Modelling the complex dynamics of online disinformation cascades
Abstract
Disinformation on online social media has been on the rise in the past years. While there is extensive literature modelling social cascades, online disinformation cascades show distinctive characteristics: they provoke debunking reactions, they spread over networks with heterogeneous connectivity, and they compete for the limited attention of users --- which recommendation algorithms may partly offset. We propose a model including these ingredients and study it through Monte--Carlo simulations and mean--field approximations, on random graphs and an empirical Twitter network. We find that debunking reactions are double--edged: they reduce the reach of appealing items but allow unappealing ones to reach audiences they would not otherwise reach. Degree heterogeneity makes cascades trigger at a much lower content appeal but limits their final reach: heterogeneous networks are easy to ignite yet hard to saturate. Limited attention cancels the effects from degree heterogeneity, although the mediation of recommendation algorithms may restore them.
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BibTeX
@misc{moreno2026modelling,
title = {{Modelling the complex dynamics of online disinformation cascades}},
author = {Guillermo Romero Moreno and Valentin Robu and Han La Poutré},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.13839},
eprint = {2610.13839},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.13839},
note = {CC BY-NC-SA 4.0},
}
RIS
TY - UNPB
TI - Modelling the complex dynamics of online disinformation cascades
AU - Guillermo Romero Moreno
AU - Valentin Robu
AU - Han La Poutré
PY - 2026
DA - 2026/10/09
PB - arXiv
DO - 10.48550/arxiv.2610.13839
UR - https://arxiv.org/abs/2610.13839
AB - Disinformation on online social media has been on the rise in the past years. While there is extensive literature modelling social cascades, online disinformation cascades show distinctive characteristics: they provoke debunking reactions, they spread over networks with heterogeneous connectivity, and they compete for the limited attention of users --- which recommendation algorithms may partly offset. We propose a model including these ingredients and study it through Monte--Carlo simulations and mean--field approximations, on random graphs and an empirical Twitter network. We find that debunking reactions are double--edged: they reduce the reach of appealing items but allow unappealing ones to reach audiences they would not otherwise reach. Degree heterogeneity makes cascades trigger at a much lower content appeal but limits their final reach: heterogeneous networks are easy to ignite yet hard to saturate. Limited attention cancels the effects from degree heterogeneity, although the mediation of recommendation algorithms may restore them.
ER -