“Because that’s who they are”: using language abstraction to detect attribution bias in young people’s talk about politics

This study examines whether linguistic abstraction can serve as a methodological tool for analyzing social perception, using young people’s discourse about governmental and community actors during a climate crisis as a test case. Drawing on the framework of linguistic intergroup bias and the linguistic category model, the study investigates how implicit evaluations of institutional actors are reflected in the use of abstract versus concrete language. Eleven undergraduate participants took part in a climate hackathon, simulating decision-making and problem-solving in a fictional UK seaside city. During structured, game-based activities designed to prompt negotiation and strategic discussion, participants described the roles and responsibilities of government, community groups, and their own hackathon team. A two-way ANOVA revealed significant differences in abstraction across social groups, with governmental actors described using more abstract language than both community groups and hackathon participants. Although overall sentiment effects were limited, negative evaluations of government were associated with heightened abstraction, suggesting that institutional behavior was constructed in dispositional rather than situational terms. In contrast, community and in-group actors were described more concretely, indicating context-dependent interpretations of their actions. These findings demonstrate the value of analyzing linguistic abstraction for identifying implicit attributional biases in discourse.