Zobraziť v EDS

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

Uložené v:
Podrobná bibliografia
Názov: Research on strategies for enhancing drug knowledge dissemination on Chinese social media WeChat public accounts based on text mining technology.
Autori: Yu, Xihui, Chen, Xiaotong, Yan, Xia, Wu, Xuejun, Zhang, Yizhi, Luo, Xiajiong, Ma, Weihao, Fu, Hongbo, Zhang, Yaofeng
Zdroj: Frontiers in Pharmacology; 2025, p1-13, 13p
Predmety: TEXT mining, SCIENTIFIC literacy, PATIENT compliance, MEDICATION safety, SCIENTIFIC communication, HEALTH literacy, PHARMACY education
Reviews & Products: WECHAT (Web resource)
Abstrakt: 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. [ABSTRACT FROM AUTHOR]
Copyright of Frontiers in Pharmacology is the property of Frontiers Media S.A. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Databáza: Complementary Index
FullText Text:
  Availability: 0
CustomLinks:
  – Url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=search&db=pmc&term=1663-9812[TA]+AND+1[PG]+AND+2025[PDAT]
    Name: FREE - PubMed Central (ISSN based link)
    Category: fullText
    Text: Full Text
    Icon: https://imageserver.ebscohost.com/NetImages/iconPdf.gif
    MouseOverText: Check this PubMed for the article full text.
  – Url: https://resolver.ebscohost.com/openurl?sid=EBSCO:edb&genre=article&issn=16639812&ISBN=&volume=&issue=&date=20250919&spage=1&pages=1-13&title=Frontiers in Pharmacology&atitle=Research%20on%20strategies%20for%20enhancing%20drug%20knowledge%20dissemination%20on%20Chinese%20social%20media%20WeChat%20public%20accounts%20based%20on%20text%20mining%20technology.&aulast=Yu%2C%20Xihui&id=DOI:10.3389/fphar.2025.1569863
    Name: Full Text Finder
    Category: fullText
    Text: Full Text Finder
    Icon: https://imageserver.ebscohost.com/branding/images/FTF.gif
    MouseOverText: Full Text Finder
  – Url: https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=EBSCO&SrcAuth=EBSCO&DestApp=WOS&ServiceName=TransferToWoS&DestLinkType=GeneralSearchSummary&Func=Links&author=Yu%20X
    Name: ISI
    Category: fullText
    Text: Nájsť tento článok vo Web of Science
    Icon: https://imagesrvr.epnet.com/ls/20docs.gif
    MouseOverText: Nájsť tento článok vo Web of Science
Header DbId: edb
DbLabel: Complementary Index
An: 188091902
RelevancyScore: 1060
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 1060.48962402344
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Research on strategies for enhancing drug knowledge dissemination on Chinese social media WeChat public accounts based on text mining technology.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Yu%2C+Xihui%22">Yu, Xihui</searchLink><br /><searchLink fieldCode="AR" term="%22Chen%2C+Xiaotong%22">Chen, Xiaotong</searchLink><br /><searchLink fieldCode="AR" term="%22Yan%2C+Xia%22">Yan, Xia</searchLink><br /><searchLink fieldCode="AR" term="%22Wu%2C+Xuejun%22">Wu, Xuejun</searchLink><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Yizhi%22">Zhang, Yizhi</searchLink><br /><searchLink fieldCode="AR" term="%22Luo%2C+Xiajiong%22">Luo, Xiajiong</searchLink><br /><searchLink fieldCode="AR" term="%22Ma%2C+Weihao%22">Ma, Weihao</searchLink><br /><searchLink fieldCode="AR" term="%22Fu%2C+Hongbo%22">Fu, Hongbo</searchLink><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Yaofeng%22">Zhang, Yaofeng</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: Frontiers in Pharmacology; 2025, p1-13, 13p
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22TEXT+mining%22">TEXT mining</searchLink><br /><searchLink fieldCode="DE" term="%22SCIENTIFIC+literacy%22">SCIENTIFIC literacy</searchLink><br /><searchLink fieldCode="DE" term="%22PATIENT+compliance%22">PATIENT compliance</searchLink><br /><searchLink fieldCode="DE" term="%22MEDICATION+safety%22">MEDICATION safety</searchLink><br /><searchLink fieldCode="DE" term="%22SCIENTIFIC+communication%22">SCIENTIFIC communication</searchLink><br /><searchLink fieldCode="DE" term="%22HEALTH+literacy%22">HEALTH literacy</searchLink><br /><searchLink fieldCode="DE" term="%22PHARMACY+education%22">PHARMACY education</searchLink>
– Name: SubjectProduct
  Label: Reviews & Products
  Group: Su
  Data: <searchLink fieldCode="PS" term="%22WECHAT+%28Web+resource%29%22">WECHAT (Web resource)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Frontiers in Pharmacology is the property of Frontiers Media S.A. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://erproxy.cvtisr.sk/sfx/access?url=https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edb&AN=188091902
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3389/fphar.2025.1569863
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 13
        StartPage: 1
    Subjects:
      – SubjectFull: WECHAT (Web resource)
        Type: general
      – SubjectFull: TEXT mining
        Type: general
      – SubjectFull: SCIENTIFIC literacy
        Type: general
      – SubjectFull: PATIENT compliance
        Type: general
      – SubjectFull: MEDICATION safety
        Type: general
      – SubjectFull: SCIENTIFIC communication
        Type: general
      – SubjectFull: HEALTH literacy
        Type: general
      – SubjectFull: PHARMACY education
        Type: general
    Titles:
      – TitleFull: Research on strategies for enhancing drug knowledge dissemination on Chinese social media WeChat public accounts based on text mining technology.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Yu, Xihui
      – PersonEntity:
          Name:
            NameFull: Chen, Xiaotong
      – PersonEntity:
          Name:
            NameFull: Yan, Xia
      – PersonEntity:
          Name:
            NameFull: Wu, Xuejun
      – PersonEntity:
          Name:
            NameFull: Zhang, Yizhi
      – PersonEntity:
          Name:
            NameFull: Luo, Xiajiong
      – PersonEntity:
          Name:
            NameFull: Ma, Weihao
      – PersonEntity:
          Name:
            NameFull: Fu, Hongbo
      – PersonEntity:
          Name:
            NameFull: Zhang, Yaofeng
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 19
              M: 09
              Text: 2025
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 16639812
          Titles:
            – TitleFull: Frontiers in Pharmacology
              Type: main
ResultId 1