What Can Social Media Tell Us About Patient Symptoms: A Text-Mining Approach to Online Ovarian Cancer Forum.

Abstract

Ovarian cancer (OvCa) patients suffer from symptoms that severely affect quality of life. To optimally manage these symptoms, their symptom experiences must be better understood. Social media have emerged as a data source to understand these experiences. The objective of this study was to use topic modeling (ie, latent Dirichlet allocation [LDA]) to understand the symptom experience of OvCa patients through analysis of online forum posts from OvCa patients and their caregivers. Ovarian cancer patient/caregiver posts (n = 50 626) were collected from an online OvCa forum. We developed a symptom dictionary to identify symptoms described therein, selected the top 5 most frequently discussed symptoms, extracted posts that mentioned at least one of those symptoms, and conducted LDA on those extracted posts. Pain, nausea, anxiety, fatigue, and skin rash were the top 5 most frequently discussed symptoms (n = 4536, 1296, 967, 878, and 657, respectively). Using LDA, we identified 11 topic categories, which differed across symptoms. For example, chemotherapy-related adverse effects likely reflected fatigue, nausea, and rash; social and spiritual support likely reflected anxiety; and diagnosis and treatment often reflected pain. The frequency of a symptom discussed on a social media platform may not include all symptom experience and their severity. Indeed, users, who are experiencing different symptoms, mentioned different topics on the forum. Subsequent studies should consider the influence of additional factors (eg, cancer stage) from discussions. Social media have the potential to prioritize and answer the questions about clinical care that are frequently asked by cancer patients and their caregivers.