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Research on strategies for enhancing drug knowledge dissemination on Chinese social media WeChat public accounts based on text mining technology.

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Title: Research on strategies for enhancing drug knowledge dissemination on Chinese social media WeChat public accounts based on text mining technology.
Authors: Yu X; Department of Pharmacy, The Second Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China., Chen X; Department of Pharmacy, The Second Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China., Yan X; Department of Pharmacy, The Second Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China., Wu X; Department of Pharmacy, The Second Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China., Zhang Y; Department of Pharmacy, The Second Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China., Luo X; Department of Pharmacy, The Second Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China., Ma W; Department of Pharmacy, The Second Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China., Fu H; Department of Pharmacy, The Second Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China., Zhang Y; Department of Pharmacy, The Second Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China.
Source: Frontiers in pharmacology [Front Pharmacol] 2025 Aug 26; Vol. 16, pp. 1569863. Date of Electronic Publication: 2025 Aug 26 (Print Publication: 2025).
Publication Type: Journal Article
Language: English
Journal Info: Publisher: Frontiers Media] Country of Publication: Switzerland NLM ID: 101548923 Publication Model: eCollection Cited Medium: Print ISSN: 1663-9812 (Print) Linking ISSN: 16639812 NLM ISO Abbreviation: Front Pharmacol Subsets: PubMed not MEDLINE
Imprint Name(s): Original Publication: [Lausanne : Frontiers Media]
Abstract: Objective: Health science popularization is an important means to improve public health literacy, promote healthy lifestyles, prevent diseases and respond to health crises, which is of great significance for improving the overall health of the people. Strengthening the medication education of patients is also one of the key factors to improve patients' medication adherence. In order to strengthen the dissemination of pharmaceutical popular science articles and give full play to the value of pharmaceutical popular science, this study takes WeChat public account as the research platform to explore effective strategies to improve pageviews of science popularization. It provides references for science popularization workers, so that science popularization can play a better role in improving the public's knowledge of medication safety.
Methods: Taking the well-known pharmaceutical science popularization WeChat account "PSM Medicine Shield Public Welfare" as an example, we combined the Term Frequency-Inverse Document Frequency (TF-IDF) algorithm and VOSviewer visualization analysis technology to construct a hot topic analysis model for pharmaceutical science popularization articles, and analyzed the common rules and characteristics of successful hot articles. Latent Dirichlet Allocation (LDA) and The Bidirectional Encoder Representations from Transformers Topic (BERTopic) model were used to realize the construction of the topic model.
Results: The model selected the top 20% of popularization articles with the greatest reading volume between 2015 and 2023 as the database for text mining. The clustering results indicated that the public was interested in these five types of pharmaceutical science popularization themes: drug dosage, drug side effects, children's infections, the efficacy of traditional Chinese medicine and Chinese patent medicines, and the usage methods of different drug administration routes. The public's interest in topics changed from drug side effects to practical drug usage issues, as seen by the keyword time series graph.
Conclusion: Pharmaceutical professionals may more effectively discover hot themes in the industry by combining the TF-IDF algorithm with VOSviewer visualization analysis and LDA and BERTopic in the text mining. This improves the readability of popularization articles and the impact of WeChat accounts, which may improve medication adherence and raise public awareness of medication usage.
(Copyright © 2025 Yu, Chen, Yan, Wu, Zhang, Luo, Ma, Fu and Zhang.)
Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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Contributed Indexing: Keywords: VOSviewer; WeChat; medication adherence; natural language processing; pharmic science popularization; term frequency-inverse document frequency (TF-IDF); topic modelling; visualization analysis
Entry Date(s): Date Created: 20250922 Date Completed: 20250922 Latest Revision: 20250924
Update Code: 20250924
PubMed Central ID: PMC12446869
DOI: 10.3389/fphar.2025.1569863
PMID: 40978476
Database: MEDLINE
Description
Abstract:Objective: Health science popularization is an important means to improve public health literacy, promote healthy lifestyles, prevent diseases and respond to health crises, which is of great significance for improving the overall health of the people. Strengthening the medication education of patients is also one of the key factors to improve patients' medication adherence. In order to strengthen the dissemination of pharmaceutical popular science articles and give full play to the value of pharmaceutical popular science, this study takes WeChat public account as the research platform to explore effective strategies to improve pageviews of science popularization. It provides references for science popularization workers, so that science popularization can play a better role in improving the public's knowledge of medication safety.<br />Methods: Taking the well-known pharmaceutical science popularization WeChat account "PSM Medicine Shield Public Welfare" as an example, we combined the Term Frequency-Inverse Document Frequency (TF-IDF) algorithm and VOSviewer visualization analysis technology to construct a hot topic analysis model for pharmaceutical science popularization articles, and analyzed the common rules and characteristics of successful hot articles. Latent Dirichlet Allocation (LDA) and The Bidirectional Encoder Representations from Transformers Topic (BERTopic) model were used to realize the construction of the topic model.<br />Results: The model selected the top 20% of popularization articles with the greatest reading volume between 2015 and 2023 as the database for text mining. The clustering results indicated that the public was interested in these five types of pharmaceutical science popularization themes: drug dosage, drug side effects, children's infections, the efficacy of traditional Chinese medicine and Chinese patent medicines, and the usage methods of different drug administration routes. The public's interest in topics changed from drug side effects to practical drug usage issues, as seen by the keyword time series graph.<br />Conclusion: Pharmaceutical professionals may more effectively discover hot themes in the industry by combining the TF-IDF algorithm with VOSviewer visualization analysis and LDA and BERTopic in the text mining. This improves the readability of popularization articles and the impact of WeChat accounts, which may improve medication adherence and raise public awareness of medication usage.<br /> (Copyright © 2025 Yu, Chen, Yan, Wu, Zhang, Luo, Ma, Fu and Zhang.)
ISSN:1663-9812
DOI:10.3389/fphar.2025.1569863