Who is the hero, the villain, and the victim? Detection of roles in news articles using natural language techniques

Many news articles use narrative frames to present people, organizations, and facts. These narrative frames follow cultural archetypes, where readers can associate each one of the presented elements with familiar stereotypes, well-known characters, and recognizable outcomes. In this way, authors can cast real people or organizations as heroes, villains, or victims. We present a system that identifies the main entities of the article, and it uses dictionaries based on fictional characters and sentiment analysis to determine when an entity is being cast as a hero, villain, or a victim. This system interacts with news consumers directly through a browser extension. Our hope is that by informing readers when an entity is cast in one of these roles, we can make implicit bias explicit, and assist readers in applying their media literacy skills.