Victim or Perpetrator? Analysis of Violent Characters Portrayals from Movie Scripts

TL;DR

This paper introduces a computational method analyzing movie scripts to identify stereotypes in violent character portrayals, revealing demographic biases and enabling early-stage content analysis.

Contribution

It is the first to use language in scripts to detect violent stereotypes and demographic biases in movie character roles.

Findings

Female characters more often victims of violence.

Perpetrators more likely to be White when victims are Black or Latino.

Language in scripts strongly indicates violent content.

Abstract

Violent content in the media can influence viewers' perception of the society. For example, frequent depictions of certain demographics as victims or perpetrators of violence can shape stereotyped attitudes. We propose that computational methods can aid in the large-scale analysis of violence in movies. The method we develop characterizes aspects of violent content solely from the language used in the scripts. Thus, our method is applicable to a movie in the earlier stages of content creation even before it is produced. This is complementary to previous works which rely on audio or video post production. In this work, we identify stereotypes in character roles (i.e., victim, perpetrator and narrator) based on the demographics of the actor casted for that role. Our results highlight two significant differences in the frequency of portrayals as well as the demographics of the interaction between victims and perpetrators : (1) female characters appear more often as victims, and (2) perpetrators are more likely to be White if the victim is Black or Latino. To date, we are the first to show that language used in movie scripts is a strong indicator of violent content, and that there are systematic portrayals of certain demographics as victims and perpetrators in a large dataset. This offers novel computational tools to assist in creating awareness of representations in storytelling