How Do Canadian Public Health Agencies Respond to the COVID-19 Emergency Using Social Media: A Protocol for a Case Study Using Content and Sentiment Analysis
Bibliographic record
Abstract
INTRODUCTION: Keeping Canadians safe requires a robust public health (PH) system. This is especially true when there is a PH emergency, like the COVID-19 pandemic. Social media, like Twitter and Facebook, is an important information channel because most people use the internet for their health information. The PH sector can use social media during emergency events for (1) PH messaging, (2) monitoring misinformation, and (3) responding to questions and concerns raised by the public. In this study, we ask: what is the Canadian PH risk communication response to the COVID-19 pandemic in the context of social media? METHODS AND ANALYSIS: We will conduct a case study using content and sentiment analysis to examine how provinces and provincial PH leaders, and the Public Health Agency of Canada and national public heath leaders, engage with the public using social media during the first wave of the pandemic (1 January-3 September 2020). We will focus specifically on Twitter and Facebook. We will compare findings to a gold standard during the emergency with respect to message content. ETHICS AND DISSEMINATION: Western University's research ethics boards confirmed that this study does not require research ethics board review as we are using social media data in the public domain. Using our study findings, we will work with PH stakeholders to collaboratively develop Canadian social media emergency response guideline recommendations for PH and other health system organisations. Findings will also be disseminated through peer-reviewed journal articles and conference presentations.
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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 | 0.045 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.039 | 0.007 |
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".