Analysis of Supportive Communication Based on TVDM Social Network Information Propagation Dynamics Model: A Case Study of HIV Online Support Groups

Abstract

Utilizing digital health technologies for mental health and medical knowledge during HIV/ADS prevention becomes an emphasis proposed by the Joint United Nations Programme on HIV/AIDS (UNAIDS), and supportive communication within HIV online support groups can be its cutting point. Although previous research has delved into the social network structure of HIV online support groups and the content effects on support information, there remains a dearth of exploration about support information propagation within HIV online support groups. This research collects posts (n=1401), comments (n=2402), and reposts (n=1415) from December 2nd, 2016 to August 11th, 2023 within HIV online support groups on Sina Weibo and classes them into emotional and informational support information by SVM model, aiming at quantifying support information propagation probability based on the Time-Varying Damping Motion (TVDM) social network information propagation dynamics model, clustering each type of support information’s propagation patterns based on T-SC time series clustering algorithm, and exploring factors of propagation probability with principal component regression. It is found that emotional support information tends to hold a higher propagation probability than informational one, while informational support information likely propagates multiple times. Furthermore, besides network-structural positive effects, the time interval between user posting and user encountering information is negatively associated with information propagation probability. This research provides insight into HIV digital prevention and caring for HIV patients’ mental well-being.