论文标题
量化消费文化中的性别偏见
Quantifying Gender Bias in Consumer Culture
论文作者
论文摘要
诸如歌曲之类的文化项目在创建和增强刻板印象，偏见和歧视方面具有重要影响。但是，此类项目的实际性质通常不那么透明。以歌曲为例。歌词对女性有偏见吗？随着时间的流逝，这种偏见发生了什么变化？在50年中，四分之一的歌曲中的四分之一的自然语言处理量化了厌女症。妇女不太可能与理想的特征（即能力）相关，尽管这种偏见有所下降，但它仍然存在。辅助分析进一步表明，歌曲歌词可能有助于推动社会刻板印象对女性的转变，而抒情的转变是由男性艺术家驱动的（因为女性艺术家一开始就不太有偏见）。总体而言，这些结果阐明了文化进化，偏见和歧视的微妙措施，以及自然语言处理和机器学习如何为刻板印象和文化变革提供更深入的了解。
Cultural items like songs have an important impact in creating and reinforcing stereotypes, biases, and discrimination. 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 over 50 years quantifies misogyny. 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 help drive shifts in societal stereotypes towards 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 and cultural change.
