Narrative Sharpens Gender Gaps: Surveying Film Characters with LLM Agents

TL;DR

This paper introduces a framework that converts film characters into surveyable AI agents to analyze gender attitudes, revealing that narratives amplify gender differences and may influence AI training data.

Contribution

It presents a novel method for measuring gender values in film content using large language models as survey agents, highlighting narrative effects on gender gaps.

Findings

Agents reproduce systematic gender differences without explicit prompts.

Agents exaggerate gender gaps compared to real data.

Narratives amplify gender contrasts, affecting AI training data.

Abstract

Mainstream film is one of the richest sources of cultural content that AI systems learn from. Yet we have few tools for measuring the gender values it encodes. We present a proof-of-concept framework that turns fictional film characters into surveyable LLM agents. Using 160 U.S. films (1990--2019), we build 734 character agents from script dialogue and scene descriptions, condense their personas via expert-style reflections, and simulate World Values Survey gender-attitude responses. Agents reproduce systematic gender differences without explicit demographic prompting, suggesting attitudes emerge from behavior rather than identity labels. Benchmarked against historical survey data, agents exaggerate gender gaps and show greater decade-to-decade volatility than real populations. Narrative sharpens rather than homogenizes gender contrasts, complicating the consistent-input assumption underlying cultivation theory's mainstreaming mechanism. AI systems trained on such corpora may inherit this stylization before any model-level amplification occurs.