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Public health agencies widely adopt social media for health and risk communication. Moreover, different platforms have different affordances, which may impact the quality and nature of the messaging and how the public engages with the content. However, these platform effects are not often compared in studies of health and risk communication and not previously for the COVID-19 pandemic.
This study measures the potential media effects of Twitter and Facebook on public health message design and engagement by comparing message elements and audience engagement in COVID-19–related posts by local, state, and federal public health agencies in the United States during the pandemic, to advance theories of public health messaging on social media and provide recommendations for tailored social media communication strategies.
We retrieved all COVID-19–related posts from major US federal agencies related to health and infectious disease, all major state public health agencies, and selected local public health departments on Twitter and Facebook. A total of 100,785 posts related to COVID-19, from 179 different accounts of 96 agencies, were retrieved for the entire year of 2020. We adopted a framework of social media message elements to analyze the posts across Facebook and Twitter. For manual content analysis, we subsampled 1677 posts. We calculated the prevalence of various message elements across the platforms and assessed the statistical significance of differences. We also calculated and assessed the association between message elements with normalized measures of shares and likes for both Facebook and Twitter.
Distributions of message elements were largely similar across both sites. However, political figures (
In general, we find a data and policy orientation for Twitter messages and users and a local and personal orientation for Facebook, although also many similarities across platforms. Message elements that impact engagement are similar across platforms but with some notable distinctions. This study provides novel evidence for differences in COVID-19 public health messaging across social media sites, advancing knowledge of public health communication on social media and recommendations for health and risk communication strategies on these online platforms.
Social media have become integral tools for public health messaging and online communication of health and risk information worldwide [
There is ample research on social media use by public health agencies [
Research on public health messaging on social media has focused on 2 broad areas: (1) the
Audience or user engagement on social media is often formalized in the platform via a
Although studies in the social media literature recognize the distinct
In practice, Facebook is more widely adopted than Twitter across all demographic groups [
For this study, we aim to assess differences in public health message design elements and audience engagement with the various message elements across Twitter and Facebook regarding COVID-19 during 1 year of the pandemic. We therefore ask the following research questions (RQs):
RQ1. How do public health message design elements differ across Twitter and Facebook?
RQ2. How does audience engagement with public health message elements differ across Twitter and Facebook?
In the following sections, we describe the methods of the study, the results, and the discussion in relation to the literature and provide evidence-based policy recommendations for better-targeted health communication strategies.
We identified 11 major federal health agencies in the United States associated with infection prevention and control [
On Twitter, we identified 11 federal accounts (with a total of COVID-19–related original posts and retweets), 48 state accounts (with a total of 40,716 posts and retweets), and 33 local accounts (with a total of 20,164 posts and retweets) that matched the criteria. On Facebook, we identified 10 federal accounts (with a total of 3592 posts), 49 state accounts (with a total of 34,930 posts), and 38 local accounts (with a total of 14,356 posts) that matched the criteria. On Facebook, it is more difficult to differentiate original posts from shared posts; the figures just reported for Facebook include both. This data set of all COVID-19–related posts from all identified agencies in 2020 was called the
For manual content analysis, we used a stratified random sampling technique where we sampled 900 posts from Twitter and 900 posts from Facebook proportional to the amount of posts made by agency level (ie, local vs state vs federal), the
We adapted an existing framework [
Definitions and examples of message elements: textual.
