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<p>Narratives on social media emerge through the interaction of content, users, and platform-specific structures, yet identifying cohesive narrative formations across platforms remains challenging. This paper introduces Weighted Contextual Focal Structures Analysis (W–CFSA), a graph-based method for discovering structurally embedded narrative groups in multiplex networks. We construct a three-layer multiplex network integrating user–user interactions, user–narrative associations, and narrative–narrative similarity, with edge weights derived from semantic alignment. The method is evaluated on over 70,000 cross-platform data collected from YouTube, TikTok, X (formerly Twitter), and Instagram between January and May 2025, comprising posts related to U.S.-China trade and tariff policies. Empirical results show that W–CFSA identifies focal sets with higher internal cohesion, greater internal density, and more substantial impact on global clustering than a size-matched eigenvector-centrality baseline. Qualitative analysis further indicates that the recovered focal sets correspond to distinct narrative architectures rather than simple stance groupings, demonstrating the value of integrating narrative context with network structure for cross-platform analysis.</p>

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Identifying cohesive narrative formations in cross-platform trade-war discourse using weighted contextual focal structures analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月10日

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  • Ridwan Amure,
  • Nitin Agarwal

摘要

Narratives on social media emerge through the interaction of content, users, and platform-specific structures, yet identifying cohesive narrative formations across platforms remains challenging. This paper introduces Weighted Contextual Focal Structures Analysis (W–CFSA), a graph-based method for discovering structurally embedded narrative groups in multiplex networks. We construct a three-layer multiplex network integrating user–user interactions, user–narrative associations, and narrative–narrative similarity, with edge weights derived from semantic alignment. The method is evaluated on over 70,000 cross-platform data collected from YouTube, TikTok, X (formerly Twitter), and Instagram between January and May 2025, comprising posts related to U.S.-China trade and tariff policies. Empirical results show that W–CFSA identifies focal sets with higher internal cohesion, greater internal density, and more substantial impact on global clustering than a size-matched eigenvector-centrality baseline. Qualitative analysis further indicates that the recovered focal sets correspond to distinct narrative architectures rather than simple stance groupings, demonstrating the value of integrating narrative context with network structure for cross-platform analysis.