This project is developing a network theory of attitudes based on Dynamic Attitude Fixing in NETworks (DAFINET).
Funded by an ERC starter grant, and in collaboration with James Gleeson, Kevin Burke and Susan Fennel at the University of Limerick, Kevin Durrheim at the University of KwaZulu-Natal and others, the project will attempt to develop and test a multilayer model of attitudes in networks.
Here’s the abstract:
Understanding the coordination of attitudes in societies is vitally important for many disciplines and global social challenges. Network opinion dynamics are poorly understood, especially in hybrid networks where automated (bot) agents seek to influence economic or political processes (e.g. USA: Trump vs Clinton; UK: Brexit). A dynamic fixing theory of attitudes is proposed, premised on three features of attitudes demonstrated in ethnomethodology and social psychology; that people: 1) simultaneously hold a repertoire of multiple (sometimes ambivalent) attitudes, 2) express attitudes to enact social identity; and 3) are accountable for attitude expression in interaction. It is proposed that interactions between agents generate symbolic links between attitudes with the emergent social-symbolic structure generating perceived ingroup similarity and outgroup difference in a multilayer network. Thus attitudes can become dynamically fixed when constellations of attitudes are locked-in to identities via multilayer networks of attitude agreement and disagreement; a process intensified by conflict, threat or zero-sum partisan processes (e.g. elections/referenda). Agent-based simulations will validate the theory and explore the hypothesized channels of bot influence. Network experiments with human and hybrid networks will test theoretically derived hypotheses. Observational network studies will assess model fit using historical Twitter data. Results will provide a social-psychological-network theory for attitude dynamics and vulnerability to computational propaganda in hybrid networks.
The theory will explain:
(a) when and how consensus can propagate rapidly through networks (since identity processes fix attitudes already contained within repertoires);
(b) limits of identity-related attitude propagation (since attitudes outside of repertoires will not be easily adopted); and
(c) how attitudes can often ‘roll back’ after events (since contextual changes ‘unfix’ attitudes).
The proposed project capitalizes on multi-disciplinary advances in attitudes, identity and network science to develop the theory of Dynamic Fixing of Attitudes In NETworks (DAFINET).
Specifically, DAFINET integrates advances in identity research from social psychology, models of attitudes from ethnomethodology (a branch of sociology), and multilayer network modelling from network science to propose a novel theory of opinion dynamics and social influence in networks. DAFINET will have impact in a broad range of disciplines where attitude propagation and social influence is of concern, including in economics, sociology, social psychology, marketing, political science, health behaviour, environmental science, and many others.