| Abstract: | Every time a major violent act takes place in the United States, a public debate erupts as to whether it should be considered terrorism. Political scientists have offered a variety of conceptual frameworks, but have neglected to explore how ordinary citizens understand terrorism, despite the central role the public plays in our understanding of the relationship between terrorism and government action in the wake of violence. We synthesize components of both scholarly definitions and public debates to formulate predictions for how various attributes of incidents affect the likelihood they are perceived as terrorism. Combining a conjoint experiment with machine learning techniques and automated content analysis of media coverage, we show the importance not only of the type and severity of violence, but also the attributed motivation for the incident and social categorization of the actor. The findings demonstrate how the language used to describe violent incidents, for which the media has considerable latitude, affects the likelihood the public classifies incidents as terrorism. |
