Pathways to radicalisation: using social network analysis to detect harmful and protective influences within social networks (Countering Violent Extremism)
There are numerous novel CVE intervention programmes around the world that deal with communities or peer effects (UK Home Office’s ‘anti-radicalisation programme’s’ nudging people away from ‘peer pressure’; Moonshot CVE and Jigsaw focus on online networks; and Exit Norway establishes support networks). People against Violent Extremism (PaVE), the only program in Australia, employs ‘innovative practice and peer-to-peer influence’. The evidence base for these efforts is exclusively based on a notion of the network as a metaphor and not as an analytical tool. We extend such programs by using innovative network analysis to evaluate the preventative potential of social networks and social network analysis (SNA) is a coherent framework for studying peer-to-peer influence (rather than defining ‘peers’ as generalised community or specific peer groups), and adopt a network lens on community and peer factors in radicalisation, which are important processes as drivers in all forms of VE. Integral to this is how the network relates ‘community resilience’ to ‘disengaging individuals’. The project is predicated on the notion that interventions should take the network into account, and could be strengthened by approaches which privilege network perspectives and analysis. Together with the Universities of Flinders and Deakin we will examine and test both ‘push’ and ‘pull’ factors as conceived in terms of embeddedness in different network communities using longitudinal network data on individuals known to have been radicalised.
Funded by The State of Victoria through the Department of Justice and Community Safety
Longitudinal Analysis for 10 Years Beyond Bushfires
With colleagues at UNSW and Swinburne we re-analyse a longitudinal community network dataset for bushfire affected communities. In studying the association of wellbeing and support ties we have to account for the fact that data are very patchy with non-response and missingness often being contingent on the network structure itself.
Creation of knowledge on ecological hazards in Russian and European local communities
ith colleagues at St Petersburg State University and a number of European partners, we aim to determine how local actors learn flood management using a sociosemantic network approach. This entails developing and extending current methodological approaches for looking at the association of social ties with concepts and their embeddedness is semantic networks.
Funded by the Russian Science Foundation