Learning to Hate: Explaining Participation in Online Extremism

Abstract

Purpose – This chapter investigates if Ronald Aker’s Social Structure Social Learning (SSSL) theory can help explain who is involved with the production of online materials considered hateful or extremist.

Methodology/Approach – After discussing how SSSL can account for becoming exposed to online extremism and then becoming involved in its production, the authors conduct a logistic regression on data from 1,008 American adults that predicts if they produced online hate materials with variables derived from SSSL.

Findings – Results strongly support SSSL. While structural factors such as the respondents’ differential social organization, differential social location, and differential location in the social structure predict production of online hate materials, the effect of these factors is largely mediated once social learning variables are included in the model. Specifically, the respondents’ general definitions related to violence, specific definitions related to hate speech, and differential association accounts for variation in the production of online hate materials.

Originality/Value – This research contributes to the literature in two primary ways: (1) the authors investigate a critical, yet understudied, factor involved in the radicalization process; and (2) the authors demonstrate that a leading criminological theory applies to this form of deviance. This research also suggests key variables for creating strategies for countering violent extremism.

Citation

Hawdon, J. and Costello, M. (2020), "Learning to Hate: Explaining Participation in Online Extremism", Silva, D.M.D. and Deflem, M. (Ed.) Radicalization and Counter-Radicalization (Sociology of Crime, Law and Deviance, Vol. 25), Emerald Publishing Limited, Leeds, pp. 167-182. https://doi.org/10.1108/S1521-613620200000025010