A content analysis of government-issued social media posts during multi-jurisdictional enteric illness outbreaks in Canada

Introduction: Most Canadians use at least one social media platform regularly, making social media a potentially effective tool for reaching broad audiences. The Public Health Agency of Canada (PHAC) uses social media as one tool for rapidly communicating with the public during multi-jurisdictional enteric illness outbreaks. However, the effectiveness of social media in enhancing public risk communication during these outbreaks remains unexplored. Addressing this gap may help optimise social media use for risk communication to inform the public and prevent additional illness. This study aims to analyse the engagement with and quality of PHAC’s social media content regarding multi-jurisdictional enteric illness outbreaks.

Methods: Using a search of PHAC’s social media platforms, 482 posts during enteric illness outbreaks (2014–2022) were identified, including 198 posts from Facebook and 284 posts from X (formerly Twitter) in English and French. A codebook was developed using engagement metrics for gauging public interest, the Centers for Disease Control and Prevention’s (CDC) Modified Clear Communication Index (CCI) to assess clarity as a proxy for comprehension, the Health Belief Model (HBM) to evaluate the potential to motivate behaviour change, and measures of consistency. Descriptive statistics were used to analyse post content.

Results: The average engagement rates for PHAC social media accounts were < 1%, below standard average engagement rates (1–5%). While posts generally adhered to the CDC’s CCI criteria, clear language (45.7% on Facebook, 26.5% on X) and clear communication of risk (7.6% on Facebook, 0.0% on X) were scarce. HBM constructs were present in all posts, but certain constructs, such as barriers were used sparingly (1% on Facebook, 0% on X). Despite this, posts consistently communicated outbreak investigation details and prevention information.

Discussion: The low average engagement rates suggest a lack of public awareness or interest in the posts. The partial adherence to the CCI indicates room for improvement in clarity, a key component for supporting public understanding. Although some HBM constructs were utilised, no posts incorporated all HBM constructs, which may hinder efforts to promote behaviour change. To enhance effective risk communication using social media during multi-jurisdictional enteric illness outbreaks in Canada, tools like the CDC’s CCI should be used to improve message clarity, use of all HBM constructs as applicable, and message consistency across products and channels are recommended to improve overall message quality and content.

1 Introduction

In Canada, approximately one in eight Canadians are affected by enteric illnesses each year (Thomas et al., 2013). Enteric illnesses occur from the consumption of contaminated food or water, contact with animals, or person-to-person contact. These illnesses often result in self-limiting, gastrointestinal symptoms such as nausea, vomiting, and diarrhoea. While everyone is susceptible to enteric illnesses, young children, older adults, individuals with weakened immune systems, and people who are pregnant are at higher risk of severe illness, hospitalisation, and death (Barkley et al., 2016). Therefore, it is crucial that communication of enteric illness outbreaks is visible, understandable, and promotes preventive health behaviours to lessen the impact of the outbreak.

An enteric illness outbreak occurs when two or more persons become ill after exposure to a common source, such as a contaminated food item (Gould et al., 2013). In Canada, when an outbreak occurs in more than one province/territory, or between Canada and another country, the Public Health Agency of Canada (PHAC) manages the multi-jurisdictional outbreak response (Public Health Agency of Canada, 2017). PHAC communicates outbreak investigation details, pathogen information (if known), and health preventive strategies using several channels, including Public Health Notices (PHN) posted to the Government of Canada’s website, and corresponding social media posts on Facebook, X (formerly Twitter), and LinkedIn (Patel et al., 2024). As new information becomes available, PHNs are updated, and new social media posts are made to reflect the latest details of the outbreak. Communication during these outbreak events is intended to enhance awareness and increase uptake of preventive behaviours at the individual level, such as hand-washing, proper cooking methods, or disposal of contaminated food items.

Given the accessibility and real-time nature of social media (Delisle et al., 2021), government public health officials have leveraged these platforms to increase dissemination, reach, and engagement with health information (Kass-Hout and Alhinnawi, 2013; Moorhead et al., 2013). Engagement with health information on social media can enhance comprehension (Bennett and Glasgow, 2009) and promote behaviour change (Webb et al., 2010). Features such as audiovisual elements and hashtags can foster engagement, prompting user actions such as liking, commenting, or sharing (Jiang et al., 2016). These actions not only symbolise social endorsement (Kim, 2018), but also influence other users’ perceptions of the post and their subsequent behavioural intentions (Park and Jung, 2023), a phenomenon known as the bandwagon effect (Marsh, 1985). Additionally, social media has other advantages for risk communication, including the potential to increase public awareness using brief, tailored messaging (Hyland-Wood et al., 2021) and elicit emotional responses that can increase risk perception and uptake of preventative behaviours (Al-Dmour et al., 2020; Li and Liu, 2020; Mahmood et al., 2021; Oh et al., 2020).

To improve use of social media for effective risk communication, standardised tools and frameworks have been developed to enhance message comprehension and effectiveness. One such tool is the Centers for Disease Control and Prevention’s (CDC) Modified Clear Communication Index (CCI). The modified CCI, consisting of four introductory questions and 13 scored items, is a research-based tool that can be used to evaluate the characteristics of social media posts to enhance clarity and comprehension (Centers for Disease Control and Prevention, 2017). Previous studies have used the modified CCI to evaluate short-form and oral communication materials, such as social media posts, podcasts, and infographics (da Silva et al., 2021; Kelly et al., 2023; Nunes et al., 2023).

Effective health communication that influences behaviour change should be grounded in theory (Milgrom, 2015) as health communication materials grounded in theory are more effective than those without a theoretical framework (Glanz and Bishop, 2010). Studies have shown that health communication materials grounded in theory, such as the Health Belief Model (HBM), tend to be more effective at modifying behaviours than those not based on such frameworks (Brewer et al., 2007; Luquis and Kensinger, 2019; Vahedian-Shahroodi et al., 2019; Limbu and Gautam, 2023). The HBM has been widely adopted in health education and promotion programmes (Ngigi and Busolo, 2018). The HBM theorises that individual behaviour is dependent on beliefs about the threat to one’s well-being and the perceived effectiveness of the recommended behaviour according to six constructs: perceived susceptibility, perceived severity, perceived benefits, perceived barriers, cues to action, and self-efficacy (Becker, 1974; Rosenstock, 1974; Champion and Skinner, 2008). According to the HBM, optimal behaviour change occurs when all six constructs are present in health and risk messaging. Further, message consistency, as understood in this study, refers to the extent to which predetermined key message elements are consistently present across posts and is important in risk communication (Seeger, 2006). Consistent messages, in the context of behavioural recommendations, foster public understanding and improve behaviour adoption (Seeger, 2006).

While previous research has demonstrated the effectiveness of social media in risk communication, this approach remains understudied concerning food safety and food-related behaviours (Mou and Lin, 2014; Yang et al., 2015; Overbey et al., 2017). Despite growing concerns about enteric illness outbreaks among public health authorities (Hossain et al., 2015), the literature examining the role of social media in these outbreaks is scarce and limited in geographical scope (Overbey et al., 2017). Moreover, studies that investigate the use of social media in emerging infectious disease outbreaks, including foodborne illness outbreaks, often lack a theoretical framework (Tang et al., 2018), which has demonstrated effectiveness in developing health communication strategies. These gaps present a challenge for developing effective communication strategies to mitigate the spread of enteric illnesses. Understanding how social media can be leveraged in the context of enteric illness outbreaks could fill this knowledge gap and enhance public health response strategies during these outbreaks.

