Impact of Elon Musk’s Tweeting about Psychiatric Medication on the Internet, Media, and Purchasing: Observational Study
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".