“My People Already Know That”: The Imagined Audience and COVID-19 Health Information Sharing Practices on Social Media
Bibliographic record
Abstract
This article examines how imagined audiences and impression management strategies shape COVID-19 health information sharing practices on social media and considers the implications of this for combatting the spread of misinformation online. In an interview study with 27 Canadian adults, participants were shown two infographics about masks and vaccines produced by the World Health Organization (WHO) and asked whether or not they would share these on social media. We find that interviewees' willingness to share the WHO infographics is negotiated against their mental perception of the online audience, which is conceptualized in three distinct ways. First, interviewees who would not share the infographics frequently describe a self-similar audience of peers that are "in the know" about COVID-19; second, those who might share the infographics conjure a specific and contextual audience who "needs" the information; and finally, those who said they would share the infographics most frequently conjure an abstract audience of "the public" or "my community" to explain that decision. Implications of these sharing behaviors for combatting the spread of misinformation are discussed.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".