The exploitation of social networking and other online communications platforms for the dissemination of extremist content and the radicalization of other users has risen meteorically in the last decade. Unfortunately, limited scholarly attention has been devoted to research aimed at understanding these phenomena. Moreover, nascent approaches for countering these narratives or otherwise diminishing their influence are underdeveloped and have demonstrated limited efficacy. This research seeks to re-domain innovative approaches to trend forecasting – developed by the fast-paced and highly competitive fashion and lifestyle industry – and combine them with burgeoning social and computer science research. This will enable the rapid identification of trendsetters who may not have large followings themselves, but whose ideas carry to wider audiences by being picked up and amplified by users with large numbers of followers. It will also increase understanding of how influential users promoting extremist content appeal specifically to teens and tweens, the next market for online radicalization.
This research project will develop an approach for identifying ideological influencers. The model will combine trend-forecasting principles informed by fashion industry driven innovation and burgeoning social and computer science research. Specifically, the model will identify trendsetters (innovator hubs) who may not have large followings themselves, but whose ideas carry to wider audiences by being picked up and amplified by users with large numbers of followers (follower hubs). It will also identify which follower hubs appeal principally to youths, the next market for online radicalization. These findings will inform the development of an automatic detection/prediction model leveraging machine learning (AI).