W. P. Carey School of Business, Arizona State University.
Wharton School, University of Pennsylvania.
J Exp Psychol Gen. 2023 Sep;152(9):2591-2602. doi: 10.1037/xge0001412. Epub 2023 Apr 13.
Cultural items (e.g., songs, books, and movies) have an important impact in creating and reinforcing stereotypes. But the actual nature of such items is often less transparent. Take songs, for example. Are lyrics biased against women, and how have any such biases changed over time? Natural language processing of a quarter of a million songs quantifies gender bias in music over the last 50 years. Women are less likely to be associated with desirable traits (i.e., competence), and while this bias has decreased, it persists. Ancillary analyses further suggest that song lyrics may contribute to shifts in collective attitudes and stereotypes toward women, and that lyrical shifts are driven by male artists (as female artists were less biased to begin with). Overall, these results shed light on cultural evolution, subtle measures of bias and discrimination, and how natural language processing and machine learning can provide deeper insight into stereotypes, cultural change, and a range of psychological questions more generally. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
文化产品（例如歌曲、书籍和电影）在创造和强化刻板印象方面具有重要影响。但此类产品的实际性质往往不那么透明。以歌曲为例。歌词是否存在对女性的偏见，以及这种偏见随时间发生了怎样的变化？对 25 万首歌曲进行自然语言处理，定量分析了过去 50 年来音乐中的性别偏见。女性不太可能与理想的特质（即能力）相关联，尽管这种偏见有所减少，但仍然存在。辅助分析进一步表明，歌曲歌词可能导致人们对女性的集体态度和刻板印象发生变化，而且歌词的变化是由男性艺术家推动的（因为女性艺术家一开始就没有那么大的偏见）。总的来说，这些结果揭示了文化进化、微妙的偏见和歧视衡量标准，以及自然语言处理和机器学习如何能够更深入地了解刻板印象、文化变革以及更广泛的一系列心理学问题。（PsycInfo 数据库记录（c）2023 APA，保留所有权利）。
