{"ID":2893616,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.13310","arxiv_id":"2507.13310","title":"Modelling the spillover from online engagement to offline protest: stochastic dynamics and mean-field approximations on networks","abstract":"Social media is transforming various aspects of offline life, from everyday decisions such as dining choices to the progression of conflicts. In this study, we propose a coupled modelling framework with an online social network layer to analyse how engagement on a specific topic spills over into offline protest activities. We develop a stochastic model and derive several mean-field models of varying complexity. These models allow us to estimate the reproductive number and anticipate when surges in activity are likely to occur. A key factor is the transmission rate between the online and offline domains; for offline outbursts to emerge, this rate must fall within a critical range, neither too low nor too high. Additionally, using synthetic networks, we examine how network structure influences the accuracy of these approximations. Our findings indicate that low-density networks need more complex approximations, whereas simpler models can effectively represent higher-density networks. When tested on two real-world networks, however, increased complexity did not enhance accuracy.","short_abstract":"Social media is transforming various aspects of offline life, from everyday decisions such as dining choices to the progression of conflicts. In this study, we propose a coupled modelling framework with an online social network layer to analyse how engagement on a specific topic spills over into offline protest activit...","url_abs":"https://arxiv.org/abs/2507.13310","url_pdf":"https://arxiv.org/pdf/2507.13310v2","authors":"[\"Moyi Tian\",\"P. Jeffrey Brantingham\",\"Nancy Rodríguez\"]","published":"2025-07-17T17:30:13Z","proceeding":"physics.soc-ph","tasks":"[\"physics.soc-ph\",\"cs.SI\",\"math.DS\",\"nlin.AO\",\"q-bio.PE\"]","methods":"[]","has_code":false}