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Exploring fluoride-free content on Twitter: A topic modeling analysis

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 inv...

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Autores principales: Lotto, M, Zakir Hussain, I, Kaur, J, Butt, Z A, Cruvinel, T, Morita, P P
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10595767/
http://dx.doi.org/10.1093/eurpub/ckad160.601
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author Lotto, M
Zakir Hussain, I
Kaur, J
Butt, Z A
Cruvinel, T
Morita, P P
author_facet Lotto, M
Zakir Hussain, I
Kaur, J
Butt, Z A
Cruvinel, T
Morita, P P
author_sort Lotto, M
collection PubMed
description 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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spelling pubmed-105957672023-10-25 Exploring fluoride-free content on Twitter: A topic modeling analysis Lotto, M Zakir Hussain, I Kaur, J Butt, Z A Cruvinel, T Morita, P P Eur J Public Health Parallel Programme 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. Oxford University Press 2023-10-24 /pmc/articles/PMC10595767/ http://dx.doi.org/10.1093/eurpub/ckad160.601 Text en © The Author(s) 2023. Published by Oxford University Press on behalf of the European Public Health Association. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Parallel Programme
Lotto, M
Zakir Hussain, I
Kaur, J
Butt, Z A
Cruvinel, T
Morita, P P
Exploring fluoride-free content on Twitter: A topic modeling analysis
title Exploring fluoride-free content on Twitter: A topic modeling analysis
title_full Exploring fluoride-free content on Twitter: A topic modeling analysis
title_fullStr Exploring fluoride-free content on Twitter: A topic modeling analysis
title_full_unstemmed Exploring fluoride-free content on Twitter: A topic modeling analysis
title_short Exploring fluoride-free content on Twitter: A topic modeling analysis
title_sort exploring fluoride-free content on twitter: a topic modeling analysis
topic Parallel Programme
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10595767/
http://dx.doi.org/10.1093/eurpub/ckad160.601
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