Long term exposure to biased content in literature or media can significantly
influence people's perceptions of reality, leading to the development of
implicit biases that are difficult to detect and address (Gerbner 1998). In
this study, we propose a novel method to analyze the differences in
representation between two groups and use it examine the representation of
African Americans and White Americans in books between 1850 to 2000 with the
Google Books dataset (Goldberg and Orwant 2013). By developing better tools to
understand differences in representation, we aim to contribute to the ongoing
efforts to recognize and mitigate biases. To improve upon the more common
phrase based (men, women, white, black, etc) methods to differentiate context
(Tripodi et al. 2019, Lucy; Tadimeti, and Bamman 2022), we propose collecting a
comprehensive list of historically significant figures and using their names to
select relevant context. This novel approach offers a more accurate and nuanced
method for detecting implicit biases through reducing the risk of selection
bias. We create group representations for each decade and analyze them in an
aligned semantic space (Hamilton, Leskovec, and Jurafsky 2016). We further
support our results by assessing the time adjusted toxicity (Bassignana,
Basile, and Patti 2018) in the context for each group and identifying the
semantic axes (Lucy, Tadimeti, and Bamman 2022) that exhibit the most
significant differences between the groups across decades. We support our
method by showing that our proposed method can capture known socio political
changes accurately and our findings indicate that while the relative number of
African American names mentioned in books have increased over time, the context
surrounding them remains more toxic than white Americans.Comment: Main paper is 9 page