While PHAC routinely uses social media platforms, particularly Facebook and X, to communicate outbreak information and prevention strategies, a formal evaluation of their content to promote public awareness and comprehension has not been conducted. This research aims to evaluate the content of PHAC social media posts on Facebook and X issued during multi-jurisdictional enteric illness outbreaks to identify communication strengths and opportunities for improvement. The specific objectives are to assess (1) the public’s awareness of social media posts using engagement metrics, (2) the clarity of social media posts using the CCI, (3) the use of HBM constructs in social media posts using operationalized constructs, and (4) the consistency of social media posts through the inclusion and repetition of key information.

2 Materials and methods

2.1 Inclusion criteria

Social media posts published from May 27, 2014 (the date of the first PHN) to December 31, 2022 that explicitly mentioned multi-jurisdictional enteric illness outbreaks for which a PHN had been published were included. Posts were collected from official Canadian federal public health accounts on Facebook (Healthy Canadians, Canadiens en santé) and X (Health Canada and PHAC, Santé Canada et l’ASPC) in English and French.

2.2 Data collection

Social media posts were manually reviewed and collected by reviewing all account posts to identify those that met inclusion criteria. No specific keywords or search terms were used in the collection process. A unique identifier was created for each eligible social media post and the following metadata was recorded: link to the social media post, pathogen, publication date, and date of collection. Data was collected from May 10–18, 2023 by the first author and managed using Microsoft Excel (Microsoft Corporation, 2018).

2.3 Engagement analysis

An engagement rate was calculated for each social media post using published platform-specific formulas (Sehl and Mikolajczyk, 2023). Engagement metrics included number of reactions, comments, and shares on Facebook, and number of retweets and comments on X. For all calculations, the total number of followers at the time of data analysis was used (November 2023). The engagement rate per post (ER post) was calculated by adding the number of engagements divided by the number of followers. The overall average engagement rate per platform and language was subsequently calculated by dividing the total number of engagements by the number of social media posts. Descriptive time trends in engagement rates over the study period (2015–2022) were assessed by examining differences in annual engagement rates.

2.4 Content analysis

A deductive, iterative content analysis of all social media posts was conducted following established methodology (Hsieh and Shannon, 2005). French social media posts that were direct translations of English social media posts were excluded to avoid redundancy in the content analysis. A codebook was developed by VP to guide the analysis, and included engagement metrics, CCI criteria, HBM constructs, other health communication best practices identified in the literature, and measures of consistency (Supplementary Table A1).

The modified CCI for short form communication materials includes a total of 13 items in four parts (Supplementary Table A2). Part A applies to all communication materials and includes the main message and call to action and language. Parts B, C, and D selectively apply and include behavioural recommendations, numerical information, and risk, respectively. For Facebook, 11 of the 13 CCI items were used, as written. Two criteria were excluded because no Facebook posts required mathematical equations or used numeric probability to describe risk. For X, 7 of 13 items were used, as written, while six items were excluded due to the lack of mathematical equations and the non-applicability of certain criteria in the context of short messages.

Similar to other studies, HBM constructs were operationalized to identify explicit features of the text that aligned with these constructs (Meadows et al., 2019; MacKay et al., 2022b; Bajouk and Ferré-Pavia, 2023) (Supplementary Table A3). For example, perceived susceptibility was coded as present if the post mentioned the number of cases.

Other health communication best practices were also included, specifically the use of hashtags (Lin et al., 2016), mentions (Zappavigna and Martin, 2018), visuals (Chou et al., 2021), tone (Maal and Wilson-North, 2019), intended audience information (Korda and Itani, 2013), interaction (Lin et al., 2016), and combatting misinformation (Lin et al., 2016) (Supplementary Table A1).

Consistency was measured at a high level through the inclusion and repetition of key information from the PHN (e.g., source, pathogen, and geographical distribution) and prevention information (e.g., link to additional information, behavioural recommendations) (Supplementary Table A1).

Two researchers (VP, HS) independently coded a ~ 10% simple random sample of 50 Facebook and X posts using the codebook. Inter-rater reliability was calculated in R using the ‘irr’ package (Gamer et al., 2010; Posit team, 2023) and met the a priori cut-off of 0.8. A kappa value (κ) of 0.92 was achieved indicating almost perfect agreement (McHugh, 2012). Minor disagreements were resolved through discussion, leading to no major codebook revisions. The codebook was applied to the remaining 432 posts by one researcher (VP). All coding was done using Microsoft Excel (Microsoft Corporation, 2018).

2.5 Statistical analysis

Descriptive statistics, including mean, standard deviation, and minimum and maximum values, were computed for continuous variables. Frequencies and proportional frequencies were computed for categorical variables. Data were tabulated and visualised using RStudio version 2023. 6. 1. 524, using base packages (Posit team, 2023), dplyr (Wickham et al., 2023), ggplot2 (Wickham, 2016), and stringr (Wickham, 2023). Analyses were stratified by language, platform, and both language and platform.

2.6 Ethics

As per the University of Guelph’s Research Ethics Board, ethics approval was not required as this study used publicly available data.

3 Results

In total, 482 social media posts were identified: 198 posts from Facebook (97 in English, 101 in French) and 284 posts from X (153 in English, 131 in French). The engagement analysis included both English and French social media posts (n = 482). Subsequent content analyses used English data only (n = 250).

3.1 Engagement

English social media posts (n = 250) had an average engagement rate of 0.36 ± 1.32% (0.01–12.45%) on Facebook and 0.01 ± 0.02% (0.00–0.21%) on X (Table 1). French social media posts (n = 232) had similar engagement with an average engagement rate of 0.37 ± 0.89% (0.00–7.02%) on Facebook and 0.01 ± 0.01% (0.00–0.05%) on X (Table 1).

Table 1

Table 1. Descriptive statistics of engagement variables for Facebook (Healthy Canadians, Canadiens en santé) and X (Health Canada and PHAC, Santé Canada et l’ASPC) accounts in both official languages (English and French) for multi-jurisdictional enteric illness outbreak posts.

Facebook and X average engagement rates varied across time and by language (Figure 1). On Facebook, engagement rates peaked in 2016 for French-language posts (ER post = 2.37%) and 2021 for English-language posts (ER post = 1.21%). On X, engagement rates were highest in 2016 and 2020 for English-language posts. Contrastingly, French-language posts demonstrated a consistent engagement rate of ~0.01% over the study period.

Figure 1

Figure 1. Time trends in average engagement rates on multi-jurisdictional enteric illness outbreak posts from 2015 to 2022 on Facebook accounts in both official languages (Healthy Canadians, Canadiens en santé) (A) and from 2016 to 2022 on X accounts in both official languages (Health Canada and PHAC, Santé Canada et l’ASPC) (B).

3.2 CDC’s modified CCI

3.2.1 Main message and call to action and language

Facebook posts adhered to core communication principles, with 100% of posts featuring a call to action and 97.9% with a main message always displayed at the top of the post. Facebook posts less frequently communicated the main message and call to action in an active voice (35.1%) using only words the public uses (2.1%). Most X posts also featured a call to action (99.3%) and main message statement (87.6%). Just over half of X posts employed an active voice when communicating the main message and call to action (51.6%); however, posts rarely communicated using plain language (1.3%) (Table 2).

