How Differing Audiences Were Associated with User Emotional Expression on a Well-Being App

Abstract

In the last five years, there has been an explosion of mobile apps that aim to impact emotional well-being, yet limited research has examined the ways that users interact, and specifically write to develop a therapeutic alliance within these apps. Writing is a developmental practice in which a narrator transforms amorphous thoughts and emotions into expressions, and according to narrative theory, the linguistic characteristics of writing can be understood as a physical manifestation of a narrator’s affect. Informed by literacy theorists who have argued convincingly that narrators address different audiences in different ways, we used IBM Watson’s Natural Language Processing software (IBM Watson NLP) to examine how users expression of emotion on a well-being app differed depending on the audience. Our findings demonstrate that audience was strongly associated with the way users expressed emotions in writing. When writing to an explicit audience users wrote longer narratives, with less sadness, less anger, less disgust, less fear, and more joy, these findings have direct relevance for researchers and well-being app design.