Exploring the impact of life events, social media and political leaning on extremism attitudes.

• In Event: EXTR10 Susceptibility to radicalisation and extremist attitudes

Abstract

This paper is based on a survey conducted in early 2023 across three countries: Germany, the United States, and the United Kingdom, with a sample size of 951 participants. The study explores the interplay between personal life experiences, political leaning, and the use of Social Media, and their collective influence on the endorsement of violent extremism. Using this rich dataset, this paper explores how personality attributes, empathetic capacities, and political orientations intersect with lived experiences and social media use to shape an individual's propensity towards supporting violent extremist ideologies.

The individual extremism scores are based on a factor analysis, synthesising responses from a series of questions that probe the perceived efficacy and moral justification of employing violence to advance and propagate ideological beliefs. Our previous research into the nexus between far-right extremism and personal experiences suggested an absence of correlation. However, the influence of gender and social media engagement emerged as significant factors. In this study, we extend our analysis to encompass all manifestations of extremism, extending the previous research. Furthermore, we integrate an assessment of personality traits into our analytical framework, an element previously omitted from our research methodology.

As part of this research, we will employ cluster analysis cluster analysis, allowing for the categorisation of individuals into distinct groups based on the confluence of the aforementioned variables. Moreover, we leverage the predictive power of machine learning algorithms to analyse the data, explore the patterns that emerge from the data and test the efficacy of our predictive model about extremism. This approach not only enriches our understanding of the factors contributing to violent extremism but also enhances the predictive accuracy of our analytical model.

Based on these findings, we will further explore what impact this might have on government policy to prevent violent extremism.

Author

• Joshua Skoczylis, University of Lincoln