The queer Hero versus the Fool bias of the queer trait: An archetypometric analysis of the collective portrayal of queerness in fictional stories
2026-07-09 • Computers and Society
Computers and Society
AI summaryⓘ
The authors studied how queer characters are shown in movies, TV, and books by looking at common character types like Heroes or Fools. They found that the most queer characters often have positive, heroic roles. However, when looking at many stories together, there's a bias where queer traits are linked more with less heroic types like Fools. Their work shows how queer representation is complicated and suggests caution when using many authors' stories to learn about these characters.
queer representationmedia visibilityarchetypesHeroFoolidentity developmentLGBTQIA+character portrayalstereotypesstory analysis
Authors
Ashley M. A. Fehr, Calla Glavin Beauregard, Julia Witte Zimmerman, Danny Benett, Timothy R. Tangherlini, Christopher M. Danforth, Peter Sheridan Dodds
Abstract
Visibility in media is pivotal for identity development and for broadening societal views of gender and sexuality. Queer representation has increased in recent years, yet damaging stereotypes and tropes persist. Here, we focus on queer portrayal and its perception by audiences in fictional stories (television, film, and literature) by studying characters by their quantified archetypes which are operationalizations of common conceptions such as Hero, Diva, and Outcast. We use the archetypometrics and Fandom's LGBTQIA+ datasets to study samples of fictional characters along the trait differential spanning straight to queer. We find, quantify, and explain a seeming paradox. The characters with the highest queer score present positive primary archetypes and are typically Heroes rather than Fools, Angels rather than Demons, and Adventurers rather than Traditionalists. But evaluation across many stories for the straight-queer trait itself reveals a strong collective-writing bias towards Fool (away from Hero) and no meaningful loading for the other two dimensions. Our analysis offers a population-scale view of the complexities of queer portrayal, while also pointing to risks in blindly training on many-authored story corpora.