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Three studies developed and validated a linguistic dictionary to measure negative affective polarization in English and Spanish political texts. It captures three dimensions: negative affect, delegitimization, and political context. In the first study, two independent judges evaluated the candidate words, and reliability indicators were calculated, showing acceptable values for short texts (.572 in English, .541 in Spanish) and higher values for larger corpora (.964 in English, .957 in Spanish). The second study tested discriminant validity by comparing negative affective polarization scores in social media comments on politics and entertainment. Results showed significantly higher polarization scores in political content, confirming the dictionary's validity. The third study compared the dictionary to an existing online polarization measure, finding greater coverage and alignment with the construct. Additionally, it was observed that polarization scores were higher in texts containing hate speech compared to those where it was absent. The findings suggest that the dictionary in both languages have strong psychometric properties, making it a valuable tool for analyzing online content, particularly social media comments. It can be used as an independent measure or as input for machine and deep learning models. 

Individuals who are “strongly fused” with a group view the group as self-defining. As such, they should be particularly reluctant to leave it. For the first time, we investigate the implications of identity fusion for university retention. We found that students who were strongly fused with their university (+1 SD) were 7–9% points more likely than weakly fused students (−1 SD) to remain in school up to a year later. Fusion with university predicted subsequent retention in four samples ( N = 3,193) and held while controlling for demographics, personality, prior academic performance, and belonging uncertainty. Interestingly, fusion with university was largely unrelated to grades, suggesting that identity fusion provides a novel pathway to retention independent of established pathways like academic performance. We discuss the theoretical and practical implications of these findings.