Public Health Communication and Engagement on Social Media during the COVID-19 Pandemic (Preprint)
Bibliographic record
Abstract
BACKGROUND Social media provides governments the opportunity to directly communicate with their constituents. During a pandemic, reaching as many citizens as possible with health messaging is critical to reducing the spread of the disease. This study evaluates efforts to spread healthcare information by Canadian local, provincial, and federal governments during the first five months of the COVID-19 pandemic. OBJECTIVE This study explores engagement patterns with government COVID-19 information shared on social media. It quantitatively evaluates platform-specific dynamics, including meta-data of posts such as account type, number of followers, type of content included, and time of post. It then performs exploratory, theory-building content analysis on outlier communications to identify previously under-examined features that contribute to engagement. METHODS We collect all health-related communications coming from government accounts on Facebook and Twitter and analyze the data using a nested mixed method approach. We first identify quantifiable features linked with citizen engagement. Then, we perform content analysis on those posts with the highest and highest negative residuals to identify content-specific engagement patterns. RESULTS We find considerable within and cross-platform heterogeneity in the relationship between embedded media type and engagement with public health information on social media. On Twitter, public health tweets containing videos receive 121 percent more engagements than those which are text-only, at P<.001. Images receive 35 percent more at P<.001. On Facebook text statuses dominate, with links receiving 39 percent fewer engagements, at P<.001, and videos a 26 percent decrease, also at P<.001. Even more, we find that who posts is more important than what is posted. Controlling for different audience sizes, tweets from the Prime Minister generate 727 percent more engagements than city governments', at P<.001. On Facebook this increases to 5640 percent, still at P<.001. The discrepancy between local and national accounts is larger on Facebook, where mayors, city governments, and local health authorities receive the least engagement. On both platforms premiers and provincial health authorities receive the second and third highest levels of engagement, highlighting the importance of sub-national officials in public health communication. All of these estimates are statistically significant at P<.001. In our qualitative analysis, we find a consistent relationship between content and over- or under performance, relative to our predicted levels of engagement. Concise messages with direct appeals are overrepresented among posts with large positive residuals, as are those which include high quality media, or which leverage pop-culture references or influencers. On the other hand, low quality video and infographics, lengthy policy descriptions, and negative news routinely generate fewer engagements than predicted. CONCLUSIONS We make two critical contributions to existing knowledge about government communication, particularly during public health crises. We identify and theorize cross-platform variations in strategy effectiveness and draw attention to specific, evidence-based practices that can increase engagement with government health information.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".