Dataset of Fake News Detection and Fact Verification: A Survey

TL;DR

This survey comprehensively reviews 118 public datasets related to fake news detection and fact verification, highlighting their characteristics, utilization, challenges, and research opportunities to aid future studies.

Contribution

It provides a detailed large-scale overview of datasets for fake news research, aiding researchers in selecting suitable resources and addressing dataset construction challenges.

Findings

Reviewed 118 datasets across multiple fake news tasks

Identified key challenges in dataset construction

Highlighted research opportunities in fake news dataset development

Abstract

The rapid increase in fake news, which causes significant damage to society, triggers many fake news related studies, including the development of fake news detection and fact verification techniques. The resources for these studies are mainly available as public datasets taken from Web data. We surveyed 118 datasets related to fake news research on a large scale from three perspectives: (1) fake news detection, (2) fact verification, and (3) other tasks; for example, the analysis of fake news and satire detection. We also describe in detail their utilization tasks and their characteristics. Finally, we highlight the challenges in the fake news dataset construction and some research opportunities that address these challenges. Our survey facilitates fake news research by helping researchers find suitable datasets without reinventing the wheel, and thereby, improves fake news studies in depth.