An Agent-Based Model to Simulate Individual Reframing of News Media Posts on Social Media

News frames have long been considered a powerful device for shaping people’s understanding of news issues. Recent studies have shown that social media users actively reframe media content. However, there is a limited understanding of the dynamics through which social media users reframe news over time. To address this research gap, this study develops an agent-based model to simulate individual reframing behaviour on social media, exploring the role of sentiment in the individual reframing of news posts. In the model, we design novel heterogeneous agents (innovative agent and persuadable agents), develop semantic distance-based rules to agents’ confidence in external information. The agent-based model is validated by comparing the simulation results with real data (discussions about COVID-19 vaccine) collected from Weibo. Finally, in the discussion section, we analyse the simulation results, highlight the contributions and limitations of this study, and propose avenues for future research.