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

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

Abstract

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.
Original languageEnglish
Title of host publicationProceedings of the 59th Hawaii International Conference on System Sciences
PublisherScholarSpace
Pages2585-2594
Number of pages10
ISBN (Print)9780998133195
Publication statusPublished - 9 Jan 2026
Event59th Hawaii International Conference on System Sciences -
Duration: 6 Jan 20269 Jan 2026

Publication series

NameProceedings of the Hawaii International Conference on System Sciences
ISSN (Electronic)2572-6862

Conference

Conference59th Hawaii International Conference on System Sciences
Abbreviated titleHICSS
Period6/01/269/01/26

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