Use of Social Media in Crisis Communication in the Federal Government During COVID-19 Pandemic: Analysis of Responses Strategies
Bibliographic record
Abstract
Social media has become a prime tool in communicating during crises. Ongoing COVID-19 pandemic illustrates this trend strongly with the Canadian government conveying messaging through many platforms. In this thesis, we aimed to explore how Public Health Agency of Canada (PHAC) and Health Canada (HC) communicate with Canadians through social media, namely Twitter. Based on insights from social media practices drawn from work by Wendling et al. (2013); Lin et al. (2016) and from social mediated crisis communication theory of Austin et al. (2012), we sought to find what type of social media strategies adopted to execute crisis communication during COVID-19 pandemic. We also aimed at understanding to what extent these reflect the theoretical framework of the present study. To undertake this research, we opted for a mix of quantitative and qualitative analysis enabled by thematic analysis to identify categories of meaning and their trend. We found that social media strategies adopted by PHAC and HC have many aspects in common with the theoretical framework, yet they offer many nuances in practices driven mainly by the length of the crisis and the uncertainty it has caused. This thesis brings theory into practice by researching an ongoing crisis, gauging social media use practices against messaging strategies. It also calls on the need for updating theories and good practices in light of the outcomes of the COVID-19 crisis.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".