Textual element | Definition | Example | |
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Representative | Clause in declarative form, describing a behavior, state, or event | “#COVID19 can be spread by people who do not have symptoms” |
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Directive | A sentence that directs, commands, or mandates an action, especially via an imperative sentence | “Continue to wear masks” OR “Donate blood.” |
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Question | A rhetorical question or question prompt | “Are you looking for work? We are hiring!” |
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Expressive | Expression of sentiment by the message speaker (eg, sadness, appreciation) | “Thank you, #EMS heroes, for staying strong” |
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Request | Request to participate in research, volunteer, or means to reach an agency | “Call us for questions at this number” |
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Protection | Information about what to do to prevent or treat the issue | “Disinfect things you and your family touch frequently” |
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Policy | Actions, policies, or programs of officials, government agencies, or related entities | “Multnomah County is almost ready for reopening schools.” |
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Surveillance | Statistics or data about prevalence (eg, cases/deaths) | “Yesterday, there were 85 new deaths” |
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Science | Describes or explains a cause, mechanism, or symptom of the issue | “there is no evidence that produce can transmit #COVID19” |
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Emergent | Event of emergency concern or immediate priority | “Travelers: DON'T book air travel to NY for just a few days” |
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Interactive | Interactive service, such as question-and-answer (Q&A) with policy makers or watching live | “FDA will host a virtual Town Hall on 3D printed swabs” |
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Material | Testing sites, financial assistance, vaccine provision | “Use our map to find locations for vaccination sites.” |
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Corrective | Correction of a rumor, misinformation, or pointing to related resources | “A death previously reported in Warren was incorrect, and has been removed.” |
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Group | Refers to a demographic group (eg, adults, Hispanics) or a vulnerable population | “Cancer patients are among those at high risk of serious illness from a COVID19 infection.” |
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Secondary | Consequences of or issues directly related to the main issue | “Many are feeling stressed because of #COVID19.” |
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Other language | Message or part of message in another language, including sign language | “Números del #COVID19 en California:” |
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External | Expert or staff from another agency | “The head of the CDC will speak…” |
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Political | Mayor, governor, or other political figure | “Watch the Mayor’s updates on…” |
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Expert | Expert or staff of the agency | “Our own Dr. Elinore will discuss the crisis” |
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Personality | Nonpolitical or nongovernmental personality, including celebrities or community members | “Juan from Blue Eagles football club speaks about COVID19” |
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Collective | Focus on collective terms to characterize an issue or to address it | “We all need to do our part to combat Covid-19” |
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Emphasis | Sentence with an explanation point or with all capitalized directive | “WEAR a mask!” |
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Positive | Positive framing of agency action | “We’re making progress is getting vaccines” |
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Metaphor | Using metaphors to explain the science or prevention of the issue | “The swiss cheese respiratory virus defense” |
Definitions and examples of message elements: media.
Media element | Definition | Example |
Hyperlink | A long or short web URL | https://twitter.com/... |
Hashtag | Any term preceded by a # symbol | #COVID-19 #WearAMask |
Text-in-image | Image with additional text not included in the text part of the message | See examples below. |
Illustration | Illustration in the image—at least beyond use of a table and colors |
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Photograph | Photograph of a person, object, or scene |
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Infographic | Image that conveys data or illustrated directives (overrides illustration) |
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Video | A video embedded in the message |
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The content analysis consisted of manual binary coding for the presence or lack of each element in a post. As the definition of the categories became apparent, the nature of some definitions made some categories mutually exclusive, especially within each textual or media dimension. For example, a question is, by definition, not a
A random training sample of 150 posts (75, 50%, from Twitter and 75, 50%, from Facebook) was first retrieved for training and category development. Using these 150 posts, during training, 3 authors updated and defined the message categories. Once this training was accomplished, the 3 authors independently began coding a 20% subsample of the
After obtaining IRR measures, the coders discussed the results. At this point, the results were not perfect and discrepancies in coding existed and needed to be reconciled. In particular, there were issues with the
To address our first RQ, we calculated the distribution of each message element on Twitter and Facebook and then compared this total across platforms via an independent 2-sample Z-test of proportions, where the null hypotheses assumed that the proportion of each message element is equal on both platforms. Although Z-tests expect normal distributions, and social media phenomena are notoriously not normally distributed, given the relatively large sample of most message elements, we found it reasonable to apply the Z-tests [
To address our second RQ, we operationalized audience engagement as normalized frequencies of likes and shares. Other studies have used the CTR to measure audience engagement [
We calculated a measure of normalized likes (NLm) as the number of likes of each message “m,” divided by the follower count of the account that posted the message. NLm is the percentage of the agency’s follower count that liked the message. Although Facebook includes additional positive and negative measures of audience engagement—namely
Similar to normalized likes, we created a measure of normalized shares (NSm) of each message “m.” The NSm measure, compared to likes, can be more directly considered a
For every message element, we then computed the
Statisticsa for the sample data set as a percentage of the population of COVID-19 posts in 2020.
