使用结构性主题建模来理解与种族相关的叙述
Rachel Jacobson1, Dulce Wilkinson Westberg2, Edward Chou3
1Department of Psychology, University of Oregon, Eugene, Oregon, USA.
Journal of personality
|July 23, 2024
概括
这项研究使用自然语言处理来分析来自1149名年轻成年人的种族叙述,确定了14个不同的主题及其与代理和共融的联系.
科学领域:
- 心理学 心理学 心理学
- 社会学 社会学 社会学
- 计算语言学 计算语言学
背景情况:
- 叙述性身份研究提供了对种族身份内容的见解.
- 传统的编码方法耗时,可能缺乏灵活性.
研究的目的:
- 通过一种新的统计方法来探索与种族相关的经验.
- 应用自然语言处理来分析叙事数据.
主要方法:
- 结构主题建模 (STM) 应用于1149个年轻成人叙述.
- 参与者写了一段民族意识的时刻.
主要成果:
- 在种族叙述中确定了14个主题.
- 主题与参与者的种族,代理和共融有关.
- 例如包括"结构不平等的获得视角" (机构) 和"同行动态" (共融).
结论:
- STM有效地从叙事数据中提取主题内容.
- 对种族叙事的多方法评估可以增强对民族经验的理解.
- 研究结果有助于理解人格的上下文方面.
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