An Agent-Based Model of Online Protest and Repression in Authoritarian Settings
Summary
Social media can amplify protests in authoritarian settings but also enable repression by regimes and their supporters. Existing research on online protest and repression has established the need for exploring mechanisms affecting their dynamics—an inquiry well-suited for an agent-based modeling approach. Drawing on relevant theories and existing models, we present a conceptual agent-based model designed to simulate social media interactions between protesters and repressors, which may result in enduring campaigns or the suppression of dissenting voices. Our central aim is to understand what key mechanisms affect these dynamics and how, accounting for the most relevant social media features and the central drivers for online protest and repression specific to authoritarian settings. Accordingly, we address the following pressing questions: What combinations of factors enable protests to scale up and dominate over repression for the longest on social media? Conversely, what combinations of factors allow repression to scale up and weaken protests the fastest on social media? Ultimately, our model will help us understand how dissenting voices are empowered and silenced in authoritarian contexts online. This study aims to contribute to the theoretical understanding of digital activism in authoritarian settings and offer insights that may help policymakers, activists, and social media stakeholders navigate, counter, and prevent digital repression.
