Moral Alignment and Embodied Difference in Superhero Narratives: A Computational Character Analytics Study

This study examines how moral alignment is structured across gender, race/species, publisher universe, and embodied exceptionality in superhero character data. Using a computational character analytics approach applied to a publicly documented, fan-curated dataset of 734 characters, the analysis combines descriptive statistics, chi-square tests, non-parametric comparisons, and binary logistic regression. Sensitivity analyses address missing species information, duplicate character versions, and extreme body values. Results show that moral alignment is significantly associated with gender, race/species, publisher universe, and embodied exceptionality at the bivariate level. Female characters are more frequently coded as Good, artificial and robotic characters are the only group in which villainous coding predominates, and Bad and Neutral characters display higher embodied exceptionality than Good characters. In the multivariable model, gender and artificial/robotic embodiment remain independent predictors of Good versus Bad coding, while the remaining associations are attenuated. The findings, interpreted within the limits of a cross-sectional and Anglo-American corpus, reveal how superhero narratives connect heroism, villainy, bodily difference, and categorical identity.