Why are some diseases more stigmatized than others? And, has disease stigma declined over time? Answers to these questions have been hampered by a lack of comparable, longitudinal data. Using word embedding methods, we analyze 4.7 million news articles to create new measures of stigma for 106 health conditions from 1980 to 2018. Using mixed-effects regressions, we find that behavioral health conditions and preventable diseases attract the strongest connotations of immorality and negative personality traits, and infectious diseases are most marked by disgust. These results lend new empirical support to theories that norm enforcement and contagion avoidance drive disease stigma. Challenging existing theories, we find no evidence for a link between medicalization and stigma, and inconclusive evidence on the relationship between advocacy and stigma. Finally, we find that stigma has declined dramatically over time, but only for chronic physical illnesses. In the past four decades, disease stigma has transformed from a sea of negative connotations surrounding most diseases into two primary conduits of meaning: infectious diseases spark disgust, and behavioral health conditions cue negative stereotypes. These results show that cultural meanings are especially durable when they are anchored by interests, and that cultural changes intertwine in ways that only become visible through large-scale research.

Public culture is a powerful source of cognitive socialization; for example, media language is full of meanings about body weight. Yet it remains unclear how individuals process meanings in public culture. We suggest that schema learning is a core mechanism by which public culture becomes personal culture. We propose that a burgeoning approach in computational text analysis – neural word embeddings – can be interpreted as a formal model for cultural learning. Embeddings allow us to empirically model schema learning and activation from natural language data. We illustrate our approach by extracting four lower-order schemas from news articles: the gender, moral, health, and class meanings of body weight. Using these lower-order schemas we quantify how words about body weight “fill in the blanks” about gender, morality, health, and class. Our findings reinforce ongoing concerns that machine-learning models (e.g., of natural language) can encode and reproduce harmful human biases.

Measuring meaning is a central problem in cultural sociology and word embeddings may offer powerful new tools to do so. But like any tool, they build on and exert theoretical assumptions. In this paper, I theorize the ways in which word embeddings model three core premises of a structural linguistic theory of meaning: that meaning is coherent, relational, and may be analyzed as a static system. In certain ways, word embeddings are vulnerable to the enduring critiques of these premises. In other ways, word embeddings offer novel solutions to these critiques. More broadly, formalizing the study of meaning with word embeddings offers theoretical opportunities to clarify core concepts and debates in cultural sociology, such as the coherence of meaning. Just as network analysis specified the once vague notion of social relations, formalizing meaning with embeddings can push us to specify and reimagine meaning itself.

In this article, we apply computational word embeddings to a 200-million-word corpus of American print media (1930–2009) to examine how education-relevant gender stereotypes changed as women’s educational attainment caught up with and eventually surpassed men’s. This case presents a rare opportunity to observe how cultural components of the gender system transform alongside the reversal of an important pattern of stratification. We track six stereotypes that prior work linked to academic outcomes. Our results suggest that stereotypes most closely tied to the core stereotypical distinction between women as communal and men as agentic remained unchanged. The other stereotypes we tracked, however, became increasingly gender polarized: as school and studying gained feminine associations, intelligence and unintelligence gained masculine ones. Unexpectedly, we observe that trends in the gender associations of intelligence and studying are near-perfect mirror opposites, suggesting an interrelationship. We use these observations to further elaborate contemporary theoretical accounts of the gender system, arguing that this system persists partly because stereotypes shift to reinterpret social change in terms of a durable hierarchical distinction between women and men.