Abstract
Abstract The violation of social norms in TV and cinema is a well-known source of humor and catharsis, and researchers in digital humanities may benefit from the automatic identification of social norm violations. In this article, we introduce a novel methodology for identifying and analyzing the violation of social norms in textual data and illustrate it in the analysis of movie plots. The methodology leans on zero-shot classification, specifically relevant when massive, labeled datasets are unavailable. We test our methodology and provide researchers with (1) a theoretically grounded tool for screening textual data for social norm violation and with new datasets that include (2) 6,806 embarrassing situations from movie plots and their hypothesized violated norm and (3) 3,059 movie plots with their average embarrassment score.
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