Network-based modeling of emotional expressions for multiple cancers via a linguistic analysis of an online health community

The diagnosis and treatment of cancer can evoke a variety of adverse emotions. Online health communities (OHCs) provide a safe platform for cancer patients and those closely related to express emotions without fear of judgement or stigma. In the literature, linguistic analysis of OHCs is usually limited to a single disease and based on methods with various technical limitations. In this article we analyze posts from September 2003 to September 2022 on eight cancers that are publicly available at the American Cancer Society's Cancer Survivors Network (CSN). We propose a novel network analysis technique based on low-rank matrices. The proposed approach decomposes the emotional expression semantic networks into an across-cancer time-independent component (which describes the "baseline" that is shared by multiple cancers), a cancer-specific time-independent component (which describes cancer-specific properties), and an across-cancer time-dependent component (which accommodates temporal effects on multiple cancer communities). For the second and third components, respectively, we consider a novel clustering structure and a change point structure. A penalization approach is developed, and its theoretical and computational properties are carefully established. The analysis of the CSN data leads to sensible networks and deeper insights into emotions for cancer overall and specific cancer types.