Abstract
Online health communities (OHCs) provide free, open, and well-resourced platforms for patients, family members, and others to discuss illnesses, express feelings, and connect with others. Linguistic analysis of OHC posts can assist in better understanding disease conditions as well as monitoring the emotional and mental status of patients and those who are closely related. Many existing OHC linguistic analyses are limited by focusing on individual words. There are a handful of cooccurrence network analyses, which have multiple methodological limitations. In this article we analyze posts that are publicly available at the LUNGevity Foundation's Lung Cancer Support Community (LCSC). The analyzed data contains 21,028 posts published between April 2018 and February 2022. For word cooccurrence network analysis, we develop a two-part latent space model, which advances from the existing ones by accommodating network weights. Further, we consider the scenario where there are change points in time, networks remain the same between two change points but differ on the two sides of a change point, and the number and locations of change points are unknown. A penalized fusion approach is developed to data-dependently determine change points and estimate networks. In data analysis multiple change points are identified, which reflect significant changes in lung cancer patients' and their close affiliates' emotional/mental status and mostly align with the changes in COVID-19. The obtained network structures and other findings are also sensible.