Online health communities (OHCs) constitute a useful source of information and social support for patients. American Cancer Society’s Cancer Survivor Network (CSN), a 173,000-member community, is the largest online network for cancer patients, survivors, and caregivers. A discussion thread in CSN is often initiated by a cancer survivor seeking support from other members of CSN. It captures a multi-party conversation that often serves the function of providing social support e.g., by bringing about a change of sentiment from negative to positive on the part of the thread originator. While previous studies regarding cancer survivors have shown that members of OHC derive benefits from their participation in such communities, causal accounts of the factors that contribute to the observed benefits have been lacking. This paper reports results of a study that seeks to address this gap by discovering temporal causality of the dynamics of sentiment change (on the part of the thread originators) in CSN. The resulting accounts offer new insights that the designers, managers and moderators of an online community such as CSN can utilize to facilitate and enhance the interactions so as to better meet the social support needs of the community participants. The proposed methodology also has broad applications in the discovery of temporal causality from big data.