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Local, n/N (%) | State, n/N (%) | Federal, n/N (%) | All, n/N (%) |
Facebook accounts | 32/38 (84.0) | 48/49 (98.0) | 9/10 (90.0) | 89/97 (92.0) |
Twitter accounts | 29/33 (88.0) | 45/48 (94.0) | 9/11 (82.0) | 83/92 (90.0) |
Facebook total posts | 231/14,356 (1.6) | 560/34,930 (1.6) | 60/3592 (1.7) | 851/52,878 (1.6) |
Twitter total posts | 262/15,421 (1.7) | 482/27,866 (1.7) | 82/4620 (1.8) | 826/47,907 (1.7) |
aStatistics are for the final sample data set used in content and statistical analyses in relation to the population data set of all COVID-19–related posts from all accounts identified in 2020.
Box plot of IQRs of followers per account across agency levels and platforms.
Message design elements across Facebook (n=851) and Twitter (n=826) posts.
Message element | Facebook, n (%) | Twitter, n (%) | Z-score | |||
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Representative | 755 (88.7) | 722 (87.4) | –0.83 | .41 | |
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Directive | 344 (40.4) | 374 (45.2) | 2.01 | .04 | |
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Question | 107 (12.5) | 96 (11.6) | –0.60 | .55 | |
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Expressive | 79 (9.2) | 77 (9.3) | 0.03 | .98 | |
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Request | 28 (3.2) | 38 (4.6) | 1.40 | .17 | |
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Protection | 391 (45.9) | 395 (47.8) | 0.77 | .44 | |
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Policy | 292 (34.3) | 321 (38.8) | 1.93 | .05 | |
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Surveillance | 160 (18.8) | 222 (26.8) | 3.94 | <.001 | |
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Science | 73 (8.5) | 78 (9.4) | 0.62 | .53 | |
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Emergent | 39 (4.5) | 26 (3.1) | –1.52 | .13 | |
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Interactive | 192 (22.5) | 175 (21.1) | –0.68 | .49 | |
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Material | 112 (13.1) | 112 (13.5) | 0.24 | .81 | |
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Corrective | 11 (1.2) | 12 (1.4) | 0.28 | .78 | |
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Group | 85 (9.9) | 113 (13.6) | 2.34 | .02 | |
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Secondary | 73 (8.5) | 59 (7.1) | –1.09 | .27 | |
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Other language | 42 (4.9) | 25 (3.0) | –1.99 | .04 | |
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External | 153 (17.9) | 86 (10.4) | –4.43 | <.001 | |
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Political | 89 (10.4) | 28 (3.3) | –5.68 | <.001 | |
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Expert | 66 (7.7) | 39 (4.7) | –2.56 | .01 | |
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Personality | 17 (1.9) | 5 (0.6) | –2.51 | .01 | |
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Collective | 123 (14.4) | 105 (12.7) | –1.04 | .30 | |
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Emphasis | 103 (12.1) | 81 (9.8) | –1.50 | .13 | |
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Positive | 12 (1.4) | 23 (2.7) | 1.97 | .05 | |
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Metaphor | 5 (0.5) | 2 (0.2) | –1.10 | .27 | |
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Hyperlink | 485 (56.9) | 597 (72.2) | 6.54 | <.001 | |
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Hashtag | 392 (46.0) | 613 (74.2) | 11.76 | <.001 | |
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Text-in-image | 387 (45.4) | 343 (41.5) | –1.63 | .10 | |
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Illustration | 235 (27.6) | 258 (31.2) | 1.63 | .10 | |
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Photograph | 196 (23.0) | 170 (20.5) | –1.22 | .22 | |
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Infographic | 101 (11.8) | 149 (18.0) | 3.55 | <.001 | |
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Video | 130 (15.2) | 83 (10.0) | –3.21 | <.001 |
Elements used significantly more on Facebook and significantly more on Twitter.
Mean percentage of account followers that liked messages with and without specific elements.
