Understanding group-based coordination of attitudes is critically important to our most pressing global issues including climate change, vaccine hesitancy and threats to democratic processes. Until now, it has been difficult to identify opinion-based groups and to link the micro to the macro (ie. locate individuals in group-based opinion systems), as methods tend to focus on one or the other level of analysis. For example, which attitudes are most important to group identity? How are individuals located within a group depending on their individual attitudes?
A core idea of the ERC-DAFINET project is that people are linked by the attitudes they jointly hold, and that attitudes become socially connected when they are jointly held by people. From this basic idea, we developed methods to construct bipartite networks directly from survey-based data to visualize attitude networks. This method has broad interdisciplinary relevance for visualizing group structure in survey data. For example, we have applied this method to attitude data collected in the first wave of the COVID pandemic in the UK (Maher, P. J., MacCarron, P., & Quayle, M. (2020). Mapping public health responses with attitude networks..... British Journal of Social Psychology, 59(3), 641–652.
https://doi.org/10.1111/bjso.12396(odnośnik otworzy się w nowym oknie)) and American political attitudes (Dinkelberg, A., O’Reilly, C., MacCarron, P., Maher, P. J., & Quayle, M. (2021). Multidimensional polarization dynamics in US election data..... Analyses of Social Issues and Public Policy, 21(1), 284–311.
https://doi.org/10.1111/asap.12278(odnośnik otworzy się w nowym oknie)). After deriving a graph from the survey data, we can apply network methods to detect polarization (even without extremism); identify items which most clearly distinguish groups; and track polarization over time. How much overlap is there between groups? How can we identify polarization across multiple opinion dimensions? The ERC-DAFINET project provides a breakthrough mathematical method to answer these questions, but previously required high levels of mathematical and coding expertise to implement from first-principles.
The Attitude-Maps-4-All Proof of Concept project prototyped open software for implementing this network method, making it available to ordinary end-users (researchers and practitioners across disciplines). Where it previously required high-level technical mathematical and coding skills to implement, as a result of the work done in this Proof of Concept grant, it is now accessible to any researcher.
