  We propose to extend the current binary understanding of terrorism (versus
non-terrorism) with a Dynamic Matrix of Extremisms and Terrorism (DMET). DMET
considers the whole ecosystem of content and actors that can contribute to a
continuum of extremism (e.g., right-wing, left-wing, religious, separatist,
single-issue). It organizes levels of extremisms by varying degrees of
ideological engagement and the presence of violence identified (e.g., partisan,
fringe, violent extremism, terrorism) based on cognitive and behavioral cues
and group dynamics. DMET is globally applicable due to its comprehensive
conceptualization of the levels of extremisms. It is also dynamic, enabling
iterative mapping with the region- and time-specific classifications of
extremist actors. Once global actors recognize DMET types and their distinct
characteristics, they can comprehensively analyze the profiles of extremist
actors (e.g., individuals, groups, movements), track these respective actors
and their activities (e.g., social media content) over time, and launch
targeted counter activities (e.g. de-platforming, content moderation, or
redirects to targeted CVE narratives).
