Exploring fluoride-free content on Twitter: A topic modeling analysis
Bibliographic record
Abstract
Abstract Social media discussions about the hypothetical side effects of fluoride-containing products have contributed to forming and strengthening health beliefs underpinning the anti-fluoridation movement. Given the importance of content analysis in mitigating online misinformation, this study aimed to investigate fluoride-free content on Twitter. Firstly, 21,169 tweets published in English between May 2016 and May 2022 concerning the keyword ‘fluoride-free’ were collected using the Twitter API. After the data preprocessing, Latent Dirichlet Allocation (LDA) topic modeling was conducted to identify the salient terms and topics inside the collected tweets. Then, the similarity between topics was calculated by an intertopic distance map. Furthermore, an investigator manually reviewed a sample of tweets that featured the most representative word groups associated with particular topics to determine the main issues. Finally, additional data visualization was performed using Elastic Stack software to analyze the total count and relevance of each topic related to fluoride-free records over time. The topic modeling analysis demonstrated a significant distance between the topics from a coherence score of 0.542, confirming the identification of three different issues: ‘healthy lifestyle’ (topic 1), ‘consumption of natural/organic oral care products’ (topic 2), and ‘recommendations for using fluoride-free products/measures’ (topic 3). Moreover, the number of fluoride-free publications decreased between 2016 and 2019 but increased again in 2020. Thus, recent increases in fluoride-free tweets appear driven by public concerns about adopting a healthy lifestyle, including the consumption of natural and organic products. Therefore, public health authorities, health professionals, and legislators must acknowledge the dissemination of fluoride-free content on social media to implement effective strategies to counteract its potential harm to the population's oral health. Key messages • People are concerned about the hypothetical side effects of fluoride-containing measures and products. • The consumption of fluoride-free content on social media is associated with adopting a healthy lifestyle.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".