Reacting, Sharing, and Commenting: How Many Facebook Users Are Engaging with Posts Related to Dental Caries That Contain Misinformation?
Bibliographic record
Abstract
Recent studies have been concerned about the vast amount of misinformation detected on social media that directly hampers the prevention and control of chronic diseases. Based on these facts, the aim of this study was to identify and characterize misinformation about dental caries-related content found on Facebook, regarding the predictive factors of user interaction with posts. Then, CrowdTangle retrieved 2,436 posts published in English, ordered by the total interaction of the highest users. A total of 1,936 posts were selected for inclusion and exclusion criteria to select a sample of 500 posts. Subsequently, two independent investigators characterized the posts by their time of publication, author's profile, motivation, the aim of content, content facticity, and sentiment. The statistical analysis was performed using Mann-Whitney U and χ2 tests and multiple logistic regression models to determine differences and associations between dichotomized characteristics. p values <0.05 were considered significant. In general, posts were predominantly originated from the USA (74.8%), related to business profiles (89%), presented preventive content (58.6%), and noncommercial motivation (91.6%). Furthermore, misinformation was detected in 40.8% of the posts and was positively associated with positive sentiment (OR = 3.43), business profile (OR = 2.22), and treatment of dental caries (OR = 1.60). While the total interaction was only positively associated with misinformation (OR = 1.44), the overperforming score was associated with posts from the business profile (OR = 5.67), older publications (OR = 1.57), and positive sentiment (OR = 0.66). In conclusion, misinformation was the unique predictive factor of increased user interaction with dental caries-related posts on Facebook. However, it did not predict the performance of the diffusion of posts such as business profiles, older publications, and negative/neutral sentiment. Therefore, it is essential to promote the development of specific policies toward good quality information on social media, which includes the production of adequate materials, the increase of the critical sense of consuming health content, and information filtering mediated by digital solutions.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".