Collective Stress in The Digital Age




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Collective stress occurs when communities are faced with unfavorable circumstances in which they fear losing (or lose) conditions of life they are accustomed to. Such stressors are plenty in current times, ranging from complications of pandemics, technological and man-made disasters, toxic job environments, economic crises, government oppressions, terror attacks, and political breakdowns are all acting as catalysts for collective stress. Since their impacts can be devastating and multi-faceted, a better understanding of social behaviors before (e.g. in preparation), during (e.g. in first responses), and after (e.g. in recovery) them is needed to prevent and alleviate their effects. While going through all these phases, in our current digital age we actively use information and communication technologies (ICT) both at work (e.g. calendar and e-mail) and outside work (e.g. social media). I argue and demonstrate in this dissertation that by examining ICT data with computational techniques, we can understand, model, and theorize about collective stress related social behaviors in ways that were not possible before, and thus handle their complications more effectively. In particular, I show how computational social science (CSS) can solve the common scientific problems of behavioral unobservability (temporal and spatial), limited study extent (sample size and longevity), and informant subjectivity (biases in self-report based measurements). Thus, this dissertation makes a paradigmatic contribution to the field of collective stress research. Regarding more specific theoretical, methodological, and empirical contributions, this dissertation contains three studies each of which is the first computational social scientific study in its own domain. Collective stress researchers have made calls particularly for a need of an objective work stressor measurement strategy, for more empirical studies on blame attribution (in collective stress situations) and also on adoption of teleworking out of necessity; and this dissertation responds to each of them with a separate essay. The study on work stressors is a solution-oriented research that guides People Analytics practitioners in achieving better employee experience by showing how to measure stressors in organizations using commonplace workplace ICT. The other two studies serve to the advancement of theories by forming and testing hypotheses using the data collected and analyzed during the 2016 Flint Water Crisis and COVID-19 pandemic, from social media and workplace ICT (calendar and workplace messaging apps), respectively. Thus, by building novel research designs (such as retrospective cohort analysis), implementing new computational and quantitative methods (such as combining data from multiple sources, conducting large scale social network analysis, and sentiment analysis), and exploiting newly available data sources (social media and work ICT), this dissertation shows how computational social science can increase our understanding of collective stress in the digital age.