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. |
Similar Papers
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 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. |
Pushing the Frontiers of Scientific Fact-Checking: The SCINLP Dataset (2026.findings-eacl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are increasingly being used to understand how scientific research evolves, drawing growing interest from the research community. |
| Approach: | They propose a scientific fact-checking dataset, SCINLP, tailored to the NLP domain that verifies the veracity of scientific research questions across varying rationale contexts. |
| Outcome: | The proposed framework examines scientific claims and research focus from a curated collection of influential and reputable NLP papers published between 2000 and 2024. |
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. |
Fact or Fiction: Verifying Scientific Claims (2020.emnlp-main)
Copied to clipboard
David Wadden, Shanchuan Lin, Kyle Lo, Lucy Lu Wang, Madeleine van Zuylen, Arman Cohan, Hannaneh Hajishirzi
| Challenge: | SciFact is a dataset of 1.4K expert-written scientific claims paired with evidence-containing abstracts annotated with labels and rationales. |
| Approach: | They construct a dataset of 1.4K scientific claims paired with evidence-containing abstracts annotated with labels and rationales to test their system. |
| Outcome: | The proposed system can verify claims related to COVID-19 by identifying evidence from the CORD-19 corpus. |
Explainable Automated Fact-Checking for Public Health Claims (2020.emnlp-main)
Copied to clipboard
| Challenge: | a few blind spots exist in the state-of-the-art in fact-checking for political claims. |
| Approach: | They propose to use a dataset of 11.8K claims to explain fact-check labels for claims . they define and evaluate three coherence properties of explanation quality with humans . |
| Outcome: | The proposed model can be trained on in-domain data and evaluates its coherence properties with humans and computationally. |
HealthFC: Verifying Health Claims with Evidence-Based Medical Fact-Checking (2024.lrec-main)
Copied to clipboard
| Challenge: | determining the trustworthiness of online medical content is challenging in the digital age . fact-checking is an approach to assess the veracity of factual claims . a new dataset is presented to help advance automated fact- checking . |
| Approach: | They propose a dataset that assesses the veracity of factual claims using evidence from credible sources. |
| Outcome: | The proposed dataset can be used for automated fact-checking tasks. |
Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News (2020.emnlp-main)
Copied to clipboard
| Challenge: | fabricated stories and hoaxes are still pervading our cyberspace. |
| Approach: | They propose a framework to search for fact-checking articles that address the content of an original tweet that may contain misinformation posted by online users. |
| Outcome: | The proposed framework can detect and disseminate fake news on real-world datasets and warn fake news posters and online users about misinformation. |
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. |
FactCorp: A Corpus of Dutch Fact-checks and its Multiple Usages (2020.lrec-1)
Copied to clipboard
| Challenge: | Fact-checking information before publication has long been a core task for journalists, but recent times have seen the emergence of dedicated news items specifically aimed at fact-checks after publication. |
| Approach: | They propose to study fact-checks from a corpus linguistic perspective and to create a textual corpus that contains 1,974 fact- checks from three major Dutch newspapers. |
| Outcome: | The proposed method can be applied to scientific communication landscapes and to the media. |