Table 2

Table 2. Analysis of the binary (yes/no) presence of CCI criteria for multi-jurisdictional enteric illness outbreak Facebook posts (n = 97) and X posts (n = 153) on English accounts.

3.2.2 Behavioural recommendations

Facebook and X posts consistently provided behavioural recommendations (99.0 and 96.7%, respectively). Further, Facebook posts frequently explained the importance of adopting the recommended behaviours (91.8%) (Table 2).

3.2.3 Numbers

If numbers were present in Facebook and X posts, they were always communicated clearly using whole numbers (100%) (Table 2).

3.2.4 Risk

Facebook and X posts less frequently explained the nature of the risk to the public (14.1 and 0.0%, respectively). Further, Facebook posts rarely addressed the risks and benefits of the recommended behaviours (1.0%) (Table 2).

3.3 HBM

Facebook posts frequently used cues to action (95.9%) and benefits constructs (91.8%). However, posts less often employed the severity (14.4%) and barrier constructs (1.0%). On average, Facebook posts most often incorporated three of the six HBM constructs (40.2%) per post. No Facebook posts employed all six constructs in a single post. Likewise, X posts also used cues to action (95.4%) and benefits constructs (87.6%) with infrequent use of severity (2.0%) and barriers constructs (0.0%). X posts typically included two HBM constructs (45.1%) per post, and like Facebook, no X posts contained all six constructs in one post (Table 3).

Table 3

Table 3. Analysis of the binary (yes/no) use of HBM constructs and integration of HBM for multi-jurisdictional enteric illness outbreak Facebook posts (n = 97) and X posts (n = 153) on English accounts.

3.4 Other health communication best practices

Facebook posts frequently incorporated visuals in 94.8% of posts, primarily using still photographs (43.5%). Hashtags were rarely used on Facebook (10.3%), but when they were, posts adhered to the best practice of including a maximum of two or less hashtags per post (100%). Mentions of other organisations were infrequent (2.1%). Posts containing messaging to combat misinformation were never used (0.0%) (Table 4).

Table 4

Table 4. Analysis of other health communication best practices and intended audiences used for multi-jurisdictional enteric illness outbreak Facebook posts (n = 97) on English account.

Likewise, X posts included visuals in 97.4% of posts, with still photographs most often used (53.0%). Hashtags were commonly used on X (70.6%), and like Facebook, a maximum of two or less hashtags per post was usually adhered to (94.4%). Mentions of other organisations were slightly more common on X (7.2%), aimed at increasing engagement with other governmental bodies like InspectionCan (54.5%), CFIA_Food (36.4%), and CDCgov (9.1%). X posts did not contain messaging to combat misinformation (0.0%) (Table 5).

Table 5

Table 5. Analysis of other health communication best practices and intended audiences used for multi-jurisdictional enteric illness outbreak X posts (n = 153) on English account.

3.5 Consistency

Posts on both platforms consistently provided details on outbreak investigations, including the source (99.0% on Facebook, 100% on X) and the pathogen (97.9% on Facebook, 100% on X). All Facebook posts and most X posts (98.7%) included a link to additional information for preventing illness. However, providing geographical distribution information was less consistent, with 52.6% of Facebook posts and 17.6% of X posts including this information (Table 6).

Table 6

Table 6. Analysis of the consistency of outbreak investigation and prevention information for multi-jurisdictional enteric illness outbreak Facebook posts (n = 97) and X posts (n = 153) on English accounts.

4 Discussion

Designing and disseminating effective social media posts can improve public awareness and comprehension to prevent and promote health. This study assessed public awareness of and the potential to comprehend social media posts issued by PHAC during multi-jurisdictional enteric illness outbreaks using a descriptive analysis of engagement metrics and a comprehensive content analysis. Although Facebook posts had higher overall engagement, neither Facebook nor X achieved the overall benchmark engagement rate across all industries (1–5%), indicating sub-optimal awareness. Social media posts met several CCI criteria, including clear communication of the call to action and main message, behavioural recommendations, and numerical information. However, using plain language and clarifying the nature of the risk may help to improve public comprehension. Similarly, social media posts used multiple HBM constructs. Integration of all six constructs could be improved to optimally promote healthy behaviours. Social media posts almost always demonstrated message consistency, supporting understanding and use of the provided information.

4.1 PHAC social media posts had below-average engagement

Engagement rates on social media platforms can be used to gauge public awareness of posts. Rates between 1 and 5% are indicative of good engagement (Sehl and Mikolajczyk, 2023). At the end of 2023, average engagement rates on Government of Canada accounts were 1.62% on Facebook and 1.36% on X (Mikolajczyk, 2023). However, our study revealed average engagement rates below 1% across all accounts and platforms for multi-jurisdictional enteric illness outbreaks with relatively higher engagement on Facebook. Facebook is more commonly adopted across all demographics (Krallman et al., 2016; Pew Research Center, 2021), as it traditionally serves as a platform for social interactions (Voorveld et al., 2018). In contrast, X is considered more news-oriented (Kwak et al., 2010) and tends to garner less engagement than Facebook (Reuter et al., 2021). This trend, where Facebook demonstrates higher engagement rates than X, aligns with our findings (Bhattacharya et al., 2017; Haro-de-Rosario et al., 2018; Reuter et al., 2021; DePaula et al., 2022).

Lack of engagement, as evidenced by rates below industry standards, may reflect an overall inattentive and unresponsive audience (Guidry et al., 2017), hindering the awareness and amplification of posts (Bajouk and Ferré-Pavia, 2023). Engagement metrics, such as share and like functions on social media posts, affect the perception of the post and other user’s behavioural intentions (Kim, 2018). These functions can serve as a cue, triggering the bandwagon heuristic which leads people to accept others’ beliefs, attitudes, or decisions, as their own (Sundar, 2008). Negative engagement can cue others to view the content in the same light. Therefore, the lower engagement rates observed on PHAC’s social media posts may cause others to view PHAC content as uninteresting or irrelevant. Additionally, lower engagement rates may impact how platform algorithms rank and display these posts in followers’ feeds (Meta, 2024; Twitter, 2023), potentially reducing visibility and further affecting engagement.

Social and mixed media features, almost always employed on both platforms, can increase engagement (Rus and Cameron, 2016; Moran et al., 2019; Li and Xie, 2020; Cao et al., 2021). The use of pictures in communication materials can enhance comprehension and adherence, especially among individuals with low literacy skills (Houts et al., 2006). Moreover, the use of a dialogue loop, initiated by hashtags and questions, can promote two-way communication (Men et al., 2018). These features can increase engagement by encouraging the public to voice their opinions through social media functions, such as sharing, liking, and commenting (Watkins, 2017). To complete the dialogue loop, organisations should take the opportunity to respond to questions, comments, and concerns (Kent and Taylor, 1998). Utilising dialogue loops enhances engagement and supports a public-centred approach, fostering trust between the public and the posting organisation (Kent and Taylor, 2002). In the future, social media posts should consider the use of social and mixed media features to improve engagement, comprehension, and adherence.

4.2 Social media posts could be improved by using plain language and clearly communicating the risks

An analysis of the posts, focusing on their adherence to the CCI, revealed that posts effectively communicated the main message, behavioural recommendations, and numerical information. However, the use of plain language and the communication of risk could be improved. A similar study examining government health messaging in Canada found inconsistent application of the CCI, possibly impacting the clarity and comprehension of communication materials (Kelly et al., 2023).

Recognising the importance of clear health communication language, often termed plain language, the Government of Canada introduced the Directive on the Management of Communications. This directive emphasises the need for plain language in all public communications, defining it as writing that is easily understandable upon first reading (Government of Canada, 2021). Our analysis revealed that posts contained few familiar words that the public could easily comprehend. Unclear language can hinder public understanding and ability to adopt health behaviours (Zarcadoolas, 2010). Communicating about enteric illnesses, which often requires complex scientific terminology, is further complicated by the character limits imposed by social media platforms. These constraints make it challenging to streamline the content effectively, which can further impede understanding (Zarcadoolas, 2010). While plain language is crucial for improving health information comprehension (Grene et al., 2017), it is also important to recognise that some scientific terminology may be unavoidable, for example, pathogen names. Therefore, it is recommended to minimise the use of complex terms as much as possible while preserving accuracy, rather than solely relying on plain language. Further, social media is a common source of health information for older adults (Dooley et al., 2012; Tennant et al., 2015; Zhao et al., 2022). However, older adults, who have lower health literacy on average (Rootman and Gordon-El-Bihbety, 2008), are of particular concern as they are also at higher risk for enteric illnesses (Barkley et al., 2016). Therefore, ensuring information shared on social media is written in plain language is important to help older adults both understand and act on the content.

Effectively crafting posts during outbreak responses entails curating content that resonates with affected populations, including clear identification and explanation of public health risks (Tumpey et al., 2019). While social media posts often identified the cause of harm, crucial information enabling individuals to assess the risk’s relevance, such as associated symptoms, was seldom provided. Without this information, affected populations may not understand the significance of the risk and how it relates to them, potentially hindering adherence to recommended health behaviours (Tumpey et al., 2019).

4.3 HBM constructs were used with variability, indicating an opportunity for enhanced integration to support behaviour change

According to the extended parallel processing model (EPPM), a communication theory, the susceptibility and severity constructs constitute a threat that needs to be elicited for individuals to respond to fear and engage in preventive behaviours (Witte, 1992). The posts inconsistently included the susceptibility construct and seldom the severity construct, hindering the ability of individuals to properly appraise the threat and take preventive actions. A prior meta-analysis of the HBM constructs’ effectiveness indicated that benefit and barrier constructs are more predictive of behaviour change compared to severity and susceptibility constructs (Carpenter, 2010). While benefit constructs were almost always present, barrier constructs were rarely present in posts. This is important given that barrier constructs are influential in motivating behaviour change.

Nonetheless, the presence of all constructs is important: perceived susceptibility, perceived severity, perceived barriers, and perceived benefits to maximally influence health behaviours (Sulat et al., 2018). As such, social media posts should aim to incorporate all relevant HBM constructs to optimise the potential to influence behaviour change. For instance, posts could include the number of cases (susceptibility) and, if applicable, hospitalisations and deaths (severity) to influence risk perception and support accurate threat appraisal. Additionally, when providing behavioural recommendations, posts should offer stepwise and detailed instructions to enhance self-efficacy, helping individuals take preventive actions to avoid or reduce the threat. Briefly outlining the benefits (prevention of illness) and overcoming barriers (ease of task) can enhance an individual’s confidence that completing the action will have a desirable effect and encourage individuals to adopt preventive actions to avoid or reduce the threat (Witte, 1992). Due to character limits imposed on social media, integrating the full HBM in a single post may not be feasible. Instead, multiple posts, such as a thread on X, can be used to convey information. Additionally, using visuals with text overlays or videos in combination with the captions can convey more information, while adhering to character limits.

4.4 Consistent posts can foster public trust, understanding, and use

During public health threats like enteric illness outbreaks, individuals intensify their reliance on trusted sources and perceived expertise for emotional release during information seeking (Ball-Rokeach, 1985). People seek information from the media and turn to organisations they trust (Holroyd et al., 2020). Public health organisations, such as PHAC, were historically viewed as trusted sources of health information (Lachlan et al., 2014). However, the COVID-19 pandemic has been associated with growing concerns about trust in the federal government (Herati et al., 2023). This growing scepticism may affect how people perceive and value information from PHAC, potentially impacting PHAC’s communication effectiveness during enteric illness outbreaks.

For government communication, consistent posts are crucial to establishing trust (MacKay et al., 2022a,b; Seeger et al., 2018; Wray et al., 2008). The consistent messaging demonstrated by PHAC, exemplified by including outbreak investigation details and prevention information in each post, helps individuals sift through information, distinguishing between authentic and misleading content (Wajeeha et al., 2020), and builds trust in the information provided (Vereen et al., 2020). Conversely, inconsistent messaging creates confusion and hampers the public’s ability to take preventive actions (Seeger, 2006). Trust in government organisations as key information providers is important for risk perception and subsequent adoption of preventive behaviours (Dohle et al., 2020; Lim et al., 2021; Jeong and Kim, 2023). Maintaining consistency in communication aids in building public trust and ensuring effective information dissemination during enteric illness outbreaks.

4.5 Limitations

Our study has several limitations. First, the study assumed that the follower count recorded in November 2023 was constant over the study period when calculating engagement rate metrics. This assumption may potentially impact the accuracy of engagement rate calculations. If followership increased over time, the engagement rate in earlier time periods would have been underestimated, potentially leading to an inaccurate assessment of engagement trends. Second, the application of CCI criteria was scored as absent or present but did not measure the effectiveness of each scored item. This means that recommendations coded as present might not be actionable, which could have overestimated how comprehensible the post was by the public. Third, the HBM was operationalized to identify explicit features that may motivate an individual to undergo a behaviour change; however, this study could not ascertain whether an individual would change their behaviours. Fourth, consistency was measured broadly to assess post patterns for key information surrounding outbreak and prevention information. However, this approach did not account for variation in the types of information present, which can mask inconsistencies.

4.6 Future research

Future research should explore the underlying causes of engagement rate variability over time, including assessing post characteristics to understand which features result in higher engagement to inform effective communication strategies. Further, the effectiveness to which the CCI was applied, beyond presence/absence, should be explored. While our study employed tools to assess the potential impact of posts on comprehension, other data collection methods, such as focus groups or surveys, are needed to directly assess comprehension. These insights can offer valuable perspectives on public comprehension of social media posts during multi-jurisdictional enteric illness outbreaks. Focus groups or surveys could also include open-ended questions to explore reasons for low engagement. Additionally, exploring consistency of content within and between posts, beyond merely assessing for the presence of key types of information, can help provide insights into content variations which may impact public trust. Furthermore, future studies could benefit from international comparisons to understand how communication strategies vary across different public health contexts.

