On-line Health Counseling on Latent Tuberculosis Infection: An Analysis Using Text Mining and Machine Learning

The study’s objective is to explore the health information needs of latent tuberculosis patients and their communities by analyzing data from the online health counselling platform, Naver Jisik-iN. Initially, 3,261 questions related to ‘latent tuberculosis’ were collected. Following the removal of duplicates and irrelevant image information, the final dataset for analysis comprised 2,198 questions. Text pre-processing, Latent Dirichlet Allocation (LDA) topic modelling, and Long Short-Term Memory (LSTM)-based text summarization model were used. Manual categorization was added to supplement the unsupervised learning process. Seven topics were identified using LDA, from which five specific topics (‘side effects’, ‘treatment’, ‘army’, ‘interaction’, and ‘infectiousness’) were derived. Subsequently, manual classification was conducted based on these five topics. Manual summary and LSTM-based text summarization results were consistent. Numerous individuals sought information about the potential for curing latent tuberculosis and the risk of tuberculosis development. Moreover, questions related to the interpretation of test results and interactions with other substances were widespread. Concerning side effects, issues predominantly revolved around drug discontinuation due to skin problems and elevated liver function tests. The findings reveal the prevalent concerns and inquiries of society regarding latent tuberculosis. The identified topics offer valuable insights into the key aspects of interest related to this condition.