Victim or Perpetrator? Analysis of Violent Characters Portrayals from Movie Scripts
This addresses the issue of media influence on societal perceptions by providing tools for early-stage content analysis, though it is incremental as it builds on prior work by focusing on language instead of audio/video.
The study tackled the problem of analyzing violent content and stereotypes in movies by developing a computational method that uses language from scripts to identify character roles and demographics, revealing that female characters appear more often as victims and perpetrators are more likely to be White if the victim is Black or Latino.
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