Journal of Complex Networks

Representing collective thinking through cognitive networks – 2022

with D. Duarte (UFMG), G. Guedes (UFMG), W. H. Pereira (UFMG) – accepted for publication at Journal of Complex Networks

Abstract

This work presents a novel quantitative approach using network features to represent community collective thinking. We propose a new function, called cognitive affinity coefficient, that maps individual cognitive links within a graph structure. This function transforms the data generated by the words chosen for an individual regarding a specific subject into an appropriate relational object for analyzing cognitive networks. We apply our methodology to novel data on evocations about river floods, which allowed us to find communities inside the network according to their thinking about this subject and identify the most active individuals inside each one and, therefore, explicit their collective thinking. We also present a utility tool implemented in R, which allows the analysis of the data collected as a network.

Find the PDF file at

to appear at Journal of Complex Network.

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