Data Annotation Methodologies for Fake News

Abstract

With the development of technology, information dissemination has become faster and more convenient. Fake news has drawn much attention due to its characteristics, such as rapid spread, strong disguise ability, and great harm. The performance of existing fake news detection models is highly dependent on the quality of training datasets. It is crucial to construct high-quality and lower-cost training datasets. The research progress of fake news dataset construction is systematically reviewed in this paper. Firstly, the categories and definition of fake news and the summary of existing mainstream datasets for detecting fake news are reviewed in this paper. Secondly, for traditional text news and newly derived multimodal news, the advantages and disadvantages of the existing annotation technologies are analyzed starting from the three aspects of traditional manual annotation, semi-automated annotation, and dynamic annotation. Finally, future research directions are proposed to address the problems of current datasets in dynamic annotation, multimodal fusion, and cross-domain generalization. High-quality datasets can effectively promote the development of fake news detection technology to meet the challenges of the increasingly complex network information environment.