arXiv AI By Yuliia Vistak, Viktoriia Makovska, Vera Schmitt, Veronika Solopova

Graph-Based Detection of Disinformation Narrative Diffusion between Russian and Ukrainian Telegram Channels

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arXiv:2607. 11894v1 Announce Type: cross Abstract: Detecting disinformation narratives on social media is challenging due to the scale of amplification, rapid evolution, and linguistic variability of online content.

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arXiv Machine Learning
Aug 4

Network Information Enhances Unreliable News Domain Detection

arXiv:2608. 02399v1 Announce Type: cross Abstract: Content-based detection of unreliable news is increasingly difficult, as low-reliability sources mimic credible journalism and generative AI makes fabricated content harder to flag.

By Raphaela Ke{\ss}ler, Roman David Ventzke, Viola Priesemann, Giordano De Marzo
arXiv Computation and Language
Sep 11

From Repetition to Recognition: Inductive Discovery of Disinformation Narratives

The paper introduces a three-tier evaluation framework—recovery, mining, and discovery—for unsupervised narrative label generation in disinformation datasets. It compares clustering-based and graph-community-based pipelines across seven datasets, finding that clustering can underrepresent prominent topics while graph methods produce many singletons that human annotators recognize as valid narratives. The authors release human-validated narrative candidate labels for the Climate Obstruction and PolyNarrative datasets to aid taxonomy development and dataset expansion.

By Max Upravitelev, Veronika Solopova, Jing Yang, Charlott Jakob, Alexandra Tsiakalou, Neda Foroutan, Vera Schmitt
arXiv Computation and Language
Sep 11

Automated Identification of Competing Narratives in Political Discourse on Social Media

The paper introduces an unsupervised framework that identifies and characterizes competing narratives in political discourse on social media, specifically analyzing German politicians' tweets. It uses a multi‑stage pipeline incorporating topic modeling, event detection, and event linking to form coherent stories and reveal distinct user community perspectives. Two case studies on polarizing issues demonstrate the method’s effectiveness in uncovering divergent viewpoints and framing conflicts around trending political topics.

By Sergej Wildemann, Erick Elejalde