Analysis of Fluoride-Free Content on Twitter: Topic Modeling Study
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 inf...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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JMIR Publications
2023-06-01
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| Series: | Journal of Medical Internet Research |
| Online Access: | https://www.jmir.org/2023/1/e44586 |
| _version_ | 1797733990550994944 |
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| author | Matheus Lotto Irfhana Zakir Hussain Jasleen Kaur Zahid Ahmad Butt Thiago Cruvinel Plinio P Morita |
| author_facet | Matheus Lotto Irfhana Zakir Hussain Jasleen Kaur Zahid Ahmad Butt Thiago Cruvinel Plinio P Morita |
| author_sort | Matheus Lotto |
| collection | DOAJ |
| description |
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. |
| first_indexed | 2024-03-12T12:37:44Z |
| format | Article |
| id | doaj.art-cdf8975c66664c74ada51c30f55d1f65 |
| institution | Directory Open Access Journal |
| issn | 1438-8871 |
| language | English |
| last_indexed | 2024-03-12T12:37:44Z |
| publishDate | 2023-06-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | Journal of Medical Internet Research |
| spelling | doaj.art-cdf8975c66664c74ada51c30f55d1f652023-08-29T00:05:11ZengJMIR PublicationsJournal of Medical Internet Research1438-88712023-06-0125e4458610.2196/44586Analysis of Fluoride-Free Content on Twitter: Topic Modeling StudyMatheus Lottohttps://orcid.org/0000-0002-0121-4006Irfhana Zakir Hussainhttps://orcid.org/0000-0002-0629-9352Jasleen Kaurhttps://orcid.org/0000-0002-7335-4374Zahid Ahmad Butthttps://orcid.org/0000-0002-2486-4781Thiago Cruvinelhttps://orcid.org/0000-0001-7095-908XPlinio P Moritahttps://orcid.org/0000-0001-9515-6478 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.https://www.jmir.org/2023/1/e44586 |
| spellingShingle | Matheus Lotto Irfhana Zakir Hussain Jasleen Kaur Zahid Ahmad Butt Thiago Cruvinel Plinio P Morita Analysis of Fluoride-Free Content on Twitter: Topic Modeling Study Journal of Medical Internet Research |
| title | Analysis of Fluoride-Free Content on Twitter: Topic Modeling Study |
| title_full | Analysis of Fluoride-Free Content on Twitter: Topic Modeling Study |
| title_fullStr | Analysis of Fluoride-Free Content on Twitter: Topic Modeling Study |
| title_full_unstemmed | Analysis of Fluoride-Free Content on Twitter: Topic Modeling Study |
| title_short | Analysis of Fluoride-Free Content on Twitter: Topic Modeling Study |
| title_sort | analysis of fluoride free content on twitter topic modeling study |
| url | https://www.jmir.org/2023/1/e44586 |
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