# NELA-GT-2018: A Large Multi-Labelled News Dataset for The Study of Misinformation in News Articles

arXiv: https://arxiv.org/abs/1904.01546 · HTML: https://mlsanity.com/paper/1904.01546/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2019.

## Built on by (most cited first, 26 of 26)

- [A Survey on Automated Fact-Checking](https://mlsanity.com/paper/2108.11896/) (2021)
- [End-to-End Multimodal Fact-Checking and Explanation Generation: A Challenging Dataset and Models](https://mlsanity.com/paper/2205.12487/) (2022)
- [Misinfo Reaction Frames: Reasoning about Readers' Reactions to News Headlines](https://mlsanity.com/paper/2104.08790/) (2021)
- [Fake News Detectors are Biased against Texts Generated by Large Language Models](https://mlsanity.com/paper/2309.08674/) (2023)
- [MN-DS: A Multilabeled News Dataset for News Articles Hierarchical Classification](https://mlsanity.com/paper/2212.12061/) (2022)
- [Credible, Unreliable or Leaked?: Evidence Verification for Enhanced Automated Fact-checking](https://mlsanity.com/paper/2404.18971/) (2024)
- [Reliability Estimation of News Media Sources: Birds of a Feather Flock Together](https://mlsanity.com/paper/2404.09565/) (2024)
- [Rewiring What-to-Watch-Next Recommendations to Reduce Radicalization Pathways](https://mlsanity.com/paper/2202.00640/) (2022)
- [LLM Teacher-Student Framework for Text Classification With No Manually Annotated Data: A Case Study in IPTC News Topic Classification](https://mlsanity.com/paper/2411.19638/) (2024)
- [Concept Drift Adaptation in Text Stream Mining Settings: A Systematic Review](https://mlsanity.com/paper/2312.02901/) (2023)
- [Fake News Detection: Comparative Evaluation of BERT-like Models and Large Language Models with Generative AI-Annotated Data](https://mlsanity.com/paper/2412.14276/) (2024)
- [Applying Automatic Text Summarization for Fake News Detection](https://mlsanity.com/paper/2204.01841/) (2022)
- [Fake News Detection: It's All in the Data!](https://mlsanity.com/paper/2407.02122/) (2024)
- [Yesterday's News: Benchmarking Multi-Dimensional Out-of-Distribution Generalization of Misinformation Detection Models](https://mlsanity.com/paper/2410.18122/) (2024)
- [Multiverse: Multilingual Evidence for Fake News Detection](https://mlsanity.com/paper/2211.14279/) (2022)
- [MPPFND: A Dataset and Analysis of Detecting Fake News with Multi-Platform Propagation](https://mlsanity.com/paper/2505.15834/) (2025)
- [LiveFact: A Dynamic, Time-Aware Benchmark for LLM-Driven Fake News Detection](https://mlsanity.com/paper/2604.04815/) (2026)
- [Infini-News: Efficiently Queryable Access to 1.3 Billion Processed Common Crawl News Articles](https://mlsanity.com/paper/2605.18337/) (2026)
- [ECOL: Early Detection of COVID Lies Using Content, Prior Knowledge and Source Information](https://mlsanity.com/paper/2101.05499/) (2021)
- [Leveraging Commonsense Knowledge on Classifying False News and Determining Checkworthiness of Claims](https://mlsanity.com/paper/2108.03731/) (2021)
- [Development of Fake News Model using Machine Learning through Natural Language Processing](https://mlsanity.com/paper/2201.07489/) (2022)
- [Fine-grained Czech News Article Dataset: An Interdisciplinary Approach to Trustworthiness Analysis](https://mlsanity.com/paper/2212.08550/) (2022)
- [WarCov -- Large multilabel and multimodal dataset from social platform](https://mlsanity.com/paper/2406.10255/) (2024)
- [The 2021 Tokyo Olympics Multilingual News Article Dataset](https://mlsanity.com/paper/2502.06648/) (2025)
- [Spectral Analysis of Fake News Propagation](https://mlsanity.com/paper/2605.13861/) (2026)
- [What Does 99% Accuracy Measure? A Reproducible Audit of Shortcut Learning in a Widely Used Fake News Corpus](https://mlsanity.com/paper/2609.25006/) (2026)

## Abstract (arXiv)

In this paper, we present a dataset of 713k articles collected between 02/2018-11/2018. These articles are collected directly from 194 news and media outlets including mainstream, hyper-partisan, and conspiracy sources. We incorporate ground truth ratings of the sources from 8 different assessment sites covering multiple dimensions of veracity, including reliability, bias, transparency, adherence to journalistic standards, and consumer trust. The NELA-GT-2018 dataset can be found at https://doi.org/10.7910/DVN/ULHLCB.