Counter-responses to online extremism require the preliminary steps of identification, classification, and ultimately prediction of such behaviour. This thesis introduces an exploratory attempt to develop a dimension-based construct taxonomy from empirical insights into extremists, fundamentalists, lslamists, and jihadists, as a foundation upon which to build practical research. It contributes a detailed review of studies, qualified by the inclusion of individual level findings, a data-driven means to examine extremist content and communication, and an application of complex mixed methods across three papers, including machine learning to predict ideology and behaviour, in pursuit of a much-needed practical exploration of extremist phenomena.
|Qualification||Doctor of Philosophy|
|Award date||5 May 2020|
|Publication status||Unpublished - 2020|