Location, Location, Location: Disentangling Drivers of Terrorist Credit-Taking

Most academic definitions of terrorism emphasize the communicative function of terrorism. The aim of terrorist violence is widely held to be to gain publication for a political or religious cause. Despite this emphasis on communication, terrorists rarely seek attention by claiming responsibility for attacks. According to the Global Terrorism Database, claims of responsibility are only issued for approximately every sixth attack. This raises the question: Why do terrorists abstain from the easy “win” of claiming their attacks? Previous research has theorized that factors like state sponsorship, principal-agent problems, casualty levels, and inter-group competition are important factors in explaining variation in terrorist credit-taking propensities. In this paper, we diverge from the tried and trusted deductive approach and instead utilize an inductive approach. We apply machine learning techniques using Random Forests to predict claims of responsibility. Our initial results indicate that geographical factors, like country of attack, are the strongest predictors of claims—something largely overlooked by existing research. In our analysis, the predictive power of geographical factors thus exceeds that of explanations from the literature based on characteristics of the target of the attack, e.g. number of fatalities and wounded.