Thinking about tomorrow: A population-based natural language processing analysis of young adults' hopes and worries...

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 offer the potential to reveal novel insights, but remain an underused resource. Advances in natural language processing (NLP) offer powerful tools for efficiently analyzing text in large samples.

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.

Data came from an urban community sample of 24-year-olds (N = 1113) who provided brief written responses about their greatest hopes and worries. A total of 3973 text-segments were analyzed using topic modeling with the Python library BERTopic. Associations of the themes with internalizing symptoms were tested using regression analyses.

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 domains. Many themes overlapped, but hopes tended to center more on interpersonal relationships, whereas worries were focused more on systemic and global challenges. Higher levels of internalizing symptoms were associated with more interpersonal and work-, life-, and financial worries, but with fewer systemic concerns, such as climate- or war and conflict- related worries.

Our work exemplifies diverse, normative, and broadly shared hopes and worries among young adults that are important for youth-focused public policy and psychosocial support. Specific topics associated with internalizing symptoms including financial or interpersonal concerns constitute concrete interventional targets for alleviating distress.