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

TL;DR

The paper introduces NELA-GT-2020, a comprehensive dataset of nearly 1.8 million news articles from diverse sources, including embedded tweets and veracity labels, to facilitate research on misinformation in news.

Contribution

It provides an updated, large-scale multi-label news dataset with embedded tweets and source-level veracity labels for misinformation analysis.

Findings

Contains 1.8 million news articles from 519 sources

Includes embedded tweets for added context

Provides source-level veracity labels from MBFC

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.