Machine Learning for Predicting Intent of Radical Action in Text Data

Abstract

Many cases of radicalism, especially violent radicalism, manifest within the context of inter-ethnic conflict and war. Research in this domain has significantly contributed to our overarching comprehension of the issue. This form of radicalism is inherently linked to the dynamics of group affiliation. However, our focus here is directed towards the individual motivation driving radical actions. While radical actions may encompass dynamics of group affiliation (e.g., the most radical pro-life activists being members of the Army of God), they also possess a substantial individual component. For instance, numerous attacks on abortion doctors or clinics are categorized as ’lone-wolf terrorism’. In this study, we sought to examine whether state-of-the-art Machine Learning tech- nology for text analysis could effectively discern the intent behind radical actions from user-generated content. We analyzed a diverse array of radical texts published online by various activists, including anarchists, Iranian guerrilla members, Indian revolution- aries, activists from the US civil rights movement, and The Army of God. Our analysis reveals that Machine Learning models can successfully identify potential signals indica- tive of the author’s intent behind radical actions.