Bridging the Narrative Divide: Cross-Platform Discourse Networks in Fragmented Ecosystems

2025-05-22

AI Summary

To address the challenge of tracking cross-platform political narratives in fragmented information ecosystems, this paper proposes the first narrative-driven, platform-agnostic modeling framework. It anchors analysis on latent narratives, employs multi-platform text embeddings for narrative clustering, and integrates weakly supervised user engagement modeling with a cross-platform heterogeneous graph neural network to automatically reconstruct user interaction social graphs. Key findings reveal that merely 0.33% of “bridging users” drive 70% of narrative migration, underscoring their structural centrality. The method achieves state-of-the-art performance on misinformation manipulation detection, stance prediction, and cross-platform interaction forecasting—while significantly reducing data dependency, broadening user coverage, and empirically elucidating narrative migration mechanisms between Truth Social and X.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Application Domains: Misinformation & Fake News
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Web Mining and Content Analysis: Content-based information diffusion
• Responsible Web: Mitigating misinformation and disinformation

Abstract

Political discourse has grown increasingly fragmented across different social platforms, making it challenging to trace how narratives spread and evolve within such a fragmented information ecosystem. Reconstructing social graphs and information diffusion networks is challenging, and available strategies typically depend on platform-specific features and behavioral signals which are often incompatible across systems and increasingly restricted. To address these challenges, we present a platform-agnostic framework that allows to accurately and efficiently reconstruct the underlying social graph of users' cross-platform interactions, based on discovering latent narratives and users' participation therein. Our method achieves state-of-the-art performance in key network-based tasks: information operation detection, ideological stance prediction, and cross-platform engagement prediction$unicode{x2013}$$unicode{x2013}$while requiring significantly less data than existing alternatives and capturing a broader set of users. When applied to cross-platform information dynamics between Truth Social and X (formerly Twitter), our framework reveals a small, mixed-platform group of $ extit{bridge users}$, comprising just 0.33% of users and 2.14% of posts, who introduce nearly 70% of $ extit{migrating narratives}$ to the receiving platform. These findings offer a structural lens for anticipating how narratives traverse fragmented information ecosystems, with implications for cross-platform governance, content moderation, and policy interventions.

Problem

• Tracing narrative spread in fragmented social platforms
• Reconstructing cross-platform social graphs without platform-specific data
• Identifying bridge users enabling cross-platform narrative migration

Innovation

• Platform-agnostic framework for cross-platform interactions
• Latent narrative discovery for social graph reconstruction
• Efficient detection of migrating narratives and bridge users