FakeNewsCorpus
A dataset of millions of news articles labeled by credibility type for training fake news detection algorithms.
What is FakeNewsCorpus?
Fake News Corpus is an open-source dataset of millions of news articles labeled by credibility type, created by scraping domains from OpenSources.co and supplementing with reliable sources. It's designed specifically for training deep learning algorithms to recognize fake news and other types of unreliable content. The dataset includes 11 categories ranging from fake news and satire to credible sources, formatted as CSV with rich metadata fields.
Machine learning researchers and data scientists working on fake news detection, natural language processing, and credibility analysis algorithms. Academic institutions and organizations developing content moderation or media literacy tools.
Provides a large-scale, pre-labeled dataset specifically curated for fake news detection research, saving researchers the immense effort of collecting and categorizing news articles manually. The inclusion of balanced classes and multiple credibility types makes it more suitable for training robust classification models than smaller or less diverse datasets.
Overview
A dataset of millions of news articles scraped from a curated list of data sources.
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