Social media interaction is a continuous response process. Social media continues to grow as communities exchange knowledge to ease stress and express emotions. A scientific way to measure online interaction and stress reduction is needed. Although literature in social and health science domains contains extensive work on the stress concept, a formal mathematical definition of stress for online interactions does not exist. Hence, the authors have leveraged empirical definitions of stress support available in literature that overlaps with the application domain (i.e., healthcare and online modes of interaction). Using the empirical definition of stress, the authors build a model for assessing stress in online communities.
TopModel For Stress Assessment
Social sciences literature lacks a formal mathematical definition of stress for online interaction. The buffering hypothesis theory predicts social support and enhances health by reducing the impact of stressful life events (Wallace, 2005; Cohen, Kamarck, & Mermelstein, 1983; Gillott, & Standen, 2007; Hoffman, & Hatch, 1996). The buffering hypothesis suggests that social support protects individuals from the harmful effects of stressful situations under some conditions (Farmer & Sundberg, 2010 ; Endler, 1997 ; Cohen, Kessler, & Underwood, 1994). Selye (1998) defined stress as the non-specific response of the body to any demand for change. To deduce a methodology for evaluating stress in online interactions, the authors used empirical definitions of stress available in social sciences and computational science literature that overlap with the healthcare domain. According to Selye (1998), stress in an interaction between individuals that can be approximated using influential factors.