Evidence map›Paper›PMID 42314277›Full record

ArticleInternational dental journal2026

Mapping Indonesian Public Discourse on Oral Health: A Content Analysis of Social Media Using Latent Dirichlet Allocation Topic Modelling.

Rosa Amalia, Iwan Dewanto, Anggit Wirasto, Leny Pratiwi Arie Sandy

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Article in International dental journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Rosa AmaliaDepartment of Preventive and Community Dentistry, Faculty of Dentistry, Universitas Gadjah Mada, Yogyakarta, Indonesia. Electronic address: rosa_amalia@ugm.ac.id.
Iwan DewantoDepartment of Dental Public Health, Faculty of Dentistry, Universitas Muhammadiyah, Yogyakarta, Indonesia.
Anggit WirastoFaculty of Science and Technology, Universitas Harapan Bangsa, Purwokerto, Indonesia.
Leny Pratiwi Arie SandyDepartment of Preventive and Community Dentistry, Faculty of Dentistry, Universitas Gadjah Mada, Yogyakarta, Indonesia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe potential of social media that offers public health insights in middle-income countries remains underexplored. This study examines patterns of oral health discourse on Indonesian social media and highlighting such insights into promotive and preventive oral health strategies.

methodsA discourse and content analysis was conducted on user-generated posts from Quora and Threads over an 18-month period (July 1, 2023-January 31, 2025). Posts were retrieved through automated data extraction and computational text retrieval based on keyword searches and coded into 19 predefined categories. Latent Dirichlet allocation (LDA) was applied to identify underlying thematic structures. Associations between platforms and discussion topics were tested using chi-square and Monte Carlo simulations. Comparative analyses examined platform-specific trends, thematic frequencies and inquiry types, with findings cross-referenced against national survey data.

resultsA total of 858 posts (541 from Quora, 317 from Threads) were analysed. The most frequently discussed issues were mouth ulcers and toothache. LDA revealed 10 dominant themes, including oral hygiene practices, children's dental visits, gum swelling and postsurgical recovery. Quora discussions were dominated by symptom-focused inquiries, whereas Threads contained more personal and emotionally expressive narratives. Statistical testing confirmed a significant association between platform type and topics discussed (P < .001).

conclusionThis study demonstrated the utility of automated data extraction and LDA-based thematic modelling in uncovering community-level oral health patterns. Social media provides an immediate context-relevant perspective for monitoring oral health needs, supporting targeted communication strategies and responsive public health interventions. CLINICAL RELEVANCE: This study introduces a complementary approach to conventional epidemiological monitoring. The findings can guide policymakers and practitioners in developing targeted preventive initiatives and public engagement strategies for improved oral health outcomes.

Indexed as

Oral HealthSocial MediaHumansIndonesiaPublic HealthAutomated data extractionLatent Dirichlet allocation (LDA)Oral health surveillanceSocial media analysis

Identifiers

PMID42314277
PMCPMC13311919

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LicenceCC BY-NC-ND
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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.