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        <title>The Missing Parts: Augmenting Fact Verification with Half Truth Detection</title>
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    <abstract>Fact verification systems typically assess whether a claim is supported by retrieved evidence, assuming that truthfulness depends solely on what is stated. However, many real-world claims are half-truths, factually correct yet misleading due to the omission of critical context. Existing models struggle with such cases, as they are not designed to reason about omitted information. We introduce the task of half-truth detection, and propose PolitiFact-Hidden, a new benchmark with 15k political claims annotated with sentence-level evidence alignment and inferred claim intent. To address this challenge, we present TRACER, a modular re-assessment framework that identifies omission-based misinformation by aligning evidence, inferring implied intent, and estimating the causal impact of hidden content. TRACER can be integrated into existing fact-checking pipelines and consistently improves performance across multiple strong baselines. Notably, it boosts Half-True classification F1 by up to 16 points, highlighting the importance of modeling omissions for trustworthy fact verification. The benchmark and code are available via https://github.com/tangyixuan/TRACER.</abstract>
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%0 Conference Proceedings
%T The Missing Parts: Augmenting Fact Verification with Half Truth Detection
%A Tang, Yixuan
%A Wang, Jincheng
%A Tung, Anthony Kum Hoe
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F tang-etal-2025-missing
%X Fact verification systems typically assess whether a claim is supported by retrieved evidence, assuming that truthfulness depends solely on what is stated. However, many real-world claims are half-truths, factually correct yet misleading due to the omission of critical context. Existing models struggle with such cases, as they are not designed to reason about omitted information. We introduce the task of half-truth detection, and propose PolitiFact-Hidden, a new benchmark with 15k political claims annotated with sentence-level evidence alignment and inferred claim intent. To address this challenge, we present TRACER, a modular re-assessment framework that identifies omission-based misinformation by aligning evidence, inferring implied intent, and estimating the causal impact of hidden content. TRACER can be integrated into existing fact-checking pipelines and consistently improves performance across multiple strong baselines. Notably, it boosts Half-True classification F1 by up to 16 points, highlighting the importance of modeling omissions for trustworthy fact verification. The benchmark and code are available via https://github.com/tangyixuan/TRACER.
%U https://aclanthology.org/2025.emnlp-main.1724/
%P 33967-33984

Markdown (Informal)

[The Missing Parts: Augmenting Fact Verification with Half Truth Detection](https://aclanthology.org/2025.emnlp-main.1724/) (Tang et al., EMNLP 2025)

• The Missing Parts: Augmenting Fact Verification with Half Truth Detection (Tang et al., EMNLP 2025)

ACL

• Yixuan Tang, Jincheng Wang, and Anthony Kum Hoe Tung. 2025. The Missing Parts: Augmenting Fact Verification with Half Truth Detection. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 33967–33984, Suzhou, China. Association for Computational Linguistics.