Evidence map›Paper›PMID 40948145›Full record

ArticleJDR clinical and translational research2026

Analysis of Water Fluoridation Debates on Meta Platforms Using Advanced Machine Learning Approaches.

N Torwane, R Lalloo, D Ha, L Do

Abstract read
In one paragraph

Article in JDR clinical and translational research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

N TorwaneThe University of Queensland, School of Dentistry, Brisbane, QLD, Australia.ORCID 0000-0002-5938-1077
R LallooThe University of Queensland, School of Dentistry, Brisbane, QLD, Australia.ORCID 0000-0001-5822-1269
D HaThe University of Queensland, School of Dentistry, Brisbane, QLD, Australia.
L DoThe University of Queensland, School of Dentistry, Brisbane, QLD, Australia.ORCID 0000-0003-3684-9949

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Social media platforms, such as Facebook and Instagram, play a pivotal role in shaping public opinion on health interventions, including community water fluoridation (CWF). Despite its recognition as a safe and effective public health measure, CWF remains a polarizing topic, with misinformation on these platforms contributing to public mistrust. This study collected 109,117 Facebook and Instagram posts from 2014 to 2023 to examine public sentiment surrounding CWF. The analysis revealed a mix of opinions, with 42.1% positive, 39.1% negative, and 18.8% neutral sentiments. Trends highlighted a surge in negative sentiment during 2017-2019, likely influenced by misinformation and significant public events, while positive sentiment has gradually regained ground in recent years. Key themes included health benefits, safety concerns, and government trust, with positive discussions emphasizing CWF's role in public health and negative discussions focusing on risks and chemical exposure. The study employed advanced sentiment analysis models to highlight the importance of monitoring public discourse and addressing misinformation, thereby promoting trust and support for evidence-based health policies, such as CWF. These findings offer digital data-driven insights for public health communication strategies, enhancing community understanding and acceptance of vital health interventions.Knowledge Transfer Statement:The findings of this study can inform public health agencies and policymakers about public concerns and support for water fluoridation. By identifying sentiment trends and misinformation on social media, communication strategies can be tailored to improve public understanding and acceptance of fluoridation as a preventive health measure.

Indexed as

FluoridationMachine LearningPublic OpinionSocial MediaHumansPublic HealthTrusthealth communicationnatural language processingpreventive dentistrypublic opinionSentiment Analysissocial media

Identifiers

PMID40948145
PMCPMC13232570

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.