Papers with claim detection
ViClaim: A Multilingual Multilabel Dataset for Automatic Claim Detection in Videos (2025.emnlp-main)
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| Challenge: | Existing efforts in misinformation detection focus on written text, leaving a significant gap in addressing the complexity of spoken text in video transcripts. |
| Approach: | They propose to annotate video transcripts in three languages and six topics using a custom annotation tool. |
| Outcome: | The proposed tool shows strong cross-validation performance but challenges for generalization to unseen topics. |
IMHO Fine-Tuning Improves Claim Detection (N19-1)
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| Challenge: | Empirical results show that using this approach improves the state of art performance across four benchmark argumentation data sets by an average of 4 absolute F1 points in claim detection. |
| Approach: | They propose to fine-tune a language model using a Reddit corpus of opinionated claims and use the internet acronyms IMO/IMHO to identify claims. |
| Outcome: | The proposed approach improves state of art performance across four benchmark argumentation data sets by an average of 4 absolute F1 points. |
AQE: Argument Quadruplet Extraction via a Quad-Tagging Augmented Generative Approach (2023.findings-acl)
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| Challenge: | Argument mining involves multiple subtasks, but each one is insufficient for understanding argumentative structure and reasoning process. |
| Approach: | They propose a quadruplet extraction task that extracts four argumentative components . they use a generative quadragging module to augment the training of the generative framework . |
| Outcome: | The proposed method can extract arguments from a large-scale dataset. |
MM-Claims: A Dataset for Multimodal Claim Detection in Social Media (2022.findings-naacl)
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Gullal Singh Cheema, Sherzod Hakimov, Abdul Sittar, Eric Müller-Budack, Christian Otto, Ralph Ewerth
| Challenge: | Using image and text, we investigate the role of image and texts in fake news detection . claim detection is a step in fighting misinformation and as a precursor to prioritize potentially false information for fact-checking. |
| Approach: | They propose a dataset that consists of tweets and corresponding images for claim detection . they evaluate strong unimodal and multimodal baselines and analyze drawbacks of current models . |
| Outcome: | The proposed dataset evaluates strong unimodal and multimodal baselines and examines drawbacks of existing models. |
AFaCTA: Assisting the Annotation of Factual Claim Detection with Reliable LLM Annotators (2024.acl-long)
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| Challenge: | generative AI is a counter-measure to misinformation, but factual claim detection suffers from inconsistency in definitions and high cost of manual annotation. |
| Approach: | They propose a framework that assists in the annotation of factual claims with the help of large language models. |
| Outcome: | The proposed framework can be used to annotate factual claims with the help of large language models and can work with or without expert supervision. |
A Zero-Shot Claim Detection Framework Using Question Answering (2022.coling-1)
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| Challenge: | Existing claims detection frameworks are portability to emerging events and low-resource training data settings. |
| Approach: | They propose a claim detection framework that leverages zero-shot Question Answering to solve sub-tasks such as topic filtering, claim object detection, and claimer detection. |
| Outcome: | The proposed framework outperforms baselines on the NewsClaims benchmark. |