A two-year follow-up: Twitter activity regarding misinformation about spinal manipulation, chiropractic care and boosting immunity during the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: Spinal manipulative therapy (SMT) is offered by many health professions, most often by chiropractors. While SMT can be effective for some musculoskeletal disorders, there is no evidence that SMT improves human immunity in a clinically meaningful way. Despite this, we showed previously that Twitter misinformation about chiropractic/SMT improving immunity increased sharply at the start of the COVID-19 pandemic. Here, we perform a two-year follow-up. METHODS: We previously employed specialized software (i.e. Talkwalker) to search the entirety of Twitter activity in the months before and after the COVID-19 pandemic was declared (March 11, 2020). In this paper, we conducted follow-up searches over two successive 12 month periods using terms related to SMT, immunity and chiropractic. The resulting tweets were then coded into those promoting/refuting a relation between SMT and immunity (tone) and messaging about chiropractic/interventions (content). Further analyses were performed to subcategorize tweet content, tally likes, retweets and followers, and evaluate refuting tweets and the country of origin. Finally, we created a chronology of Twitter activity superimposed with dates of promoting or refuting activities undertaken by chiropractic organizations. RESULTS: Over the 27 month study period, Twitter activity peaked on March 31, 2020 then declined continuously. As in our first paper, our follow-up data showed that (1) the ratio of refuting/promoting tweets remained constant and (2) tweets that refuted a relationship between SMT and immunity were substantially more liked, retweeted and followed than those promoting. We also observed that promoting tweets suggesting that SMT improves immunity decreased more rapidly. Overwhelmingly, promoting tweets originated in the USA while refuting tweets originated in Canada, Europe and Australia. The timing of the decline in peak Twitter activity, together with a parallel decline in tweets claiming that SMT improves immunity, was coincident with initiatives by chiropractic organizations and regulators targeting misinformation. CONCLUSION: Overwhelmingly, Twitter activity during the COVID-19 pandemic focussed on refuting a relation between chiropractic/SMT and immunity. A decline in Twitter activity promoting a relation between SMT and immunity was observed to coincide with initiatives from chiropractic organizations and regulators to refute these claims. The majority of misinformation about this topic is generated in the United States.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 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 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".