Analysis of Topics and Trends in Scientific Productions in the Field of Health Information Literacy Using Text Mining Techniques

Document Type : Research َ Article

Authors

1 Shahid Sadoughi University of Medical Sciences, Yazd, Iran

2 Assistant Professor, University of Isfahan, Isfahan, Iran

3 Iran Public Libraries Foundation, Yazd, Iran

Abstract

Purpose: Diverse research in information literacy necessitates analyzing the topics of these studies to gain a clear and comprehensive understanding of this area. The current research aims to apply topic modeling to published scientific productions related to health information literacy using the PubMed database.
Method: This study employed a quantitative approach with an applied focus, utilizing text-mining techniques. Scientific publications in information literacy were extracted from the PubMed database using the MeSH term "information literacy" [Majr] without any time constraints. A search on August 5, 2024, yielded 8407 records from 1519 journals and books. Subsequently, the abstracts and titles of the articles were saved in text format and then converted into a structured Excel format for analysis. After removing null records, 7608 records with abstracts were used for analysis. The process involved tokenization, removal of punctuation and stop words, stemming, and conversion of text data into numerical vectors to apply machine learning techniques. Finally, topic modeling was performed using the Latent Dirichlet Allocation (LDA) algorithm. After data cleaning, the abstracts and titles of these articles were analyzed and topic modeled using the Pandas, PyLDAvis, sklearn, PyLDAvis, numpy, Setuptools, NLTK, Gensim, Wordcloud, and Seaborn libraries.
Findings: Analysis of the retrieved articles using the TF-IDF algorithm revealed that the terms "patients", "mental", "mental health", "information", and "care" had the highest term frequency-inverse document frequency weights. Using Latent Dirichlet Allocation, seven thematic clusters were identified, including "Online Health Information Seeking and Digital Health Literacy"; "Impact of Health Literacy on Decision-Making"; "Readability of Patient Education Materials"; "Health Literacy During the COVID-19 Pandemic";"Mental Health Literacy"; "Oral Health Literacy"; and "Communication in Healthcare."
The analysis of scientific articles in the field of health information literacy revealed that the topics “Impact of Health Literacy on Decision-Making” and “Online Health Information Seeking and Digital Health Literacy” experienced the highest growth over time. In contrast, the topic “Health Literacy During the COVID-19 Pandemic” showed a declining trend. Additionally, the distribution of publications showed that “Mental Health Literacy” accounted for the largest share at 22%, while “Health Literacy During the COVID-19 Pandemic” represented the smallest share, making up only 2% of the total publications in the field of health information literacy.
Conclusion: The extracted thematic clusters from the scientific productions on information literacy demonstrated good coherence and strong thematic relationships; therefore, this research can significantly contribute to researchers in improving scientific production in the field of health information literacy.

Keywords

Main Subjects

References

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History

  • Receive Date: 06 December 2024
  • Revise Date: 26 January 2025
  • Accept Date: 28 February 2025
  • Publish Date: 22 December 2025

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