arXiv Computation and Language September 11, 2026 By Max Upravitelev, Veronika Solopova, Jing Yang, Charlott Jakob, Alexandra Tsiakalou, Neda Foroutan, Vera Schmitt

From Repetition to Recognition: Inductive Discovery of Disinformation Narratives

Read the original on arXiv Computation and Language →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

arXiv Computation and Language

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arXiv AI

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