Automated Fact-Checking: A Survey

TL;DR

This survey reviews recent advances in automated fact-checking, focusing on claim detection and validation, highlighting datasets, pipelines, and NLP methods to combat online misinformation.

Contribution

It provides a comprehensive overview of NLP-based automated fact-checking research, emphasizing recent datasets, pipelines, and methodological developments.

Findings

Significant progress in claim detection and validation techniques.

Development of specialized datasets for fact-checking.

Identification of challenges and future directions in automated fact-checking.

Abstract

As online false information continues to grow, automated fact-checking has gained an increasing amount of attention in recent years. Researchers in the field of Natural Language Processing (NLP) have contributed to the task by building fact-checking datasets, devising automated fact-checking pipelines and proposing NLP methods to further research in the development of different components. This paper reviews relevant research on automated fact-checking covering both the claim detection and claim validation components.