Dataset of Fake News Detection and Fact Verification: A Survey
This survey addresses the need for organized dataset resources in fake news research, but it is incremental as it compiles existing information without introducing new methods or data.
The paper surveyed 118 datasets for fake news detection and fact verification, categorizing them by tasks and characteristics to help researchers find suitable resources and improve studies.
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.