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From Veracity to Diffusion: Adressing Operational Challenges in Moving From Fake-News Detection to Information Disorders

arXiv cs.CL / 3/11/2026

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Key Points

  • The paper addresses the shift in misinformation research focus from fake-news detection (veracity prediction) to understanding and predicting information diffusion and virality.
  • It highlights that misinformation involves not just fabricated content but also amplification dynamics, emphasizing the operational challenges in predicting diffusion rather than veracity.
  • Experimental comparison across two datasets, EVONS and FakeNewsNet, shows that fake-news detection remains stable with strong textual embeddings, while virality prediction is more sensitive to factors like threshold settings and early observation windows.
  • The authors propose practical, lightweight, and transparent prediction pipelines for misinformation tasks that can perform comparably to state-of-the-art methods under constrained resources.
  • This work bridges social-science theoretical insights and applied computational methods to better operationalize misinformation detection in real-world scenarios.

Computer Science > Computation and Language

arXiv:2512.02552 (cs)
[Submitted on 2 Dec 2025 (v1), last revised 10 Mar 2026 (this version, v2)]

Title:From Veracity to Diffusion: Adressing Operational Challenges in Moving From Fake-News Detection to Information Disorders

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Abstract:A wide part of research on misinformation has relied lies on fake-news detection, a task framed as the prediction of veracity labels attached to articles or claims. Yet social-science research has repeatedly emphasized that information manipulation goes beyond fabricated content and often relies on amplification dynamics. This theoretical turn has consequences for operationalization in applied social science research. What changes empirically when prediction targets move from veracity to diffusion? And which performance level can be attained in limited resources setups ? In this paper we compare fake-news detection and virality prediction across two datasets, EVONS and FakeNewsNet. We adopt an evaluation-first perspective and examine how benchmark behavior changes when the prediction target shifts from veracity to diffusion. Our experiments show that fake-news detection is comparatively stable once strong textual embeddings are available, whereas virality prediction is much more sensitive to operational choices such as threshold definition and early observation windows. The paper proposes practical ways to operationalize lightweight, transparent pipelines for misinformation-related prediction tasks that can rival with state-of-the-art.
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2512.02552 [cs.CL]
  (or arXiv:2512.02552v2 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2512.02552
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arXiv-issued DOI via DataCite

Submission history

From: Francesco Paolo Savatteri [view email] [via CCSD proxy]
[v1] Tue, 2 Dec 2025 09:24:16 UTC (30 KB)
[v2] Tue, 10 Mar 2026 16:50:18 UTC (123 KB)
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