Evidence map›Paper›PMID 39989469›Full record

ArticleEPJ data science2025

Weakly supervised veracity classification with LLM-predicted credibility signals.

João A Leite, Olesya Razuvayevskaya, Kalina Bontcheva, Carolina Scarton

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in EPJ data science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

João A LeiteDepartment of Computer Science, The University of Sheffield, Regent Court, 211 Portobello Street, Sheffield, S1 4DP United Kingdom.
Olesya RazuvayevskayaDepartment of Computer Science, The University of Sheffield, Regent Court, 211 Portobello Street, Sheffield, S1 4DP United Kingdom.
Kalina BontchevaDepartment of Computer Science, The University of Sheffield, Regent Court, 211 Portobello Street, Sheffield, S1 4DP United Kingdom.
Carolina ScartonDepartment of Computer Science, The University of Sheffield, Regent Court, 211 Portobello Street, Sheffield, S1 4DP United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Credibility signals represent a wide range of heuristics typically used by journalists and fact-checkers to assess the veracity of online content. Automating the extraction of credibility signals presents significant challenges due to the necessity of training high-accuracy, signal-specific extractors, coupled with the lack of sufficiently large annotated datasets. This paper introduces Pastel ( Supplementary Information: The online version contains supplementary material available at 10.1140/epjds/s13688-025-00534-0.

Indexed as

Credibility signalsLarge language modelsVeracity classificationWeak supervision

Identifiers

PMID39989469
PMCPMC11845407

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.