The Social Process of Coping with Work‐Related Stressors Online: A Machine Learning and Interpretive Data Science Approach

Abstract

People are increasingly turning to social media and online forums like Reddit to cope with work-related concerns. Previous research suggests that how others respond can be an important determinant of the sharer's affective and well-being outcomes. However, less is known about whether and how cues embedded in the content of what is shared can shape the type of responses that one receives from others, obscuring the joint and interactive role that both the sharer and listener may play in influencing the sharer's outcomes. In this study, we develop theory to advance our understanding of online coping with an explicitly social focus using computational grounded theorizing and machine learning (ML) techniques applied to a large corpus of work-related conversations on Reddit. Specifically, our theoretical model sheds light on the dynamics of the online social coping process related to the domain of work. We show that how sharers and listeners interact and react to one another depends on the content of stressors shared, the social coping behaviors used when sharing, and whether the sharer and listener belong to the same occupational context. We contribute to the social coping literature in three ways. First, we clarify how social actors respond to cues embedded in the social coping attempt. Second, we examine the moderating role that such responses play in shaping sharer outcomes. Finally, we extend theory on social coping with work-related stressors to the online domain. Taken together, this research highlights the importance of the dynamic interplay between sharer and listener in the context of online social coping.