Scholarlink

Narratives are increasingly disseminated across multiple social media platforms, creating complex dynamics that amplify their reach and influence. We propose a susceptible, exposed, infected, recovered, and skeptic (SEIRZ) compartmental model grounded in a multiplex exposure framework to capture both within-platform processes and cross-layer couplings. We demonstrated that the system is stable, bounded, and has a unique solution under the given system assumptions. Using data from four platforms concerning the 2024 Taiwanese Presidential election (Instagram: 4,817 posts; TikTok: 2,560; X: 11,134; YouTube: 7,327), we show that narrative reproduction depends critically on exposure intensity and inter-platform links. The framework provides a tractable, data-driven approach for analyzing and forecasting cross-platform narrative diffusion.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Modeling Cross-Platform Narrative Contagion Using a Multiplex SEIRZ Framework

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Ridwan Amure,
  • Nitin Agarwal

摘要

Narratives are increasingly disseminated across multiple social media platforms, creating complex dynamics that amplify their reach and influence. We propose a susceptible, exposed, infected, recovered, and skeptic (SEIRZ) compartmental model grounded in a multiplex exposure framework to capture both within-platform processes and cross-layer couplings. We demonstrated that the system is stable, bounded, and has a unique solution under the given system assumptions. Using data from four platforms concerning the 2024 Taiwanese Presidential election (Instagram: 4,817 posts; TikTok: 2,560; X: 11,134; YouTube: 7,327), we show that narrative reproduction depends critically on exposure intensity and inter-platform links. The framework provides a tractable, data-driven approach for analyzing and forecasting cross-platform narrative diffusion.