5 Conclusion

Government social media accounts are critical to inform the public about outbreak investigations and preventive measures during multi-jurisdictional enteric illness outbreaks. This study examined social media posts for their potential to engage and impact public awareness and comprehension during multi-jurisdictional enteric illness outbreaks. Our findings indicate that although engagement is below average industry standards, posts do incorporate some of the CCI and HBM constructs to support public comprehension. Social media posts demonstrated message consistency, fostering trust, understanding, and use. Social media is a useful communication tool during multi-jurisdictional enteric illness outbreaks and could be enhanced by improving engagement, alignment with CCI criteria, and full integration of the HBM. Enhanced risk communication during multi-jurisdictional enteric illness outbreak investigations can contribute to reducing enteric illnesses among Canadians.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the corresponding author upon request.

Author contributions

VP: Data curation, Formal analysis, Investigation, Methodology, Visualisation, Writing – original draft. LG: Conceptualisation, Methodology, Project administration, Supervision, Writing – review & editing. HS: Investigation, Writing – review & editing. MM: Methodology, Writing – review & editing. LC: Conceptualisation, Methodology, Writing – review & editing. MP: Conceptualisation, Funding acquisition, Methodology, Writing – review & editing. AP: Methodology, Supervision, Writing – review & editing. JM: Conceptualisation, Methodology, Supervision, Writing – review & editing.

Funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This project was funded by the Public Health Agency of Canada (PHAC).

Acknowledgments

This project was made possible by the Public Health Agency of Canada’s (PHAC) Outbreak Management Division (OMD).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declare that no Generative AI was used in the creation of this manuscript.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcomm.2024.1512014/full#supplementary-material

References

Al-Dmour, H., Masa’deh, R., Salman, A., Abuhashesh, M., and Al-Dmour, R. (2020). Influence of Social Media Platforms on Public Health Protection Against the COVID-19 Pandemic via the Mediating Effects of Public Health Awareness and Behavioral Changes: Integrated Model. J Med Internet Res 22, e19996. doi: 10.2196/19996

Crossref Full Text | Google Scholar

Bajouk, H., and Ferré-Pavia, C. (2023). Government social media engagement and health perceptions during the COVID-19 crisis: a cross-sectional study. Int. J. Strateg. Commun. 17, 457–474. doi: 10.1080/1553118x.2023.2231417

Crossref Full Text | Google Scholar

Ball-Rokeach, S. J. (1985). The origins of individual media-system dependency: a sociological framework. Commun. Res. 12, 485–510. doi: 10.1177/009365085012004003

Crossref Full Text | Google Scholar

Barkley, J., Julian, E., Viveiros, B., Gosciminski, M., and Bandy, U. (2016). Preventing foodborne and enteric illnesses among at-risk populations in the United States and Rhode Island. R. I. Med. J. 99, 25–28

PubMed Abstract | Google Scholar

Becker, M. H. (1974). The health belief model and sick role behavior. Health Educ. Monogr. 2, 409–419. doi: 10.1177/109019817400200407

Crossref Full Text | Google Scholar

Bennett, G. G., and Glasgow, R. E. (2009). The delivery of public health interventions via the internet: actualizing their potential. Annu. Rev. Public Health 30, 273–292. doi: 10.1146/annurev.publhealth.031308.100235

PubMed Abstract | Crossref Full Text | Google Scholar

Bhattacharya, S., Srinivasan, P., and Polgreen, P. (2017). Social media engagement analysis of U.S. federal health agencies on Facebook. BMC Med. Inform. Decis. Mak. 17:49. doi: 10.1186/s12911-017-0447-z

PubMed Abstract | Crossref Full Text | Google Scholar

Brewer, N. T., Chapman, G. B., Gibbons, F. X., Gerrard, M., McCaul, K. D., and Weinstein, N. D. (2007). Meta-analysis of the relationship between risk perception and health behavior: the example of vaccination. Health Psychol. 26, 136–145. doi: 10.1037/0278-6133.26.2.136

Crossref Full Text | Google Scholar

Cao, D., Meadows, M., Wong, D., and Xia, S. (2021). Understanding consumers’ social media engagement behaviour: an examination of the moderation effect of social media context. J. Bus. Res. 122, 835–846. doi: 10.1016/j.jbusres.2020.06.025

Crossref Full Text | Google Scholar

Carpenter, C. J. (2010). A Meta-analysis of the effectiveness of health belief model variables in predicting behavior. Health Commun. 25, 661–669. doi: 10.1080/10410236.2010.521906

PubMed Abstract | Crossref Full Text | Google Scholar

Centers for Disease Control and Prevention (2017). CDC clear communication index. Available at: https://www.cdc.gov/ccindex/index.html

Google Scholar

Champion, V. L., and Skinner, C. S. (2008). “The health belief model” in Health behavior and health education: Theory, research, and practice. eds. K. Glanz, B. K. Rimer, and K. Viswanath, 45–65.

Google Scholar

Chou, W. Y. S., Gaysynsky, A., Trivedi, N., and Vanderpool, R. C. (2021). Using social Media for Health: National Data from HINTS 2019. J. Health Commun. 26, 184–193. doi: 10.1080/10810730.2021.1903627

PubMed Abstract | Crossref Full Text | Google Scholar

da Silva, L., Marinho, A. M. C. L., Braga, N. S., Santos, T. R., Abreu, M. H. N. G., Assunção, C. M., et al. (2021). Time series analysis of government information about COVID-19 on social media and the number of new cases during the first 6 months of the pandemic: the Brazil case. Res. Soc. Dev. 10:e501101623797. doi: 10.33448/rsd-v10i16.23797

Crossref Full Text | Google Scholar

Delisle, S., Dumbeck, C., Poste, A., and Romanas, A. (2021). More than technology: experiences of virtual emergency operations centers (VEOCs) during the COVID-19 pandemic response in Canada. J. Emerg. Manag. 19, 109–126. doi: 10.5055/jem.0618

Crossref Full Text | Google Scholar

DePaula, N., Hagen, L., Roytman, S., and Alnahass, D. (2022). Platform effects on public health communication: a comparative and National Study of message design and audience engagement across twitter and Facebook. JMIR Infodemiol. 2:e40198. doi: 10.2196/40198

PubMed Abstract | Crossref Full Text | Google Scholar

Dohle, S., Wingen, T., and Schreiber, M. (2020). Acceptance and adoption of protective measures during the COVID-19 pandemic: the role of trust in politics and trust in science. Soc. Psychol. Bull. 15:4315. doi: 10.32872/spb.4315

Crossref Full Text | Google Scholar

Dooley, J. A., Jones, S. C., and Iverson, D. C. (2012). Web 2.0 adoption and user characteristics. Available at: https://ro.uow.edu.au/sspapers/28

Google Scholar

Gamer, M., Lemon, J., and Singh, I. F. P. (2010). Irr: various coefficients of interrater reliability and agreement. doi: 10.32614/cran.package.irr

Crossref Full Text | Google Scholar

Glanz, K., and Bishop, D. B. (2010). The role of behavioral science theory in development and implementation of public health interventions. Annu. Rev. Public Health 31, 399–418. doi: 10.1146/annurev.publhealth.012809.103604

Crossref Full Text | Google Scholar

Government of Canada (2021). Directive on the Management of Communications. Available at: https://www.tbs-sct.canada.ca/pol/doc-eng.aspx?id=30682

Google Scholar

Grene, M., Cleary, Y., and Marcus-Quinn, A. (2017). Use of plain-language guidelines to promote health literacy. IEEE Trans. Prof. Commun. 60, 384–400. doi: 10.1109/tpc.2017.2761578