Message element | ||||||||||||||
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With feature | Without feature | With feature | Without feature | ||||||||||
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Representative | 0.16 | 0.26 | .22 | 0.08 | 0.05 | <.001 | |||||||
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Directive | 0.20 | 0.15 | .01 | 0.07 | 0.09 | <.001 | |||||||
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Question | 0.26 | 0.16 | .04 | 0.05 | 0.08 | <.001 | |||||||
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Expressive | 0.28 | 0.16 | <.001 | 0.10 | 0.08 | <.001 | |||||||
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Request | 0.52 | 0.16 | .05 | 0.06 | 0.08 | .32 | |||||||
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Protection | 0.18 | 0.17 | .43 | 0.08 | 0.08 | .02 | |||||||
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Policy | 0.19 | 0.17 | .03 | 0.09 | 0.07 | .20 | |||||||
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Surveillance | 0.13 | 0.18 | .02 | 0.12 | 0.07 | <.001 | |||||||
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Science | 0.14 | 0.18 | .41 | 0.05 | 0.08 | .08 | |||||||
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Emergent | 0.14 | 0.17 | .26 | 0.25 | 0.07 | .06 | |||||||
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Interactive | 0.17 | 0.17 | .20 | 0.07 | 0.08 | .04 | |||||||
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Material | 0.05 | 0.19 | <.001 | 0.05 | 0.08 | <.001 | |||||||
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Corrective | 0.18 | 0.17 | .49 | 0.41 | 0.07 | .03 | |||||||
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Group | 0.16 | 0.17 | <.001 | 0.04 | 0.09 | <.001 | |||||||
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Secondary | 0.13 | 0.18 | .13 | 0.06 | 0.08 | .01 | |||||||
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Other language | 0.10 | 0.18 | .07 | 0.02 | 0.08 | <.001 | |||||||
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External | 0.13 | 0.18 | .07 | 0.06 | 0.08 | .13 | |||||||
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Political | 0.12 | 0.18 | .01 | 0.06 | 0.08 | .08 | |||||||
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Expert | 0.17 | 0.17 | .06 | 0.06 | 0.08 | .42 | |||||||
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Personality | 0.22 | 0.17 | .01 | 0.06 | 0.08 | .30 | |||||||
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Collective | 0.27 | 0.16 | <.001 | 0.10 | 0.08 | .004 | |||||||
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Emphasis | 0.29 | 0.16 | .004 | 0.08 | 0.08 | .10 | |||||||
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Positive | 0.41 | 0.17 | .12 | 0.10 | 0.08 | .43 | |||||||
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Metaphor | 0.41 | 0.17 | .09 | 0.02 | 0.08 | .26 | |||||||
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Hyperlink | 0.15 | 0.20 | <.001 | 0.07 | 0.10 | <.001 | |||||||
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Hashtag | 0.19 | 0.16 | .32 | 0.07 | 0.10 | .01 | |||||||
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Text-in-image | 0.17 | 0.17 | .01 | 0.09 | 0.07 | .002 | |||||||
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Illustration | 0.10 | 0.20 | .03 | 0.06 | 0.09 | .12 | |||||||
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Photograph | 0.21 | 0.16 | .08 | 0.07 | 0.08 | <.001 | |||||||
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Infographic | 0.20 | 0.17 | <.001 | 0.12 | 0.07 | <.001 | |||||||
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Video | 0.21 | 0.17 | .07 | 0.07 | 0.08 | .09 |
a
Mean percentage of account followers that shared messages with and without specific features.
