A0208
Title: Modeling emotional expressions for multiple cancers via a linguistic analysis of an online health community
Authors: Steven Ma - Yale University (United States) [presenting]
Abstract: 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 judgment or stigma. In the literature, linguistic analysis of OHCs is usually limited to a single disease and based on methods with various technical limitations. Posts from September 2010 to September 2022 are analyzed on nine publicly available cancers at the American Cancer Society's Cancer Survivors Network (CSN). A novel network analysis technique is proposed based on a latent space model. 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). A novel clustering structure and a change point structure are considered for the second and third components, respectively. A penalization approach is proposed, and its theoretical and computational properties are carefully examined. The analysis of the CSN data leads to sensible networks and deeper insights into emotions for cancer overall and specific cancer types.
