ArticleBMC oral health2024
Content analysis of fluoride-related posts on Instagram.
Article in BMC oral health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Review
- Information Quality, Content Scope, and Audience Engagement of Popular Turkish Instagram Posts on Tooth Whitening: A Cross-Sectional Content Analysis.Healthcare (Basel, Switzerland) · 2026Article
- Pro- and Antifluoride Use Messages on YouTube in Japan: Content Analysis.JMIR formative research · 2025Article
- Exploring artificial intelligence chatbots in pediatric fluoride education: a cross-sectional study.Scientific reports · 2025Article
- Root Canals and Conspiracies: A Social Semiotic Analysis of Digital Narratives on Social Media and the Promotion of Misinformation.Dentistry journal · 2025Article
- A Cross-Sectional Analysis of Oil Pulling on YouTube Shorts.Dentistry journal · 2025Article
- Re: Prevalence and Associated Factors of Fluoride Varnish Application.Journal of the American Board of Family Medicine : JABFMArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe Internet and social media have become many people's primary sources of health information. Instagram is one of the most popular social media platforms, and users spend a significant amount of time daily on the platform. Therefore, analyzing oral health-related information shared online can help to identify the requirements of specific groups, assisting in the planning and execution of educational public health actions. As a result, this study was carried out to conduct a qualitative evaluation of fluoride-related content on Instagram in 2022-2023.
methodsUsing hashtags associated with fluoride, a search was conducted on the Instagram application in the Persian language. All fluoride-containing posts were saved, and data was collected thrice at two-month intervals. MAXQDA software version 2020 was used to categorize the collected data content based on type, quality, and quantity.
resultsThe qualitative analysis included 1565 (9.45%) posts with fluoride-related content out of 16,475 total posts. After removing the duplicates, 400 posts remained, among which 282 posts (70.5%) and 118 posts (29.5%) were pro- and anti-fluoride, respectively. After qualitative analysis, 270 codes were examined and categorized into three main themes and eight subthemes: "fluoride sources," "The advantages and disadvantages of fluoride," and "Invalid beliefs about fluoride."
conclusionThe frequency of pro-fluoride use was higher than anti-fluoride posts. However, due to the high prevalence of posts containing false information on fluoride toxicity for health, ineffectiveness on caries prevention, and conspiracy theories, there is a need to improve the quality of information on fluoride by providing scientific and unbiased explanations by professionals online or during dental visits and supervising social media content.
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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.