  The confluence of technological and societal advances is changing the nature
of global terrorism. For example, engagement with Web, social media, and smart
devices has the potential to affect the mental behavior of the individuals and
influence extremist and criminal behaviors such as Radicalization. In this
context, social data analytics (i.e., the discovery, interpretation, and
communication of meaningful patterns in social data) and influence maximization
(i.e., the problem of finding a small subset of nodes in a social network which
can maximize the propagation of influence) has the potential to become a vital
asset to explore the factors involved in influencing people to participate in
extremist activities.
  To address this challenge, we study and analyze the recent work done in
influence maximization and social data analytics from effectiveness, efficiency
and scalability viewpoints. We introduce a social data analytics pipeline,
namely iRadical, to enable analysts engage with social data to explore the
potential for online radicalization. In iRadical, we present algorithms to
analyse the social data as well as the user activity patterns to learn how
influence flows in social networks. We implement iRadical as an extensible
architecture that is publicly available on GitHub and present the evaluation
results.