Crossref Full Text | Google Scholar

Guidry, J. P. D., Jin, Y., Orr, C. A., Messner, M., and Meganck, S. (2017). Ebola on Instagram and twitter: how health organizations address the health crisis in their social media engagement. Public Relat. Rev. 43, 477–486. doi: 10.1016/j.pubrev.2017.04.009

Crossref Full Text | Google Scholar

Haro-de-Rosario, A., Sáez-Martín, A., and del Carmen Caba-Pérez, M. (2018). Using social media to enhance citizen engagement with local government: twitter or Facebook? New Media Soc. 20, 29–49. doi: 10.1177/1461444816645652

Crossref Full Text | Google Scholar

Herati, H., Burns, K. E., Nascimento, M., Brown, P., Calnan, M., Dubé, È., et al. (2023). Canadians’ trust in government in a time of crisis: does it matter? PLoS One 18:e0290664. doi: 10.1371/journal.pone.0290664

PubMed Abstract | Crossref Full Text | Google Scholar

Holroyd, T. A., Oloko, O. K., Salmon, D. A., Omer, S. B., and Limaye, R. J. (2020). Communicating recommendations in public health emergencies: the role of public health authorities. Health Secur. 18, 21–28. doi: 10.1089/hs.2019.0073

PubMed Abstract | Crossref Full Text | Google Scholar

Hossain, L., Muhammad, R. H., and Wigand, R. T. (2015). Resilient information networks for coordination of foodborne disease outbreaks. Disaster Med. Public Health Prep. 9, 186–198. doi: 10.1017/dmp.2014.161

PubMed Abstract | Crossref Full Text | Google Scholar

Houts, P. S., Doak, C. C., Doak, L. G., and Loscalzo, M. J. (2006). The role of pictures in improving health communication: a review of research on attention, comprehension, recall, and adherence. Patient Educ. Couns. 61, 173–190. doi: 10.1016/j.pec.2005.05.004

PubMed Abstract | Crossref Full Text | Google Scholar

Hsieh, H.-F., and Shannon, S. E. (2005). Three approaches to qualitative content analysis. Qual. Health Res. 15, 1277–1288. doi: 10.1177/1049732305276687

Crossref Full Text | Google Scholar

Hyland-Wood, B., Gardner, J., Leask, J., and Ecker, U. K. H. (2021). Toward effective government communication strategies in the era of COVID-19. Humanit. Soc. Sci. Commun. 8, 1–11. doi: 10.1057/s41599-020-00701-w

Crossref Full Text | Google Scholar

Jeong, J. S., and Kim, S. Y. (2023). Risk perception and preventive behavior during the COVID-19 pandemic: testing the effects of government trust and information behaviors. Health Commun. 39, 376–387. doi: 10.1080/10410236.2023.2166698

PubMed Abstract | Crossref Full Text | Google Scholar

Jiang, H., Luo, Y., and Kulemeka, O. (2016). Social media engagement as an evaluation barometer: insights from communication executives. Public Relat. Rev. 42, 679–691. doi: 10.1016/j.pubrev.2015.12.004

Crossref Full Text | Google Scholar

Kass-Hout, T. A., and Alhinnawi, H. (2013). Social media in public health. Br. Med. Bull. 108, 5–24. doi: 10.1093/bmb/ldt028

Crossref Full Text | Google Scholar

Kelly, K. J., Campbell, A., Salijevic, A., Doak, S., Michael, L., and Montelpare, W. (2023). Evaluation of the readability, understandability, and actionability of COVID-19 public health messaging in Atlantic Canada. Front. Commun. 8:1006784. doi: 10.3389/fcomm.2023.1006784

Crossref Full Text | Google Scholar

Kent, M. L., and Taylor, M. (1998). Building dialogic relationships through the world wide web. Public Relat. Rev. 24, 321–334. doi: 10.1016/S0363-8111(99)80143-X

Crossref Full Text | Google Scholar

Kent, M. L., and Taylor, M. (2002). Toward a dialogic theory of public relations. Public Relat. Rev. 28, 21–37. doi: 10.1016/S0363-8111(02)00108-X

Crossref Full Text | Google Scholar

Kim, J. W. (2018). They liked and shared: effects of social media virality metrics on perceptions of message influence and behavioral intentions. Comput. Hum. Behav. 84, 153–161. doi: 10.1016/j.chb.2018.01.030

Crossref Full Text | Google Scholar

Korda, H., and Itani, Z. (2013). Harnessing social Media for Health Promotion and Behavior Change. Health Promot. Pract. 14, 15–23. doi: 10.1177/1524839911405850

PubMed Abstract | Crossref Full Text | Google Scholar

Krallman, A., Pelletier, M. J., and Adams, F. G. (2016). “@size vs. #impact: social media engagement differences amongst Facebook” in Twitter, and Instagram., in celebrating America’s pastimes: Baseball, hot dogs, apple pie and marketing? ed. K. K. Kim (Cham: Springer International Publishing), 557–561.

Google Scholar

Kwak, H., Lee, C., Park, H., and Moon, S. (2010). What is twitter, a social network or a news media?, In Proceedings of the 19th international conference on World wide web, (New York, NY: ACM), 591–600.

Google Scholar

Lachlan, K. A., Spence, P. R., Edwards, A., Reno, K. M., and Edwards, C. (2014). If you are quick enough, I will think about it: information speed and trust in public health organizations. Comput. Hum. Behav. 33, 377–380. doi: 10.1016/j.chb.2013.08.014

Crossref Full Text | Google Scholar

Li, X., and Liu, Q. (2020). Social Media Use, eHealth Literacy, Disease Knowledge, and Preventive Behaviors in the COVID-19 Pandemic: Cross-Sectional Study on Chinese Netizens. J. Med. Internet Res. 22, e19684–e19684. doi: 10.2196/19684

Crossref Full Text | Google Scholar

Li, Y., and Xie, Y. (2020). Is a picture worth a thousand words? An empirical study of image content and social media engagement. J. Mark. Res. 57, 1–19. doi: 10.1177/0022243719881113

Crossref Full Text | Google Scholar

Lim, V. W., Lim, R. L., Tan, Y. R., Soh, A. S., Tan, M. X., Othman, N. B., et al. (2021). Government trust, perceptions of COVID-19 and behaviour change: cohort surveys, Singapore. Bull. World Health Organ. 99, 92–101. doi: 10.2471/BLT.20.269142

PubMed Abstract | Crossref Full Text | Google Scholar

Limbu, Y. B., and Gautam, R. K. (2023). How well the constructs of health belief model predict vaccination intention: a systematic review on COVID-19 primary series and booster vaccines. Vaccines 11:816. doi: 10.3390/vaccines11040816

PubMed Abstract | Crossref Full Text | Google Scholar

Lin, X., Spence, P. R., Sellnow, T. L., and Lachlan, K. A. (2016). Crisis communication, learning and responding: best practices in social media. Comput. Hum. Behav. 65, 601–605. doi: 10.1016/j.chb.2016.05.080

Crossref Full Text | Google Scholar

Luquis, R. R., and Kensinger, W. S. (2019). Applying the health belief model to assess prevention services among young adults. Int. J. Health Promot. Educ. 57, 37–47. doi: 10.1080/14635240.2018.1549958

