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Using structural topic modeling to understand ethnicity-related narratives.

Rachel Jacobson1, Dulce Wilkinson Westberg2, Edward Chou3

  • 1Department of Psychology, University of Oregon, Eugene, Oregon, USA.

Journal of Personality
|July 23, 2024
PubMed
Summary
This summary is machine-generated.

This study used natural language processing to analyze ethnicity narratives from 1149 young adults, identifying 14 distinct topics and their links to agency and communion.

Keywords:
STMagencycommunionethnic identitynarrative identity

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Area of Science:

  • Psychology
  • Sociology
  • Computational Linguistics

Background:

  • Narrative identity research offers insights into ethnic identity content.
  • Traditional coding methods are time-intensive and may lack flexibility.

Purpose of the Study:

  • To explore ethnicity-related experiences using a novel statistical approach.
  • To apply natural language processing for analyzing narrative data.

Main Methods:

  • Structural Topic Modeling (STM) was applied to 1149 young adult narratives.
  • Participants wrote about a moment of ethnic awareness.

Main Results:

  • 14 topics were identified in ethnicity narratives.
  • Topics were linked to participants' ethnicity, agency, and communion.
  • Examples include 'Gained perspective of structural inequality' (agency) and 'Peer dynamics' (communion).

Conclusions:

  • STM is effective for extracting topic content from narrative data.
  • Multi-method assessment of ethnicity narratives enhances understanding of ethnic experiences.
  • Findings contribute to understanding contextualized personality aspects.