Priming Effects of Emotion-Based Group Stigmatization in the News

Abstract: This study examines priming effects of emotion-based group stigmatization in journalistic reporting. Using computational text analysis, we test whether subtle associations of ethnicized groups with the emotions fear and admiration shape readers’ emotions, cognitions, and behavioral intentions. We conducted a multistimulus online experiment in which 2,139 German participants were randomly exposed to one of 638 real news articles referring to one of two migrant groups. Emotion-based stigmatization was quantified through latent semantic scaling, capturing the proximity of emotion-signaling words to group labels. Results show that articles associating a group more strongly with admiration (relative to fear) improved explicit evaluations of the group and marginally increased admiration felt toward the group. No effects emerged for implicit evaluations, generalized sentiment, fear, or intergroup behavioral intentions. Findings suggest that even nuanced associations in everyday reporting can activate emotional or cognitive stigmatization, while also underscoring the limits of single-exposure effects. Methodologically, the study highlights how the combination of computational content analysis with multistimulus experimental designs can advance media effects research.