The focus at the start of the project was been on evaluating the applicability of the proposed network models to the phenomenon (theory development), and experimentally confirming the proposed psychological mechanisms linking attitudes and social identity (basic human experiments).
We developed methods to constructs bipartite graphs from survey-based data to visualize how people are linked by shared attitudes, and also between attitudes (by being jointly held by people).The latter produces a network-view of opinions similar belief-network analysis, but with fewer statistical assumptions (arXiv:2012.11392). We have applied this technique to empirical attitude data collected in the first wave of the COVID pandemic in the UK, and have demonstrated that the dynamic opinion-based groups so identified predict group relevant behaviours (doi:10.1111/bjso.12396).
We have connected with work in sociology and social psychology modelling social belief systems. We have identified also discovered threads of literature where bipartite network structures have been used to model attitudes, most notably by sociologists Ronald Breiger and Bonnie Erikson (1988). We have worked to situate our theory in relation to these existing, but little-known, approaches (under review; preprint doi:10.31234/osf.io/mh4z8). This theory-building work has helped us realize the mathematical relationship between our bipartite network and existing modelling techniques, such as cluster analysis. We have established that our approach is similarly effective as cluster-analysis in identifying cohesive opinion-based groups, but with the advantage that we can locate individuals precisely in the structural opinion-space. We have demonstrated that this bipartite network approach to opinion-modelling allows us to sensitively detect polarization, even without extremism (ie. when group opinions are tightly synchronized on several moderate opinions; e..g. doi:10.1111/asap.12278; doi:10.1142/s0219525921500065).
We have realized that Axelrod’s classic model of cultural diffusion natively relies on a bipartite network structure of people connected by attitudes. We have extended this model, introducing a multidimensional equivalent of an agreement threshold, and shown that the model generates plausible clusters similar to those observed in real data (doi:10.1371/journal.pone.0233995) and is relatively impervious to underlying social network topology (e.g. friendship links), allowing us to apply it in realistic social systems (doi:10.1016/j.physa.2021.126086).
We have developed a method for generating novel attitude statements (attitudes to which people have not yet been exposed), and now have a battery to deploy in our experiments.
Despite Covid-related delays to our virtual interaction experiments (which previously relied on people participating together in a lab), we have run three experiments with human participants. These demonstrate that:
(1) Sharing novel opinions (and more specifically, expressing agreement on such opinions) results in people experiencing a sense of shared group identity.
(2) The sense of identification produced in opinion-based groups is stronger than that produced classical “minimal group” conditions where groups are differentiated on arbitrary dimensions.
(3) People come to have more certainty about attitudes that are associated with an emerging group identity.
See, for example: doi:10.1038/s44271-024-00076-7 doi:10.1002/ejsp.3000 doi:10.1111/bjso.12773 doi:10.1016/j.actpsy.2022.103751.
The VIAPPL software platform has been updated to allow a novel network game where participants exchange opinions.
