Evidence map›Paper›PMID 37338975›Full record

ArticleJournal of medical Internet research2023

Analysis of Fluoride-Free Content on Twitter: Topic Modeling Study.

Matheus Lotto, Irfhana Zakir Hussain, Jasleen Kaur, Zahid Ahmad Butt, Thiago Cruvinel, Plinio P Morita

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–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

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
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  4. Review
  5. Article
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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

6 authors.

Matheus LottoDepartment of Pediatric Dentistry, Orthodontics and Public Health, Bauru School of Dentistry, University of São Paulo, Bauru, Brazil.ORCID 0000-0002-0121-4006
Irfhana Zakir HussainSchool of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada.ORCID 0000-0002-0629-9352
Jasleen KaurSchool of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada.ORCID 0000-0002-7335-4374
Zahid Ahmad ButtSchool of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada.ORCID 0000-0002-2486-4781
Thiago CruvinelDepartment of Pediatric Dentistry, Orthodontics and Public Health, Bauru School of Dentistry, University of São Paulo, Bauru, Brazil.ORCID 0000-0001-7095-908X
Plinio P MoritaSchool of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada.ORCID 0000-0001-9515-6478

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAlthough social media has the potential to spread misinformation, it can also be a valuable tool for elucidating the social factors that contribute to the onset of negative beliefs. As a result, data mining has become a widely used technique in infodemiology and infoveillance research to combat misinformation effects. On the other hand, there is a lack of studies that specifically aim to investigate misinformation about fluoride on Twitter. Web-based individual concerns on the side effects of fluoridated oral care products and tap water stimulate the emergence and propagation of convictions that boost antifluoridation activism. In this sense, a previous content analysis-driven study demonstrated that the term fluoride-free was frequently associated with antifluoridation interests.

objectiveThis study aimed to analyze "fluoride-free" tweets regarding their topics and frequency of publication over time.

methodsA total of 21,169 tweets published in English between May 2016 and May 2022 that included the keyword "fluoride-free" were retrieved by the Twitter application programming interface. Latent Dirichlet allocation (LDA) topic modeling was applied to identify the salient terms and topics. The similarity between topics was calculated through an intertopic distance map. Moreover, an investigator manually assessed a sample of tweets depicting each of the most representative word groups that determined specific issues. Lastly, additional data visualization was performed regarding the total count of each topic of fluoride-free record and its relevance over time, using Elastic Stack software.

resultsWe identified 3 issues by applying the LDA topic modeling: "healthy lifestyle" (topic 1), "consumption of natural/organic oral care products" (topic 2), and "recommendations for using fluoride-free products/measures" (topic 3). Topic 1 was related to users' concerns about leading a healthier lifestyle and the potential impacts of fluoride consumption, including its hypothetical toxicity. Complementarily, topic 2 was associated with users' personal interests and perceptions of consuming natural and organic fluoride-free oral care products, whereas topic 3 was linked to users' recommendations for using fluoride-free products (eg, switching from fluoridated toothpaste to fluoride-free alternatives) and measures (eg, consuming unfluoridated bottled water instead of fluoridated tap water), comprising the propaganda of dental products. Additionally, the count of tweets on fluoride-free content decreased between 2016 and 2019 but increased again from 2020 onward.

conclusionsPublic concerns toward a healthy lifestyle, including the adoption of natural and organic cosmetics, seem to be the main motivation of the recent increase of "fluoride-free" tweets, which can be boosted by the propagation of fluoride falsehoods on the web. Therefore, public health authorities, health professionals, and legislators should be aware of the spread of fluoride-free content on social media to create and implement strategies against their potential health damage for the population.

Indexed as

COVID-19Social MediaCommunicationConsumer Health InformationData MiningFluoridesHealthy LifestyleHumansInfodemicInfodemiologyFluoridesfluoridehealth informationhealthy lifestylehygieneinfodemiologyinfoveillancemisinformationoral caresocial mediaTwitter

Identifiers

PMID37338975
PMCPMC10337345

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.