The Intended Uses of Automated Fact-Checking Artefacts: Why, How and Who (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Automated fact-checking is often presented as an epistemic tool fact-seekers, social media consumers, and other stakeholders can use to fight misinformation. |
| Approach: | They analyse 100 highly-cited papers and annotate epistemic elements related to intended use, i.e., means, ends, and stakeholders. |
| Outcome: | The proposed strategies are often left out of the literature and lack empirical backing. |
Similar Papers
Explainable Automated Fact-Checking: A Survey (2020.coling-main)
Copied to clipboard
| Challenge: | Steady progress has been made in fact-checking and its orthogonal tasks. |
| Approach: | They propose to use fact-checking explanations to explain predictions by comparing existing explanations against desirable properties to find out what makes for good explanations. |
| Outcome: | The proposed explanations are compared against desirable properties and show how they may lead to improvements in the research area. |
Automated Fact Checking: Task Formulations, Methods and Future Directions (C18-1)
Copied to clipboard
| Challenge: | Recent research on fact checking has focused on misinformation . however, relevant papers and articles have been published in research communities that are unaware of each other and use inconsistent terminology. |
| Approach: | They propose avenues for future NLP research on automated fact checking . they highlight the use of evidence as an important distinguishing factor . |
| Outcome: | The proposed methods unify the task formulations and methodologies across papers and authors. |
A Survey on Automated Fact-Checking (2022.tacl-1)
Copied to clipboard
| Challenge: | Fact-checking is an essential task in journalism due to the speed with which information and misinformation can spread in the media ecosystem. |
| Approach: | They propose to use natural language processing to automate fact-checking by identifying common concepts and defining definitions. |
| Outcome: | The proposed method can predict the veracity of claims using natural language processing, machine learning, and databases. |
Automated Justification Production for Claim Veracity in Fact Checking: A Survey on Architectures and Approaches (2024.acl-long)
Copied to clipboard
| Challenge: | Current research focuses on predicting claim veracity through metadata analysis and language scrutiny, with an emphasis on justifying verdicts. |
| Approach: | They propose a comprehensive taxonomy for categorizing works based on various criteria and propose scalable methodologies for improving fact-checking explainability. |
| Outcome: | The proposed taxonomy identifies challenges while proposing future directions in fact-checking explainability. |
Task-Oriented Automatic Fact-Checking with Frame-Semantics (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing work on automatic fact-checking relies on unstructured data and large language models to produce fact- check verdicts and explanations. |
| Approach: | They propose a new paradigm for automatic fact-checking that leverages frame semantics to enhance the structured understanding of claims and guide the process of fact- checking them. |
| Outcome: | The proposed paradigm improves evidence retrieval and explainability for fact-checking by leveraging frame semantics. |
Decide less, communicate more: On the construct validity of end-to-end fact-checking in medicine (2026.findings-acl)
Copied to clipboard
Sebastian Antony Joseph, Lily Chen, Barry Wei, Michael Mackert, Iain James Marshall, Paul Pu Liang, Ramez Kouzy, Byron C Wallace, Junyi Jessy Li
| Challenge: | Evidence-based medicine connects to every individual, yet the nature of it is highly technical . e-fact-checking systems that connect to medical decisions are largely unused . we examine how clinical experts verify real claims from social media . |
| Approach: | They propose that fact-checking should be approached as an interactive communication problem . they argue that social media and AI have made medical knowledge accessible . |
| Outcome: | The proposed method is based on the work of a clinical expert on social media . it reveals that the method is difficult to connect claims to clinical trials . |
Social Good or Scientific Curiosity? Uncovering the Research Framing Behind NLP Artefacts (2025.emnlp-main)
Copied to clipboard
| Challenge: | Recent studies show that few papers explicitly identify key stakeholders, intended uses, or appropriate contexts. |
| Approach: | They propose to automate analysis of NLP research by extracting key elements and linking them through interpretable rules and contextual reasoning. |
| Outcome: | The proposed system improves on two domains of fact-checking and hate speech detection. |
The Role of Context in Detecting Previously Fact-Checked Claims (2022.findings-naacl)
Copied to clipboard
| Challenge: | Recent years have seen the proliferation of disinformation and fake news online. |
| Approach: | They propose to model the context of a political debate and the contexts of the document describing the fact-checked claim. |
| Outcome: | The proposed model improves on the state-of-the-art model by modeling the context of the claim . the experimental results show that the model can provide 10+ points of improvement over the state of the art model . |
That is a Known Lie: Detecting Previously Fact-Checked Claims (2020.acl-main)
Copied to clipboard
| Challenge: | a large number of fact-checked claims have been accumulated over the years . despite the importance of fact checking, it has been largely ignored by the research community . |
| Approach: | They propose to automate fact-checking by focusing on claims that have already been fact-tested . they propose to use specialized datasets to compare different methods . |
| Outcome: | The proposed task shows that it improves over state-of-the-art methods. |
Scientific Fact-Checking: A Survey of Resources and Approaches (2023.findings-acl)
Copied to clipboard
| Challenge: | Fact-checking is the task of assessing the veracity of factual claims based on credible evidence and background knowledge. |
| Approach: | They propose to automate scientific fact-checking using natural language processing to assess the veracity of factual claims based on credible evidence and background knowledge. |
| Outcome: | The proposed methods can help combat the spread of misinformation and help individuals understand new scientific breakthroughs. |