ArticleJournal of public health dentistry2025
Mapping the "X" Debate: Water Fluoridation Sentiment Analysis With Advanced Machine Learning.
Article in Journal of public health dentistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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.
Who cites it
3 citing papers in PubMed.
- Party identification and public attitudes toward raw milk, water fluoridation, and acetaminophen use.Health affairs scholar · 2026Article
- Review
- Mapping the "X" Debate: Water Fluoridation Sentiment Analysis With Advanced Machine Learning.Journal of public health dentistry · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
objectivesThis study aimed to examine public sentiment regarding community water fluoridation (CWF) using data from "X" (formerly Twitter) over the past decade. The goal was to understand public opinion trends and identify opportunities for targeted public health communication.
methodsWe conducted a sentiment analysis utilizing a natural language processing technique. Specifically, we applied the Sentiment Intensity Analyzer tool to classify tweets related to CWF into negative, positive, or neutral categories. Additionally, a word co-occurrence network analysis was performed to explore key discussion themes. We also compared machine learning models to assess their accuracy in classifying tweet sentiments.
resultsAnalysis of the tweets revealed a balanced distribution of sentiments: 37.4% negative, 34.4% positive, and 28.2% neutral. Peaks in public engagement occurred between 2015 and 2016, with a subsequent decline after 2018. Sentiment spikes were often associated with significant events, including policy changes and media coverage. The word co-occurrence network highlighted recurring themes related to safety and dental health. Among the machine learning models evaluated, Logistic Regression demonstrated the highest accuracy in sentiment classification.
conclusionsOur findings highlight the polarized nature of public sentiment toward CWF and the temporal fluctuations in public engagement. These insights can inform public health policymakers in developing timely, targeted communication strategies. Specifically, efforts to engage neutral audiences through transparent messaging and counter misinformation during key periods may strengthen public trust in CWF. The application of machine learning in this analysis underscores its value in enhancing real-time monitoring and supporting evidence-based public health strategies.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.