Differential Online Exposure to Extremist Content and Political Violence: Testing the Relative Strength of Social Learning and Competing Perspectives

Abstract

The present study applies Social Learning (Differential Association) Theory to the explanation of political violence, focusing on exposure to extremist content through new social media (NSM) and controlling for key variables derived from rival theories. Data are gathered using (a) a paper-and-pencil study among high school students, and (b) a web survey targeting youths between 16 and 24 years old. A total of 6020 respondents form the dataset. Binary logistic regression is used to analyze the data. Results show that even when controlling for background variables, strain variables, personality characteristics, moral values, and peer influences, the statistical association between measures of extremism through NSM (ENSM) and self-reported political violence remains significant and fairly constant. The most persistent effects are found for those measures where individuals actively seek out extremist content on the Internet, as opposed to passive and accidental encounters using NSM. Furthermore, offline differential associations with racist and delinquent peers are also strongly and directly related to self-reported political violence, as are some mechanisms from rival perspectives. This indicates that political violence can only partially be explained by social learning and suggests that the impact of ENSM is mediated by real-world associations and that the offline world has to be taken into account.