Neural Topic Modeling to Understand Breast Cancer Peer-to-peer Online Information Seeking at Diagnosis

Topic modeling is a valuable method that enhances researchers’ ability to interpret information. Our research is the first to investigate neural topic modeling (NTM) methods for healthcare content analysis and topic discovery. We evaluated two NTM models to understand online information-seeking right after being diagnosed with breast cancer. Based on the achieved topic coherence and qualitative analysis on the topics discovered by each method, the NTM models work better than the traditional Latent Dirichlet Allocation (LDA) method when evaluated using the topic coherence score. The qualitative analysis shows that NTM models can find specific content-relevant topics, whereas LDA finds the general topics. Based on the results, we discover the main topics of online peerto-peer information seeking after the diagnosis of breast cancer, which included treatment and medication for different breast cancer types, symptom management after radiation treatments, insurance coverage of pathology, and family decision-making and support.