Impact of Elon Musk’s Tweeting about Psychiatric Medication on the Internet, Media, and Purchasing: Observational Study
Why this work is in the frame
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Bibliographic record
Abstract
Background Public figures have an ability to shape public discourse, patterns of behaviors, and actions. Tech-billionaire Elon Musk, with nearly 100 million followers on Twitter, advocated for the decrease use of Wellbutrin with neutral-to-positive opinion of Ritalin.Objective We investigated Elon Musk’s Twitter posts, subsequent Google search trends, Amazon purchases, television airtime, and news articles on the terms Wellbutrin, bupropion, methylphenidate, Adderall, and Ritalin.Methods Twitter was indexed with Social Sprout, as well as to determine average analytics, impressions, and other necessary metrics. News and television airtime was catalogued in the United States’ 5 largest TV stations with the Global Database of Events, Language, and Tone. Google searches and shopping trends were analyzed with Google Trends. Amazon purchases were catalogued with Helium 10 software. Sentiment analysis was performed on Twitter hashtags using Sentiment Viz.Results From April 24 to May 14, 2022, EM made 3 tweets anecdotally about Wellbutrin and Ritalin, which resulted in a nearly 130% increase in retweets and 472% increase in comments compared to average. Sentiment on Twitter remained largely negative for Wellbutrin, compared to Ritalin. Wellbutrin was searched the most, along with its side effects and treatments, followed by Ritalin, then Adderall, Bupropion, and Methylphenidate. Bupropion and Methylphenidate had extended search periods, compared to Ritalin and Wellbutrin. Purchasing of all top Ritalin products increased on Amazon (18% increase compared to previous week), whereas Wellbutrin-like products decreased in purchasing by 11% on average.Conclusions Twitter has mass sway and influence on populations, including their purchasing power. Public health officials must work to combat medical misinformation on the platform.
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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 | 0.009 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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