Crossref Full Text | Google Scholar

Maal, M., and Wilson-North, M. (2019). Social media in crisis communication – the “do’s” and “don’ts.”. Int. J. Disaster Resil. Built Environ. 10, 379–391. doi: 10.1108/IJDRBE-06-2014-0044

Crossref Full Text | Google Scholar

MacKay, M., Colangeli, T., Thaivalappil, A., Del Bianco, A., McWhirter, J., and Papadopoulos, A. (2022a). A review and analysis of the literature on public health emergency communication practices. J. Community Health 47, 150–162. doi: 10.1007/s10900-021-01032-w

PubMed Abstract | Crossref Full Text | Google Scholar

MacKay, M., Ford, C., Colangeli, T., Gillis, D., McWhirter, J. E., and Papadopoulos, A. (2022b). A content analysis of Canadian influencer crisis messages on Instagram and the public’s response during COVID-19. BMC Public Health 22:763. doi: 10.1186/s12889-022-13129-5

PubMed Abstract | Crossref Full Text | Google Scholar

Mahmood, Q. K., Jafree, S. R., Mukhtar, S., and Fischer, F. (2021). Social Media Use, Self-Efficacy, Perceived Threat, and Preventive Behavior in Times of COVID-19: Results of a Cross-Sectional Study in Pakistan. Front Psychol 12, 562042. doi: 10.3389/fpsyg.2021.562042

Crossref Full Text | Google Scholar

Marsh, C. (1985). Back on the bandwagon: the effect of opinion polls on public opinion. Br. J. Polit. Sci. 15, 51–74. doi: 10.1017/S0007123400004063

Crossref Full Text | Google Scholar

McHugh, M. L. (2012). Interrater reliability: the kappa statistic. Biochem. Med. 276–282, 276–282. doi: 10.11613/bm.2012.031

Crossref Full Text | Google Scholar

Meadows, C. Z., Tang, L., and Liu, W. (2019). Twitter message types, health beliefs, and vaccine attitudes during the 2015 measles outbreak in California. Am. J. Infect. Control 47, 1314–1318. doi: 10.1016/j.ajic.2019.05.007

PubMed Abstract | Crossref Full Text | Google Scholar

Men, L. R., Tsai, W.-H. S., Chen, Z. F., and Ji, Y. G. (2018). Social presence and digital dialogic communication: engagement lessons from top social CEOs. J. Public Relat. Res. 30, 83–99. doi: 10.1080/1062726x.2018.1498341

Crossref Full Text | Google Scholar

Meta (2024). How Facebook distributes content. Available at: https://www.facebook.com/business/help/718033381901819?id=208060977200861

Google Scholar

Microsoft Corporation (2018). Microsoft Excel. Available at: https://www.microsoft.com/en-ca/microsoft-365/excel

Google Scholar

Mikolajczyk, K. (2023). Average Engagement Rates for 13 Industries [November 2023]. Available at: https://blog.hootsuite.com/average-engagement-rate/ (Accessed May 28, 2024).

Google Scholar

Milgrom, K. (2015). The role of health Communications in Behavior Change. Available at: https://apcoworldwide.com/blog/the-role-of-health-communications-in-behavior-change/

Google Scholar

Moorhead, S. A., Hazlett, D. E., Harrison, L., Carroll, J. K., Irwin, A., and Hoving, C. (2013). A new dimension of health care: systematic review of the uses, benefits, and limitations of social media for health communication. J. Med. Internet Res. 15:e85. doi: 10.2196/jmir.1933

PubMed Abstract | Crossref Full Text | Google Scholar

Moran, G., Muzellec, L., and Johnson, D. (2019). Message content features and social media engagement: evidence from the media industry. J. Prod. Brand Manag. 29, 533–545. doi: 10.1108/JPBM-09-2018-2014

Crossref Full Text | Google Scholar

Mou, Y., and Lin, C. A. (2014). Communicating food safety via the social media. Sci. Commun. 36, 593–616. doi: 10.1177/1075547014549480

Crossref Full Text | Google Scholar

Ngigi, S., and Busolo, D. N. (2018). Behaviour change communication in health promotion: appropriate practices and promising approaches. Int. J. Innov. Res. Dev. 7:27. doi: 10.24940/ijird/2018/v7/i9/sep18027

Crossref Full Text | Google Scholar

Nunes, W. B., Firmino, R. T., Marinho, A. M. C. L., Baretto, L., Sousa, M. L. C., Silva, S. E.Da, et al. (2023). Clarity of publications on HPV in Instagram profiles of official health agencies in Brazil. Braz. Oral Res. 37. doi: 10.1590/1807-3107bor-2023.vol37.0103,:e103

PubMed Abstract | Crossref Full Text | Google Scholar

Oh, S.-H., Lee, S. Y., and Han, C. (2020). The effects of social media use on preventive behaviors during infectious disease outbreaks: the mediating role of self-relevant emotions and public risk perception. Health Commun. 36, 972–981. doi: 10.1080/10410236.2020.1724639

PubMed Abstract | Crossref Full Text | Google Scholar

Overbey, K. N., Jaykus, L.-A., and Chapman, B. J. (2017). A systematic review of the use of social media for food safety risk communication. J. Food Prot. 80, 1537–1549. doi: 10.4315/0362-028x.jfp-16-345

Crossref Full Text | Google Scholar

Patel, V., Grant, L. E., Shereefdeen, H., MacKay, M., Cheng, L., Phypers, M., et al. (2024). Evaluating Multi-Jurisdictional Enteric Illness Outbreak Messaging in Canada: A Content Analysis of Public Health Notices. Health Communication, 1–15.

Google Scholar

Park, S., and Jung, J. (2023). The interplay between social media virality metrics and message framing in influence perception of pro-environmental messages and behavioral intentions. Telematics Inform. 78:101947. doi: 10.1016/j.tele.2023.101947

Crossref Full Text | Google Scholar

Pew Research Center (2021). Social media fact sheet. Available at: https://www.pewresearch.org/internet/fact-sheet/social-media/

Google Scholar

Posit team (2023). RStudio: Integrated development environment for R. Available at: https://posit.co/

Google Scholar

Public Health Agency of Canada (2017). Canada’s foodborne illness outbreak response protocol (FIORP): A guide to multi-jurisdictional enteric outbreak response. Available at: https://www.canada.ca/en/public-health/services/publications/health-risks-safety/canadas-foodborne-illness-outbreak-response-protocol-fiorp-guide-multi-jurisdictional-enteric-outbreak-response.html

Google Scholar

Reuter, K., Wilson, M. L., Moran, M., Le, N., Angyan, P., Majmundar, A., et al. (2021). General audience engagement with antismoking public health messages across multiple social media sites: comparative analysis. JMIR Public Health Surveill. 7:e24429. doi: 10.2196/24429

PubMed Abstract | Crossref Full Text | Google Scholar

Rootman, I., and Gordon-El-Bihbety, D. (2008). A vision for a health literate Canada: Report of the expert panel on health literacy. Canadian Public Health Association.

