The Role of Context in Detecting Previously Fact-Checked Claims (2022.findings-naacl)
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| 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 . |
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| 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 . |
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| Challenge: | Recent years have brought us a proliferation of false claims online, which spread fast . fact-checkers have been using automated fact-finding to verify claims . |
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| Challenge: | Existing systems to automate fact-checking lack credibility in the eyes of the users. |
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| Challenge: | Existing work on automatic fact-checking relies on unstructured data and large language models to produce fact- check verdicts and explanations. |
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Automated Fact Checking: Task Formulations, Methods and Future Directions (C18-1)
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| 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. |
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| Challenge: | Existing methods for document-level claim extraction focus on identifying and extracting claims from individual sentences. |
| Approach: | They propose a method for document-level claim extraction for fact-checking which aims to extract check-worthy claims from documents and decontextualise them so they can be understood out of context. |
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A Survey on Automated Fact-Checking (2022.tacl-1)
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| 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. |
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Explainable Automated Fact-Checking: A Survey (2020.coling-main)
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| 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. |
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The Intended Uses of Automated Fact-Checking Artefacts: Why, How and Who (2023.findings-emnlp)
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| 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. |
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Generating Fact Checking Explanations (2020.acl-main)
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| Challenge: | Existing work on automated fact checking is concerned with predicting the veracity of claims based on metadata, social network spread, language used in claims, and, more recently, evidence supporting or denying claims. |
| Approach: | They propose to combine the generation of justifications for verdicts on claims with the multi-task model to optimize both objectives at the same time rather than training them separately. |
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