Language in popular American culture constructs the meaning of healthy and unhealthy eating: Narratives of craveability, excitement, and social connection in movies, television, social media, recipes, and food reviews.

Many people want to eat healthier but struggle to do so, in part due to a dominant perception that healthy foods are at odds with hedonic goals. Is the perception that healthy foods are less appealing than unhealthy foods represented in language across popular entertainment media and social media? Six studies analyzed dialogue about food in six cultural products - creations of a culture that reflect its perspectives - including movies, television, social media posts, food recipes, and food reviews. In Study 1 (N = 617 movies) and Study 2 (N = 27 television shows), healthy foods were described with fewer appealing descriptions (e.g., "couldn't stop eating"; d = 0.59 and d = 0.37, respectively) and more unappealing descriptions (e.g., "I hate peas"; d = -.57 and d = -.63, respectively) than unhealthy foods in characters' speech from the film and television industries. Using sources with richer descriptive language, Studies 3-6 analyzed popular American restaurants' Facebook posts (Study 3, N = 2275), recipe descriptions from Allrecipes.com (Study 4, N = 1000), Yelp reviews from six U.S. cities (Study 5, N = 4403), and Twitter tweets (Study 6, N = 10,000) for seven specific themes. Meta-analytic results across Studies 3-6 showed that healthy foods were specifically described as less craveworthy (d = 0.51, 95% CI: 0.44-0.59), less exciting (d = 0.40, 95% CI: 0.31-0.49), and less social (d = 0.36, 95% CI: 0.04-0.68) than unhealthy foods. Machine learning methods further generalized patterns across 1.6 million tweets spanning 42 different foods representing a range of nutritional quality. These data suggest that strategies to encourage healthy choices must counteract pervasive narratives that dissociate healthy foods from craveability, excitement, and social connection in individuals' everyday lives.

Language from police body camera footage shows racial disparities in officer respect.

Using footage from body-worn cameras, we analyze the respectfulness of police officer language toward white and black community members during routine traffic stops. We develop computational linguistic methods that extract levels of respect automatically from transcripts, informed by a thin-slicing study of participant ratings of officer utterances. We find that officers speak with consistently less respect toward black versus white community members, even after controlling for the race of the officer, the severity of the infraction, the location of the stop, and the outcome of the stop. Such disparities in common, everyday interactions between police and the communities they serve have important implications for procedural justice and the building of police-community trust.