@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://aclanthology.org/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."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="vistak-etal-2026-graph">
<titleInfo>
<title>Graph-Based Detection of Disinformation Narrative Diffusion between Russian and Ukrainian Telegram Channels</title>
</titleInfo>
<name type="personal">
<namePart type="given">Yuliia</namePart>
<namePart type="family">Vistak</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Viktoriia</namePart>
<namePart type="family">Makovska</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Vera</namePart>
<namePart type="family">Schmitt</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Veronika</namePart>
<namePart type="family">Solopova</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-05</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Fifth Ukrainian Natural Language Processing Conference (UNLP 2026)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Mariana</namePart>
<namePart type="family">Romanyshyn</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Lviv, Ukraine</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-359-3</identifier>
</relatedItem>
<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.</abstract>
<identifier type="citekey">vistak-etal-2026-graph</identifier>
<location>
<url>https://aclanthology.org/2026.unlp-1.9/</url>
</location>
<part>
<date>2026-05</date>
<extent unit="page">
<start>80</start>
<end>96</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Graph-Based Detection of Disinformation Narrative Diffusion between Russian and Ukrainian Telegram Channels
%A Vistak, Yuliia
%A Makovska, Viktoriia
%A Schmitt, Vera
%A Solopova, Veronika
%Y Romanyshyn, Mariana
%S Proceedings of the Fifth Ukrainian Natural Language Processing Conference (UNLP 2026)
%D 2026
%8 May
%I Association for Computational Linguistics
%C Lviv, Ukraine
%@ 979-8-89176-359-3
%F vistak-etal-2026-graph
%X 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.
%U https://aclanthology.org/2026.unlp-1.9/
%P 80-96
Markdown (Informal)
[Graph-Based Detection of Disinformation Narrative Diffusion between Russian and Ukrainian Telegram Channels](https://aclanthology.org/2026.unlp-1.9/) (Vistak et al., UNLP 2026)
ACL