Modeling competing narratives in adaptive networks: how social pressure and network dynamics drive tipping and persistence

Contested narratives on social media often spread in bursts: most remain localized, yet a few tip into rapid diffusion and reshape polarized network structure. We develop an agent-based model of competing narrative diffusion on an adaptive network to explain when such tipping dynamics occur. In the model, sharing produces social feedback that reinforces conviction, adoption is modulated by time-varying collective attention, and ties coevolve as agents rewire based on disagreement. We also include committed seeders ("zealots”) to represent persistent promotion. Across simulation experiments, we identify two recurring diffusion pathways. Under strong social pressure and highly adaptive networks, diffusion exhibits rapid, localized bursts that consolidate ideologically homogeneous clusters-accelerating tipping while restricting cross-group reach. Under more moderate network adaptation, diffusion proceeds more gradually yet reaches a broader portion of the network through bridging ties, producing wider reach with less extreme segregation. Attention decay creates a finite window for large-scale diffusion; nonetheless, once established within polarized clusters, diffusion can persist at a nontrivial level even as attention wanes when zealots continue seeding. Finally, asymmetric zealot deployment across groups generates divergent outcomes, with one group consolidating into a more homogeneous cluster while the other becomes larger but more internally heterogeneous. The model offers a general mechanism for bursty narrative diffusion in coevolving networks, with implications for disinformation campaigns serving as an application context where strategic seeding and identity-consistent reinforcement may be especially salient.