Pith. sign in

REVIEW 3 cited by

NELA-GT-2020: A Large Multi-Labelled News Dataset for The Study of Misinformation in News Articles

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2102.04567 v1 pith:D4N2JWQE submitted 2021-02-08 cs.CY

Maurício Gruppi , Benjamin D. Horne , Sibel Adal{i} This is my paper · ORCID
classification cs.CY
keywords newsdatasetnela-gt-2020sourcesarticlescollectednela-gt-2019adding
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

In this paper, we present an updated version of the NELA-GT-2019 dataset, entitled NELA-GT-2020. NELA-GT-2020 contains nearly 1.8M news articles from 519 sources collected between January 1st, 2020 and December 31st, 2020. Just as with NELA-GT-2018 and NELA-GT-2019, these sources come from a wide range of mainstream news sources and alternative news sources. Included in the dataset are source-level ground truth labels from Media Bias/Fact Check (MBFC) covering multiple dimensions of veracity. Additionally, new in the 2020 dataset are the Tweets embedded in the collected news articles, adding an extra layer of information to the data. The NELA-GT-2020 dataset can be found at https://doi.org/10.7910/DVN/CHMUYZ.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. On the Effectiveness of Fact Checking Information from Politically Congruent and Incongruent Large Language Models

    cs.CY 2026-07 conditional novelty 6.0 of 10

    LLM fact-checkers shift trust in political headlines across partisan lines, with perceived chatbot politics mattering only for politically distant true headlines.

  2. Stop using Media Bias/Fact Check in research

    cs.SI 2026-07 unverdicted novelty 6.0 of 10

    Media Bias/Fact Check fails academic rigor standards and should not be used in research because it presents political outcomes as neutral facts about media quality.

  3. Dataset of News Articles with Provenance Metadata for Media Relevance Assessment

    cs.CL 2025-06 conditional novelty 6.0 of 10

    A new benchmark dataset and two tasks let researchers test whether AI systems can judge if a news image's recorded location and date match the article, with current chatbots scoring 64-81% on location but 42-58% on date.

Pith tools