeared to be an effort by these government ministries to indicate the relative amount of potential support for the public in the fight against this pandemic. The low prevalence of messages around disease treatment could be due to our study's timing, which during that time no COVID-19 treatment was announced on a global level. While our work focused on analyzing the contents of governmental messages on Twitter as they relate to disease or non-disease related topics, previous notable work focused on either disease or non-disease-related topics. [8, 28] Other researchers specifically explored the type of symptoms experienced by patients with COVID-19 by analyzing public conversations posted on Twitter. [29, 30] Our findings have shown the interconnections between these two topic groups, which is similar to the findings of a recent study that examined public concerns during the COVID-19 pandemic in Saudi. [15] With digital platforms being extensively used during the J o u r n a l P r e -p r o o f COVID-19 pandemic, it was no surprise to see some tweets include information about digital platforms and disease testing information. During the early stages of the spread of the disease, specific efforts by the MOH were focused on ensuring the continuity of care and protecting the public, by developing several digital applications, and widely using Twitter as one method to inform the public about their use. [12] As major community closures took place and formal education shifted to online platforms, tweets on online learning and news increased, reflecting the rapidly changing scene at the time. The government tweeted "empowerment" content to address the psychological distress Our theory-based analysis was further employed to evaluate the communication of multiple government entities in Twitter, through the lenses of health behavior theoretic frameworks. Communication is essential in connecting the public with policy decision makers for collaboration and cooperative actions, which therefore enhances the effectiveness of pandemic preparation, management, and recovery. [32] Our findings indicate that a substantial amount of Tweets, especially in the "empowerment" category, were linked to constructs related to key health behavior theories such as the Health Belief Model, the Social Cognitive Theory, and the Theory of Planned Behavior. Such findings could indicate efforts and techniques that were used to create a persuasive language, even if following such specific frameworks was not intentional. Our findings demonstrate that Twitter can be a powerful communication platform to send messages to the public during a health crisis. Such messages are of utmost importance to guide the public during different stages of a pandemic. [16, 33] While social media platforms, especially Twitter, play an essential role in sending public health messages during pandemics, such as Third, we did not analyze reach and impact, which could have measured the type of messages that drew a public reaction. Fourth, we limited our analysis to tweets that contained text only, which resulted in excluding multimedia tweets from our analysis, even though Saudi ministries heavily used them. Analyzing multimedia tweets could have potentially changed the prevalence of categories in our dataset. 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