Self-Similarity in Online Networks During Social Movements
Manuel Suarez-Roman, M. \'Angeles Serrano, Y\'erali Gandica

TL;DR
This study reveals that social movements exhibit self-similar, scale-invariant interaction patterns during peak mobilization, suggesting a universal organizing principle for collective action on online platforms.
Contribution
It uncovers the emergence of self-similar network structures in diverse social movements, linking them to hyperbolic embedding and effective social distances.
Findings
Self-similar patterns appear at peak mobilization times across movements.
Modular-to-nested network transitions occur during critical points.
A latent metric structure supports hyperbolic embedding of social networks.
Abstract
Online platforms provide an infrastructure for social movements, leaving digital traces that can be modelled as networks to quantify how information, participation, and coordination emerge during episodes of collective action and evolve over time. In this work, we unveil the emergence of scale-invariant online interaction patterns in social movements through network analysis of three geographically and sociopolitically distinct massive mobilisation events. By constructing co-occurrence networks from Twitter (now X) hashtag data and applying a degree-thresholding renormalisation procedure, we demonstrate that these highly correlated social phenomena exhibit clear signatures of self-similarity at peak mobilisation times. These critical points are characterised by modular-to-nested transitions, both in the co-occurrence networks and the bi-partite ones, maxima in user participation, and…
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