Detecting disinformation narratives on social media is challenging due to amplification scale, rapid evolution, and linguistic variability. This paper proposes a graph-based framework for identifying and analyzing disinformation narratives in Telegram ecosystems by combining weak supervision with propagation graph analysis. The approach aggregates semantically related claims into narrative-level clusters and models their diffusion across interconnected channels. This enables the detection of coordinated narrative amplification that is difficult to capture through post-level analysis alone. Our results demonstrate that integrating textual signals with network structure provides a scalable method for detecting disinformation narratives and offers insights into how they propagate within large-scale messaging environments.
Blogger's Review: This paper showcases the potential of graph analysis in disinformation detection, particularly within complex social media environments. By combining semantics with network structures, researchers can more effectively identify and understand the dynamics of information propagation, providing new insights and methodologies for future studies.