Message element | |||||||
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With feature | Without feature | With feature | Without feature | |||
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Representative | 0.20 | 0.16 | .01 | 0.06 | 0.03 | <.001 |
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Directive | 0.17 | 0.21 | .27 | 0.05 | 0.06 | <.001 |
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Question | 0.22 | 0.19 | .10 | 0.04 | 0.06 | .003 |
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Expressive | 0.29 | 0.19 | .06 | 0.07 | 0.05 | .06 |
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Request | 0.53 | 0.18 | .18 | 0.05 | 0.06 | .39 |
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Protection | 0.16 | 0.23 | .03 | 0.05 | 0.06 | .002 |
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Policy | 0.16 | 0.21 | .004 | 0.06 | 0.05 | .04 |
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Surveillance | 0.25 | 0.18 | <.001 | 0.09 | 0.04 | <.001 |
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Science | 0.15 | 0.20 | .36 | 0.04 | 0.06 | .05 |
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Emergent | 0.28 | 0.19 | .04 | 0.12 | 0.05 | .03 |
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Interactive | 0.30 | 0.17 | .34 | 0.05 | 0.06 | .35 |
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Material | 0.05 | 0.22 | <.001 | 0.04 | 0.06 | .45 |
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Corrective | 0.18 | 0.20 | .29 | 0.18 | 0.05 | .19 |
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Group | 0.12 | 0.20 | .001 | 0.03 | 0.06 | <.001 |
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Secondary | 0.33 | 0.18 | .19 | 0.04 | 0.06 | .01 |
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Other language | 0.09 | 0.20 | .16 | 0.02 | 0.06 | .001 |
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External | 0.10 | 0.22 | .04 | 0.05 | 0.06 | .18 |
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Political | 0.07 | 0.21 | <.001 | 0.02 | 0.06 | .01 |
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Expert | 0.07 | 0.21 | .26 | 0.03 | 0.06 | .02 |
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Personality | 0.08 | 0.20 | .31 | 0.03 | 0.06 | .21 |
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Collective | 0.20 | 0.19 | .07 | 0.07 | 0.05 | .28 |
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Emphasis | 0.44 | 0.16 | .04 | 0.06 | 0.05 | .31 |
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Positive | 0.22 | 0.20 | .34 | 0.05 | 0.06 | .40 |
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Metaphor | 1.63 | 0.19 | .27 | 0.01 | 0.06 | .14 |
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Hyperlink | 0.14 | 0.26 | .003 | 0.05 | 0.06 | .08 |
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Hashtag | 0.26 | 0.14 | .37 | 0.05 | 0.06 | .04 |
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Text-in-image | 0.24 | 0.16 | <.001 | 0.07 | 0.05 | <.001 |
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Illustration | 0.15 | 0.21 | .40 | 0.05 | 0.06 | .13 |
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Photograph | 0.11 | 0.22 | <.001 | 0.04 | 0.06 | <.001 |
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Infographic | 0.26 | 0.19 | <.001 | 0.09 | 0.05 | <.001 |
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Video | 0.10 | 0.21 | .01 | 0.04 | 0.06 | .01 |
a
Significant changes in likes and shares associated with the inclusion of message element. The blue bars refer to increases and the red bars to decreases in mean normalized likes and mean normalized shares associated with the inclusion of the message element.
This study analyzed 1677 COVID-19–related posts on Facebook and Twitter, by public health agencies across the United States in 2020, and found differences and similarities in the overall use and popularity of these sites in terms of message design elements and audience engagement. Our results show that Facebook posts received 2.25 times more likes and 4.4 times more shares, in general, than posts on Twitter. However, within each platform, messages received more shares than likes within Facebook—as a percentage of account followers that liked or shared the message—whereas on Twitter, measures were more liked than shared.
Our results show that messages on Twitter, compared to Facebook, are significantly more focused on
We observed that data (eg,
The distribution of message design elements is largely similar across both platforms, suggesting consistency in public health messaging, but with some significant differences between the 2 social media sites studied. Results also show significant associations between message elements and audience engagement, with some expected and surprising differences across platforms. In general—for this health and risk communication scenario—we may thus suggest that Twitter has more of a data and policy orientation, whereas Facebook has more of a local and personal orientation on the content, which largely follows the literature on social media affordances.
Previous studies have examined the characteristics of Facebook in relation to Twitter as 2 of the major social media sites in the United States and in the world today. Generally, studies support the notion that Twitter is more of a “news media” [
In this study, we did not analyze whether certain platform features caused the use of specific message elements or whether certain message features caused more or less engagement. However, our results generally support the existing literature that suggests that Facebook, while bigger and more popular across the US adult population, has more of a local and personal orientation, associated with close social interactions. Twitter, in contrast, is both a more active and a popular site for federal agencies, compared to local and state agencies, and both the content and engagement on Twitter point to more of a data and policy orientation. Ultimately, we observe great similarities in message elements and audience engagement across Facebook and Twitter, suggesting a standardization of social media policies and practices across agencies and platforms, and also similarities in user engagement on both Facebook and Twitter.
