Automated Fact-Checking: A Survey
Xia Zeng, Amani S. Abumansour, Arkaitz Zubiaga

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.
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Taxonomy
TopicsTopic Modeling · Misinformation and Its Impacts · Sentiment Analysis and Opinion Mining
