The role of information skewness in shaping extremist content: A look at four extremists

Abstract

Introduction. Extremism—distinct from activism—poses a serious threat to the healthy functioning of a society. In the contemporary world, the ability of extremists to spread their narratives using digital information environments has increased tremendously. Despite a substantial body of research on extremism, our understanding of the role of information and its properties in shaping extremist content is sketchy. Method. To fill this gap, the current research has used ‘content analysis’ and ‘affective lexicon’ to identify and categorise terms from the publicly available online content of four extremists – two groups and two individuals. The property of information skewness provided the deciphering lens through which the categorised content was assessed. Analysis. Contextual categories of information relevant to all the extremists were developed to analyse the content meaningfully. Six categories of religion, ideology, politics-history, cognition, affection, and conation provided the framework used to analyse and deductively categorise the data using content analysis. The affective lexicon developed by Ortony et al. (1987) was used to identify words belonging to the categories of cognition, affection (emotions and feelings), and conation (behaviour/actions). Results. The findings reveal that the property of information skewness plays a significant role in shaping extremist content and two aspects of this property (a) intensity and (b) positivity or negativity can be used to (1) classify extremists into meaningful categories and (2) identify generalisable information strategies of extremists. Conclusion. It was found that information skewness is used in the extremist content to produce the desired level of intensity and positivity or negativity. It was also found that intensity and positivity or negativity in information skewness can be used to categorise extremists into two categories: violent extremists (high intensity information skewness with positive bias) and violent extremists (high intensity information with negative bias).