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Dynamic Attitude Fixing: A novel theory of opinion dynamics in social networks and its implications for computational propaganda in hybrid social networks (containing humans and bots)

Total Cost €


EC-Contrib. €






 DAFINET project word cloud

Explore the words cloud of the DAFINET project. It provides you a very rough idea of what is the project "DAFINET" about.

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Project "DAFINET" data sheet

The following table provides information about the project.


Organization address
postcode: -

contact info
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 Coordinator Country Ireland [IE]
 Total cost 1˙499˙925 €
 EC max contribution 1˙499˙925 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2018-STG
 Funding Scheme ERC-STG
 Starting year 2018
 Duration (year-month-day) from 2018-12-01   to  2023-11-30


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    UNIVERSITY OF LIMERICK IE (LIMERICK) coordinator 1˙499˙925.00


 Project objective

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).

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The information about "DAFINET" are provided by the European Opendata Portal: CORDIS opendata.

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