Thinking about tomorrow: A population-based natural language processing analysis of young adults’ hopes and worries for the future

Background: The recent declines in youth mental health highlight the need for research into the factors underlying distress and those that foster well-being. Open-ended text responses from young people offer the potential to reveal novel insights, but remain an underused resource. Advances in natural language processing (NLP) now offer powerful tools for efficiently analyzing text data from large, population-based samples. Objective: This study aimed to extract meaningful themes from young adults’ open-text responses about their future hopes and worries, and to test associations of these themes with internalizing symptoms.Method: Data came from an urban community sample of 24-year-olds (N=1,113) who provided brief written responses about their greatest hopes and worries. A total of 3,973 text-segments were analyzed using topic modeling with the Python library BERTopic. Associations of the themes with internalizing symptoms were tested using regression analyses.Results: Thirteen thematic topics for both hopes and worries emerged. Young adults’ hopes and worries spanned personal, interpersonal, work-life-finances and broader systemic and global concerns. Many themes overlapped, but hopes tended to center more on interpersonal relationships, whereas worries were more focused on systemic and global challenges. Interpersonal and work-life and financial worries were associated with higher levels of internalizing symptoms but with fewer systemic and global concerns.Discussion: Effective strategies to reduce distress among young people should account for their views of the future. Interventions should address personal and interpersonal challenges, while also targeting environmental contexts and broader systemic solutions. Helping young people (re-)gain hopefulness about their own and the global future is essential for promoting healthy development.