Meme-ifying Data: The Rise of Public Health Influencers on Instagram, TikTok, and Twitter during Covid-19
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it