Google Scholar

Rosenstock, I. M. (1974). Historical Origins of the health belief model. Health Educ. Monogr. 2, 328–335. doi: 10.1177/109019817400200403

Crossref Full Text | Google Scholar

Rus, H. M., and Cameron, L. D. (2016). Health communication in social media: message features predicting user engagement on diabetes-related Facebook pages. Ann. Behav. Med. 50, 678–689. doi: 10.1007/s12160-016-9793-9

PubMed Abstract | Crossref Full Text | Google Scholar

Seeger, M. W. (2006). Best practices in crisis communication: an expert panel process. J. Appl. Commun. Res. 34, 232–244. doi: 10.1080/00909880600769944

Crossref Full Text | Google Scholar

Seeger, M. W., Pechta, L. E., Price, S. M., Lubell, K. M., Rose, D. A., Sapru, S., et al. (2018). A conceptual model for evaluating emergency risk communication in public health. Health Secur. 16, 193–203. doi: 10.1089/hs.2018.0020

PubMed Abstract | Crossref Full Text | Google Scholar

Sehl, K., and Mikolajczyk, K. (2023). Engagement rate calculator + tips for 2024. Hootsuite. Available at: https://blog.hootsuite.com/calculate-engagement

Google Scholar

Sulat, J. S., Prabandari, Y. S., Sanusi, R., Hapsari, E. D., and Santoso, B. (2018). The validity of health belief model variables in predicting behavioral change. Health Educ. 118, 499–512. doi: 10.1108/he-05-2018-0027

Crossref Full Text | Google Scholar

Sundar, S. S. (2008). The MAIN model: A heuristic approach to understanding technology effects on credibility. Cambridge, MA: MacArthur Foundation Digital Media and Learning Initiative.

Google Scholar

Tang, L., Bie, B., Park, S.-E., and Zhi, D. (2018). Social media and outbreaks of emerging infectious diseases: a systematic review of literature. Am. J. Infect. Control 46, 962–972. doi: 10.1016/j.ajic.2018.02.010

PubMed Abstract | Crossref Full Text | Google Scholar

Tennant, B., Stellefson, M., Dodd, V., Chaney, B., Chaney, D., Paige, S., et al. (2015). eHealth literacy and web 2.0 health information seeking behaviors among baby boomers and older adults. J. Med. Internet Res. 17:e70. doi: 10.2196/jmir.3992

PubMed Abstract | Crossref Full Text | Google Scholar

Thomas, M. K., Murray, R., Flockhart, L., Pintar, K., Pollari, F., Fazil, A., et al. (2013). Estimates of the burden of foodborne illness in Canada for 30 specified pathogens and unspecified agents, circa 2006. Foodborne Pathog. Dis. 10, 639–648. doi: 10.1089/fpd.2012.1389

Crossref Full Text | Google Scholar

Tumpey, A. J., Daigle, D., and Nowak, G. (2019). “Communicating during an outbreak or public health investigation” in The CDC field epidemiology manual. eds. S. A. Rasmussen and R. A. Goodman (New York: Oxford University Press), 243–260.

Google Scholar

Twitter (2023). Twitter’s recommendation algorithm. https://blog.x.com/engineering/en_us/topics/open-source/2023/twitter-recommendation-algorithm

Google Scholar

Vahedian-Shahroodi, M., Tehrani, H., Robat-Sarpooshi, D., GHolian-Aval, M., Jafari, A., and Alizadeh-Siuki, H. (2019). The impact of health education on nutritional behaviors in female students: an application of health belief model. Int. J. Health Promot. Educ. 59, 70–82. doi: 10.1080/14635240.2019.1696219

Crossref Full Text | Google Scholar

Vereen, R. N., Westmaas, J. L., Bontemps-Jones, J., Jackson, K., and Alcaraz, K. I. (2020). Trust of Information about tobacco and E-cigarettes from health professionals versus tobacco or electronic cigarette companies: differences by subgroups and implications for tobacco messaging. Health Commun. 35, 89–95. doi: 10.1080/10410236.2018.1544875

PubMed Abstract | Crossref Full Text | Google Scholar

Voorveld, H. A. M., van Noort, G., Muntinga, D. G., and Bronner, F. (2018). Engagement with social media and social media advertising: the differentiating role of platform type. J. Advert. 47, 38–54. doi: 10.1080/00913367.2017.1405754

Crossref Full Text | Google Scholar

Wajeeha, A., Fahad, M., and Mughal, M. A. (2020). Information seeking through TV and facebook and health belief model: a case of province of Punjab(Pakistan) during covid-19 pandemic. J. Res. Soc. Pakistan 57:9.

Google Scholar

Gould, H. L., Walsh, K. A., Vieira, A. R., Herman, K., Williams, I. T., Hall, A. J., et al. (2013). Surveillance for foodborne disease outbreaks - United States, 1998–2008. Morb. Mortal. Wkly. Rep. Surveill. Summ. 62, 1–34.

Google Scholar

Watkins, B. A. (2017). Experimenting with dialogue on twitter: an examination of the influence of the dialogic principles on engagement, interaction, and attitude. Public Relat. Rev. 43, 163–171. doi: 10.1016/j.pubrev.2016.07.002

Crossref Full Text | Google Scholar

Webb, T. L., Joseph, J., Yardley, L., and Michie, S. (2010). Using the internet to promote health behavior change: a systematic review and meta-analysis of the impact of theoretical basis, use of behavior change techniques, and mode of delivery on efficacy. J. Med. Internet Res. 12:e4. doi: 10.2196/jmir.1376

PubMed Abstract | Crossref Full Text | Google Scholar

Wickham, H. (2016). ggplot2: elegant graphics for data analysis. New York: Springer-Verlag.

Google Scholar

Wickham, H. (2023). Stringr: simple, consistent wrappers for common string operations. Available at: https://stringr.tidyverse.org/

Google Scholar

Wickham, H., François, R., Henry, L., Müller, K., and Vaughan, D., (2023). Dplyr: A grammar of data manipulation. Available at: https://dplyr.tidyverse.org/

Google Scholar

Witte, K. (1992). Putting the fear back into fear appeals: the extended parallel process model. Commun. Monogr. 59, 329–349. doi: 10.1080/03637759209376276

Crossref Full Text | Google Scholar

Wray, R. J., Becker, S. M., Henderson, N., Glik, D., Jupka, K., Middleton, S., et al. (2008). Communicating with the public about emerging health threats: lessons from the pre-event message development project. Am. J. Public Health 98, 2214–2222. doi: 10.2105/AJPH.2006.107102

PubMed Abstract | Crossref Full Text | Google Scholar

Yang, X., Chen, L., and Feng, Q. (2015). Risk perception of food safety issue on social media. Chin. J. Commun. 9, 124–138. doi: 10.1080/17544750.2015.1111247

Crossref Full Text | Google Scholar

Zappavigna, M., and Martin, J. R. (2018). #communing affiliation: social tagging as a resource for aligning around values in social media. Discourse Context Media 22, 4–12. doi: 10.1016/j.dcm.2017.08.001

Crossref Full Text | Google Scholar

Zarcadoolas, C. (2010). The simplicity complex: exploring simplified health messages in a complex world. Health Promot. Int. 26, 338–350. doi: 10.1093/heapro/daq075

Crossref Full Text | Google Scholar

Zhao, Y. C., Zhao, M., and Song, S. (2022). Online health information seeking behaviors among older adults: systematic scoping review. J. Med. Internet Res. 24:e34790. doi: 10.2196/34790

PubMed Abstract | Crossref Full Text | Google Scholar