From data to insights - Machine learning in thematic analysis of complex health conditions on social media

Aug 4, 2026

Social media data enables examination of public perspectives on health conditions, interventions, and policies, but qualitative analysis is resource intensive and limits timely use. This research investigates whether machine learning can augment thematic analysis of complex health issue data from social media. A deductive thematic analysis is first conducted on 7,177 YouTube comments about postpartum depression, followed by machine-assisted analysis using five NLP classification models with and without class balancing (class weights and SMOTE). Results suggest supervised ML alone is not optimal, while ML-NLP integrated with thematic analysis improves efficiency by filtering ineligible data. The study supports using social media for meaningful public health insights and recommends future work on AI tools, larger datasets, and unsupervised approaches.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages18

Reading Time45 min

AccessFree to download

Introduction

Data from social media and research benefits

Research aim and approach

Methods

Deductive thematic analysis of YouTube comments

Machine-assisted analysis with NLP classification models

Class balancing strategies

Results and discussion

Suitability of supervised machine learning for complex health data

Impact of ML-NLP integration on efficiency and insight quality

Conclusion and future work

From data to insights: Machine learning in thematic analysis of complex health conditions on social media Anila Virani Ahsan Mollani Piper Jackson Thompson Rivers University, Canada Knowledge Management & E-Learning: An International Journal (KM&EL) ISSN 2073-7904 Recommended citation: Virani, A., Mollani, A., & Jackson, P. (2025) . From data to insights: Machine learning in thematic analysis of complex health conditions on social media. Knowledge Management & E-Learning, 17(4), 666–682. [https://doi.org/10.34105/j.kmel.2025.17.031](https://doi.org/10.34105/j.kmel.2025.17.031) From data to insights: Machine learning in thematic analysis of complex health conditions on social media Anila Virani* School of Nursing Thompson Rivers University, Canada E-mail: [avirani@tru.ca](avirani@tru.ca) Ahsan Mollani Faculty of Science Thompson Rivers University, Canada [E-mail: mollania22@mytru.ca](E-mail: mollania22@mytru.ca) Piper Jackson Faculty of Science Thompson Rivers University, Canada [E-mail: pjackson@tru.ca](E-mail: pjackson@tru.ca) *Corresponding author Abstract: Social media data has the potential to enable the exploration of public perspectives on health conditions, interventions and policies. However, theresource-intensive nature of qualitative analysis creates a barrier to the timely utilization of social media data. Artificial intelligence can provide innovative ways to reduce the burden by augmenting the data analysis process for researchers. Therefore, the purpose of this research is to explore the feasibility of using Machine Learning (ML) in augmenting thematic analysis of complex health issues data available on social media. First, we performed a humandetermined deductive thematic analysis of the 7,177 comments posted by YouTube video viewers on postpartum depression. Then we used the same data to perform machine-assisted analysis using five Natural Language Processing (NLP) classification models with and without class balancing techniques (class weight balance and SMOTE) to balance the unequal number of comments across themes. Our analysis suggested that supervised machine learning may not be optimal for analyzing complex health datasets. However, integrating ML-NLP techniques with thematic analysis can effectively filter out ineligible data, thereby enhancing the efficiency of traditional thematic analysis processes. This integration allows researchers to focus on valuable data, producing meaningful insights and comprehensive analysis while saving time otherwise spent sifting through a large amount of ineligible data. Social media data can offer significant public health insights, and to enhance the use of such data, future research should focus on developing artificial intelligence tools to improve the efficiency of thematic analysis using larger datasets and unsupervised machine learning models for analyzing complex health issues. Keywords: Machine learning; Natural language processing; Thematic analysis; Social media data; Complex health issues Biographical notes: Dr. Anila Virani serves as an Assistant Professor at the School of Nursing, Thompson Rivers University, Canada. With over 25 years of extensive experience in healthcare, academia, and research, Dr. Virani ’s scholarly journey encompasses significant national and international contributions. Her doctoral research at the University of Calgary and subsequent postdoctoral project at the University of British Columbia focused on the integration of informatics solutions within healthcare systems. Dr. Virani ’s research program is dedicated to investigating the intricate dynamics between individuals and technology in healthcare and educational settings, with a particular emphasis on technological innovation. Ahsan Mollani holds a Master ’s degree in Data Science from Thompson Rivers University. His research interests include technology-enhanced learning and the application of machine learning and natural language pro

FAQs

What problem does the study address about using social media data for health research?

Qualitative analysis of social media content is resource intensive, which slows down and limits timely utilization of social media data for studying health conditions and public responses.

How was thematic analysis performed in this research?

The study conducted a human-determined deductive thematic analysis on 7,177 YouTube comments from viewers about postpartum depression.

What machine-learning approach was tested to support thematic analysis?

Five NLP classification models were used with and without class balancing techniques, including class weight balancing and SMOTE, to manage unequal comment counts across themes.

Aug 4, 2026

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