This study provides some evidence for policy recommendations on social media health communication strategies. These recommendations are based on the results of this study, which is focused on COVID-19 communication during the beginning and multiple waves of the pandemic in 2020. Public health agencies and further research need to assess whether these are valid for broader contexts as well.
For public health agencies using Facebook, we recommend caution when using political figures and external experts on their messages and instead highlight nonpolitical or nongovernment personalities, such as local celebrities or ordinary individuals who have a special story to tell. We also see an opportunity for greater or at least continued use of emotional expressions on messages and the use of collective frames to generate greater positive engagement.
Our results show that messages on Facebook, compared to Twitter, are significantly more focused on highlighting political figures, as well as internal and external experts. However, political figures and external experts were generally associated with less engagement on Facebook. Personalities, including celebrities or ordinary people (eg, an authentic post of a child from the community), were significantly associated with greater engagement on Facebook but were present in few posts (2%) on Facebook. Ultimately, the use of expressives (ie, expressing emotions) and collective frames (eg, using collective pronouns and focusing on collective issues) were particularly well engaged with on Facebook.
For public health agencies using Twitter, we recommend caution on the use of hyperlinks and hashtags on Twitter messages if the goal is to increase message likes and overall message diffusion, but continued use of surveillance information and infographics is recommended. Moreover, we recommend a greater focus on messages containing emergent issues (eg, emergency or timely information), and the use of correctives to address misinformation, because these were both not prevalent but were associated with greater positive engagement.
Our results show that messages on Twitter, compared to Facebook, are significantly more focused on policy and surveillance information and include significantly more hyperlinks and hashtags compared to messages on Facebook. Since the hashtag is a textual construction first popularized on Twitter, this is not surprising. However, both hashtags and hyperlinks were generally associated with less engagement on Twitter. Surveillance information and infographics, however, were generally associated with greater engagement on Twitter. Emergent issues, and correctives, were particularly well engaged with on Twitter. However, correctives were included in a minority of tweets (1.4%). Given that social media is part of a misinformation crisis [
For public health agencies using both platforms, we recommend careful use of images in their messages, including photographs, illustrations, and videos, as these were all media types associated with less engagement across both platforms. However, including text-in-image is a reasonable recommendation, since these were associated with greater engagement across platforms.
In general, our results show that not all types of images are similarly engaged with. On both platforms, photographs were significantly associated with fewer shares, whereas infographics were generally associated with greater shares and likes. Although illustrations were associated with fewer likes and shares on both platforms, this negative impact was only significant for Facebook likes. Infographics about the pandemic were associated with higher engagement on both platforms, but they were also largely prevalent. Therefore, the amount of use of these features in this context is likely sufficient. Lastly, text-in-image was generally associated with greater likes and shares on Twitter and greater sharing on Facebook, highlighting the importance of textual and semantic content along with visual content.
This study intended to show how public health agencies construct their messages across Facebook and Twitter and how users respond to these messages similarly or differently across platforms. To control for aspects of the message topic, we only focused on COVID-19–related messages. COVID-19 is also a major health and risk issue and one that we could expect public health agencies in the country to be communicating about in 2020. However, the focus on COVID-19 puts a limitation on the extent to which we can generalize the findings to health and risk communication more broadly. Moreover, the statistical tests used could be improved with a regression model that assesses and controls for other variables on audience engagement. Nevertheless, our random sampling technique, over multiple kinds of agencies and an entire year, helps us generalize and have confidence in the results.
Health communicators should consider that social media algorithms themselves are problematic as they lead to echo chamber effects [
There were few posts with personalities featured on Facebook (17/851, 1.9%) and Twitter (5/826, 0.6%) posts. We could thus not properly assess the impact of this message element on engagement. However, celebrities and personal stories can positively influence health behavior and may be further studied in this context [
In general, we find a data and policy orientation for Twitter messages and users and a local and personal orientation for Facebook, although also many similarities across both platforms. Message elements that impact engagement are similar across both platforms but with some notable distinctions. This study provides novel evidence for differences in COVID-19 public health messaging on social media, advancing health communication research and recommendations for health and risk communication strategies.
Sampled accounts and κ values.
Detailed framework description and coding rules.
Sample statistics and analyses.
application programming interface
click-through rate
interrater reliability
research question
Wilcoxon-Mann-Whitney
None declared.