Fact-checking based fake news detection: a review
It provides a comprehensive reference for researchers in fake news detection, but is incremental as it synthesizes existing work without new methods or data.
This paper reviews fact-checking based fake news detection, summarizing tasks, algorithms, datasets, and experimental results, and identifies challenges for future research.
This paper reviews and summarizes the research results on fact-based fake news from the perspectives of tasks and problems, algorithm strategies, and datasets. First, the paper systematically explains the task definition and core problems of fact-based fake news detection. Second, the paper summarizes the existing detection methods based on the algorithm principles. Third, the paper analyzes the classic and newly proposed datasets in the field, and summarizes the experimental results on each dataset. Finally, the paper summarizes the advantages and disadvantages of existing methods, proposes several challenges that methods in this field may face, and looks forward to the next stage of research. It is hoped that this paper will provide reference for subsequent work in the field.