Extracting real social interactions from social media: a debate of COVID-19 policies in Mexico

TL;DR

This paper analyzes Twitter social networks during COVID-19 in Mexico, revealing distinct node types and a small subset of strongly interacting users indicative of real social interactions.

Contribution

It characterizes the structural properties of social media networks and identifies a unique node group associated with real social interactions during the pandemic.

Findings

Network composed of three node types with distinct clustering and degree properties.

A small subset (~2%) of nodes shows power-law decay in clustering coefficient, indicating feedback dynamics.

Identifies a group of strongly interacting users likely representing real social connections.

Abstract

A study of the dynamical formation of networks of friends and enemies in social media, in this case Twitter, is presented. We characterise the single node properties of such networks, as the clustering coefficient and the degree, to investigate the structure of links. The results indicate that the network is made from three kinds of nodes: one with high clustering coefficient but very small degree, a second group has zero clustering coefficient with variable degree, and finally, a third group in which the clustering coefficient as a function of the degree decays as a power law. This third group represents $\sim2\%$ of the nodes and is characteristic of dynamical networks with feedback. This part of the lattice seemingly represents strongly interacting friends in a real social network.