@inproceedings{vistak-etal-2026-graph,
    title = "Graph-Based Detection of Disinformation Narrative Diffusion between {R}ussian and {U}krainian Telegram Channels",
    author = "Vistak, Yuliia  and
      Makovska, Viktoriia  and
      Schmitt, Vera  and
      Solopova, Veronika",
    editor = "Romanyshyn, Mariana",
    booktitle = "Proceedings of the Fifth {U}krainian Natural Language Processing Conference ({UNLP} 2026)",
    month = may,
    year = "2026",
    address = "Lviv, Ukraine",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2026-06/2026.unlp-1.9/",
    pages = "80--96",
    ISBN = "979-8-89176-359-3",
    abstract = "Detecting disinformation narratives on social media is challenging due to the scale of amplification, rapid evolution, and linguistic variability of online content. We propose 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."
}