Meme-ifying Data: The Rise of Public Health Influencers on Instagram, TikTok, and Twitter during Covid-19
Bibliographic record
Abstract
This article argues for the importance of the memetic tactic of bricolage within contemporary social media science communication for its capacity to curate and distill approachable, accessible, and shareable Covid-19 content. We suggest that the social media communication practices of what we call ‘public health influencers’ (PHIs) on Instagram, Tik Tok, and Twitter make use of memetic bricolage techniques of stop motion, collage, infographics, and placarding, coupled with an ethos of ‘micro-celebrity,’ in order to advance stalled public conversations and to reorient the spread of disinformation back to evidence-based facts. To make this argument, we analyze the cross-platform social media work of three key PHIs during the pediatric vaccination campaigns of late 2021 within our local context of Ontario, Canada to reflect on the effectiveness of social media presence, communication, and advocacy. Through memetic tactics, we argue that PHIs’ efforts to engage the public are driven by a larger impulse to combat health inequities that are exacerbated by the different forms of disinformation circulating on social media. Ultimately, this article illustrates how the concerted effort against disinformation by PHIs on social media via memes contributes to advocacy for more accessible, just, and equitable health care for Ontarians.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.014 | 0.014 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".