The central aim of this project is to develop a conceptually-grounded algorithm that can be combined with social media analytics software to predict radicalisation of mainstream users. Our research questions are, how and why do people develop identification with radical Islam online over time?
Our challenge in addressing these issues is that there are currently no big data analytics systems that can enable three necessary and significant sets of analyses:
- Longitudinal analysis (meaningful change in an individual’s posts over time)
- Qualitative analysis of the narrative content of a large volume of posts
- Prediction of the emergence of new online psychological groups or expansion of existing groups.
To do this, we will build upon the capabilities of an existing software tool, Chorus, and integrate it with a new conceptual framework from Psychology to explore the usefulness of a novel analysis of longitudinal qualitative big data. By doing so, we aim to identify novel variables derived from online language that can explain a significant amount of variance in the development of extremism. The project will thus provide initial tests of the new software capabilities, and proof of concept for a ‘radicalisation algorithm’.