Using structural topic modeling to understand ethnicity-related narratives.

Abstract

Objective: Narrative identity is a promising approach for understanding the content of individuals' ethnic identities but can be limited by the time-intensive nature of human coding and the reliance on preestablished coding systems.

Background: The aim of our preregistered study is to elucidate the content of individuals' ethnicity-related experiences using a novel statistical approach.

Method: We applied structural topic modeling (STM), a natural language processing tool, to narratives written by an ethnically diverse sample of 1149 young adults about a moment they felt aware of their ethnicity.

Results: We identified 14 topics within ethnicity narratives and analyzed how each topic related to both the participant's ethnicity and the human-coded themes of agency and communion. For example, the topic Gained perspective of structural inequality was associated with greater agency, whereas Peer dynamics was associated with greater communion. Ethnic/cultural celebration was associated with both.

Conclusions: This study introduces STM as a useful tool for extracting topic content in narrative data and demonstrates how the multi-method assessment of ethnicity narratives provides greater insight into the content of ethnic experiences. These findings contribute to our understanding of contextualized aspects of personality, including the innovative ways we might examine them.