Language patterns of outgroup prejudice
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Updated: Oct 31, 2025

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Published on: September 28, 2018
1Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany.
Abstract:
Although explicit verbal expression of prejudice and stereotypes may have become less common due to the recent rise of social norms against prejudice, prejudice in language still persists in more subtle forms. It remains unclear whether and how language patterns predict variance in prejudice across a large number of minority groups. Informed by construal level theory, intergroup-contact theory, and linguistic expectancy bias, we leverage a natural language corpus of 1.8 million newspaper articles to investigate patterns of language referencing 60 U.S. minority groups. We found that perception of social distance among immigrant groups is reflected in language production: Groups perceived as socially distant (vs. close) are also more likely to be mentioned in abstract (vs. concrete) language. Concreteness was also strongly positively correlated with sentiment, a phenomenon that was unique to language concerning minority groups, suggesting a strong tendency for more socially distant groups to be represented with more negative language. We also provide a qualitative exploration of the content of outgroup prejudice by applying Latent Dirichlet Allocation to language referencing minority groups in the context of immigration. We identified 15 immigrant-related topics (e.g., politics, arts, crime, illegal workers, museums, food) and the strength of their association and relationship with perceived sentiment for each minority group. This research demonstrates how perceived social distance and language concreteness are related and correlate with outgroup negativity, provides a practical and ecologically valid method for investigating perceptions of minority groups in language, and helps elaborate the connection between theoretical positions from social psychology with recent studies from computer science on prejudice embedded in natural language.
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