  Terror attacks have been linked in part to online extremist content. Although
tens of thousands of Islamist extremism supporters consume such content, they
are a small fraction relative to peaceful Muslims. The efforts to contain the
ever-evolving extremism on social media platforms have remained inadequate and
mostly ineffective. Divergent extremist and mainstream contexts challenge
machine interpretation, with a particular threat to the precision of
classification algorithms. Our context-aware computational approach to the
analysis of extremist content on Twitter breaks down this persuasion process
into building blocks that acknowledge inherent ambiguity and sparsity that
likely challenge both manual and automated classification. We model this
process using a combination of three contextual dimensions -- religion,
ideology, and hate -- each elucidating a degree of radicalization and
highlighting independent features to render them computationally accessible. We
utilize domain-specific knowledge resources for each of these contextual
dimensions such as Qur'an for religion, the books of extremist ideologues and
preachers for political ideology and a social media hate speech corpus for
hate. Our study makes three contributions to reliable analysis: (i) Development
of a computational approach rooted in the contextual dimensions of religion,
ideology, and hate that reflects strategies employed by online Islamist
extremist groups, (ii) An in-depth analysis of relevant tweet datasets with
respect to these dimensions to exclude likely mislabeled users, and (iii) A
framework for understanding online radicalization as a process to assist
counter-programming. Given the potentially significant social impact, we
evaluate the performance of our algorithms to minimize mislabeling, where our
approach outperforms a competitive baseline by 10.2% in precision.
