AFND: Arabic fake news dataset for the detection and classification of articles credibility.

ABSTRACT: The news credibility detection task has started to gain more attention recently due to the rapid increase of news on different social media platforms. This article provides a large, labeled, and diverse Arabic Fake News Dataset (AFND) that is collected from public Arabic news websites. This dataset enables the research community to use supervised and unsupervised machine learning algorithms to classify the credibility of Arabic news articles. AFND consists of 606912 public news articles that were scraped from 134 public news websites of 19 different Arab countries over a 6-month period using Python scripts. The Arabic fact-check platform, Misbar, is used manually to classify each public news source into credible, not credible, or undecided. Weak supervision is applied to label news article

SUBMITTER: Khalil A 

PROVIDER: S-EPMC9048144 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature