This awardee has received supplemental funding. This award detail page includes information about both the original award and supplemental awards.
Description of original award (Fiscal Year 2015, $386,194)
With few exceptions (Deloughery, King and Asal 2012) there has been little research to date that explores connections between hate crimes and terrorism. The role of the social media in fostering connections between individuals and hate crime and violent extremist movements and organizations has been a growing concern in recent years. There is little doubt that the use of online media by hate and extremist groups across the ideological spectrum has become both more ubiquitous and sophisticated.
In this project we propose to test these theoretical perspectives by building on two major data bases that have been funded in part by past NIJ support: one focused on individuals who have committed hate crimes and the other focused on individuals who have engaged in domestic extremism. The United States Extremist Crime Database (ECDB) study includes the names of over 2100 perpetrators, who are supporters of Far-Right, Far-Left eco-terrorists, or al-Qaeda-inspired or affiliated extremist ideologies, who committed ideologically motivated homicides and foiled plots in the United States since 1990, or who were involved in non-violent financial crimes. These cases include both hate crime offenders (they purposefully targeted racial or religious minorities) and terrorist offenders (they purposefully targeted the U.S. government, citizens, or society at large.
The Profiles of Individual Radicalization in the United States (PIRUS) database includes over 120 variables on more than 1,600 individuals who have radicalized in the United States. PIRUS includes individuals from Far-Right, Far-Left and al-Qaeda inspired ideologies and is divided about evenly between individuals who committed violent and nonviolent acts. While both ECDB and PIRUS include a number of internet-related items (e.g., what role did the
6 Internet play in the individuals radicalization?), they do not include data on the actual social media experiences of the individuals included. The proposed project will fill this gap by collecting data from social media (Twitter, Facebook), blogs and fora, and Tor networks for a subset of individuals who have engaged in hate crimes from ECDB and extremist violence from PIRUS during the period when social media and Internet data have become available.
Few prior studies have systematically compared the characteristics and trajectories of those engaged in hate crime movements and groups to those engaged in violent and nonviolent political extremism. Even fewer prior studies have attempted such a comparison with both on-line and off-line data. These empirical efforts will be grounded in classical criminological perspectives on social learning and social control. To summarize, the purpose of the proposed research is to apply social learning and social control perspectives to compare the on-line and off-line radicalization processes of a national sample of individuals who have engaged in violent and non-violent hate crime and violent and non-violent political extremism.
This project contains a research and/or development component, as defined in applicable law.
In this project we propose to test whether theories of social control and social learning can explain why individuals enter hate groups or are radicalized to violent extremism by building on two major data bases that have been funded in part by past NIJ support. The United States Extremist Crime Database (ECDB) study includes the names of over 2,100 perpetrators who are supporters of Far-Right, Far-Left Eco-terrorists, or al-Qaeda-inspired or affiliated extremist ideologies, who committed ideologically motivated homicides and foiled plots in the United States since 1990, or who were involved in non-violent financial crimes. The Profiles of Individual
Radicalization in the United States (PIRUS) database includes over 120 variables on more than 1,600 individuals who have radicalized in the United States, including those from Far-Right, Far-Left and al-Qaeda inspired ideologies. Individuals will be triangulated between these two datasets, and multivariate analyses (including linear and generalized linear regression models)will be used to highlight similarities and differences across these offenders in background, attitudes, and behaviors. This will allow us to determine if the correlates of extremist group participation are similar to that of hate group membership, and the role that social control or social learning play in directing those engagements.