Spike Protein Shedding on Reddit: Analyzing Online Discussions of an Anti-Vax Conspiracy Theory
Bibliographic record
Abstract
During the COVID-19 pandemic of 2020-2023, public opposition to vaccination was fueled in large part by online misinformation and disinformation. This content included a range of conspiracy theories that were shared on social media platforms. These theories spread rapidly and proved remarkably resistant to elimination through debunking. To shed light on this phenomenon, we conducted a qualitative examination of the "spike protein shedding" theory that gained prominence in 2021 on Reddit, a popular news and discussion platform. The theory centered on the claim that a harmful component of mRNA COVID-19 vaccines could be transmitted from vaccinated to unvaccinated individuals, presenting a significant health hazard. Some of those propounding the theory also claimed that government agencies and/or vaccine manufacturers were aware of the risk but chose to conceal it from the public. A thematic analysis of 63 Reddit threads taken from various subreddits revealed themes and patterns in how the topic was discussed by posters and those who responded to their posts. The results revealed that the theory was subject to surprisingly little critical debate on Reddit. Discussions instead took place in subreddit communities that functioned more as affinity spaces: Posters mainly invited and received support and/or advice from community members to shore up their existing beliefs on the issue and negatively caricatured those who disagreed with them rather than engaging with the substance of opposing arguments. The findings help explain the resilience of conspiracy theories that are shared online.
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How this classification was reachedexpand
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 | 0.003 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".