Papers with fact-checking systems
Active PETs: Active Data Annotation Prioritisation for Few-Shot Claim Verification with Pattern Exploiting Training (2023.findings-eacl)
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| Challenge: | Recent work on few-shot classification has addressed the issue of data prioritisation of unlabelled data. |
| Approach: | They propose a weighted approach that uses a set of pattern-exploiting training models to actively select unlabelled data as candidates for annotation. |
| Outcome: | The proposed approach shows consistent improvement over baseline methods on two technical fact-checking datasets and using six different pretrained language models. |
QACheck: A Demonstration System for Question-Guided Multi-Hop Fact-Checking (2023.emnlp-demo)
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| Challenge: | Existing fact-checking systems lack transparency in their decision-making process, making it difficult for users to comprehend their reasoning process. |
| Approach: | They propose a Question-guided Multi-hop Fact-Checking system which asks a series of questions critical for verifying a claim. |
| Outcome: | The proposed model provides a comprehensive report detailing its reasoning process, guided by a sequence of questions, answer pairs, and the source of evidence supporting each question. |
COMMUNITYNOTES: A Dataset for Exploring the Helpfulness of Fact-Checking Explanations (2026.findings-eacl)
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| Challenge: | X, Meta, and TikTok are experimenting with community-based factchecking . community-driven verification is a way to provide explanatory notes that clarify why a post might be misleading . |
| Approach: | They propose a framework that optimizes the helpfulness of explanatory notes and the reason for this by automatically optimizing the prompt definitions. |
| Outcome: | The proposed framework improves helpfulness and reason prediction on 104k posts with user-provided notes and helpfulness labels. |
SemViQA: A Semantic Question Answering System for Vietnamese Information Fact-Checking (2026.acl-industry)
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| Challenge: | Existing methods struggle with semantic ambiguity, homonyms, and complex linguistic structures, often trading accuracy for efficiency. |
| Approach: | They propose a Vietnamese fact-checking framework that integrates SER and TVC to achieve 78.97% strict accuracy. |
| Outcome: | The proposed framework achieves state-of-the-art accuracy with 78.97% strict accuracy on ISE-DSC01 and 80.82% on ViWikiFC while maintaining competitive accuracy. |
Constructing a Japanese Verdict Prediction Dataset for Fact-Checking of LLM-Generated Texts (2026.acl-srw)
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Miwa Masano, Hirokazu Kiyomaru, Atsushi Keyaki, Kaito Horio, Rei Minamoto, Ribeka Keyaki, Kouta Nakayama, Hideyuki Tachibana, Daisuke Kawahara
| Challenge: | Text generated by Large Language Models (LLMs) may contain plausible but incorrect information known as hallucinations. |
| Approach: | They extend the label set for verdict prediction to capture claim-evidence relationships humans would commonly interpret as supported or refuted. |
| Outcome: | The proposed system improves F1 by 4 percentage points compared to baseline. |
Comparing Knowledge Sources for Open-Domain Scientific Claim Verification (2024.eacl-long)
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| Challenge: | Existing systems for fact-checking scientific claims assume that the documents containing the evidence are already provided and annotated or contained in a limited corpus. |
| Approach: | They perform an array of experiments to test the performance of open-domain claim verification systems on four datasets of biomedical and health claims in different settings. |
| Outcome: | The proposed system performs better with biomedical and health claims, while Wikipedia is more suited for everyday health concerns. |
CHEF: A Pilot Chinese Dataset for Evidence-Based Fact-Checking (2022.naacl-main)
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| Challenge: | CHEF dataset provides evidence retrieval over non-English claims . e-fact-checking is a time-consuming task, which can take journalists several hours or days. |
| Approach: | They construct a dataset of 10K real-world claims that is based on annotated evidence retrieved from the Internet. |
| Outcome: | The proposed dataset provides evidence retrieval as a latent variable and can be used to train and reason over non-English claims. |
Predicting Factuality of Reporting and Bias of News Media Sources (D18-1)
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| Challenge: | a new study examines the factuality of news media and its biases . social media has democratized content creation and spread information online . |
| Approach: | They propose to characterize entire news media to predict factuality and bias . they experiment with news websites and a set of features derived from their content . |
| Outcome: | The proposed model shows that the features of news websites perform better than baseline . the results show that the feature types are important for fact-checking systems . |
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. |
| Outcome: | The proposed explanations are compared against desirable properties and show how they may lead to improvements in the research area. |
Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News (2020.emnlp-main)
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| 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. |
RU22Fact: Optimizing Evidence for Multilingual Explainable Fact-Checking on Russia-Ukraine Conflict (2024.lrec-main)
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| Challenge: | Existing methods to verify factuality of claims do not provide sufficient evidence for explainable fact-checking systems. |
| Approach: | They propose a method to automatically retrieve and summarize evidence from the Web and a novel multilingual explainable fact-checking dataset on the Russia-Ukraine conflict in 2022. |
| Outcome: | The proposed method can retrieve and summarize evidence from the Web and generate explanations in 16 languages. |
Triple-R: Automatic Reasoning for Fact Verification Using Language Models (2024.lrec-main)
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| Challenge: | Existing methods for fact-checking lack external sources and human-understandable explanations for decision-making . existing methods lack external knowledge sources and explanations . |
| Approach: | They propose a framework that uses the Web as an external knowledge source to retrieve relevant evidence for claims and generates reasons based on the retrieved evidence for datasets lacking explanations. |
| Outcome: | The proposed method improves the transparency and interpretability of fact-checking systems by providing human-understandable explanations for decision-making processes. |