Collaborative Research: A Dyadic and Dynamic Model of Supportive Conversations as Interpersonal Emotion Regulation

Decades of research shows that people benefit from social support. Those with richer social networks experience lower rates of disease, live longer lives, and enjoy better mental health. Even though the benefits of supportive relationships are well established, less is known about how and why those outcomes occur. This project focuses on common stressful events that people experience every day, and the conversations people have with others to cope with those events. It is expected that conversations about everyday stressors help people think and feel differently about their problems, which may help account for the short- and long-term health benefits so commonly found. This project develops a new method for the coding and analysis of conversations about personal problems. Tools will be developed to train laypersons and professionals in how to be most helpful in supportive conversations, ultimately benefiting people's health and well-being.

This project uses data from four previously conducted studies in which 461 people disclosed a personal stressor to a stranger, friend, or dating partner. The project analyzes the discloser's reactions to the partner's messages by examining the ways in which the discloser expresses thoughts and feelings through language. Videotapes and transcripts of these conversations allow detailed coding of every utterance of the partner's responses. The coding system is capable of distinguishing among many different statements and expressions that commonly occur in such conversations. Detailed analyses of these conversations will illuminate the turn-to-turn effects of supportive messages on a distressed person's thoughts and feelings. The analyses will also clarify how dynamics within conversations influence the distressed person's thoughts and emotions. This project will contribute to future research aimed at developing machine-coding systems for categorizing and quantifying aspects of enacted social support, while also compiling data-analytical tools that will enable researchers to investigate the dynamics of dyadic interaction more generally.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.