SRM-FND is proposed, a self-reflective multimodal reasoning framework for short-video fake news detection that outperforms strong baselines, produces more reliable and interpretable predictions, and delivers noticeable improvements in cross-dataset performance.
Abstract
Recent fake news detection pipelines increasingly leverage large language models and vision-language models for reasoning-based analysis. However, several challenges remain open: improving reasoning quality through self-reflection without ground-truth chain-of-thought supervision, using improved reasoning to benefit downstream model fine-tuning, and connecting single-sample fraudulent-pattern discovery with cross-sample verification. We propose SRM-FND, a self-reflective multimodal reasoning framework for short-video fake news detection. SRM-FND develops higher-quality reasoning through contrastive deliberation, iterative root-cause diagnosis, and corrective prompt refinement. A Blind Analyst, Counter-Conclusion Reasoner, and Self-Consistency Arbiter collaboratively identify and retain discriminative rationales. The framework also incorporates dual-phase, topic-adaptive vision-language model fine-tuning to improve multimodal grounding and enable lightweight topic specialization. For uncertain cases, it performs confidence-driven cross-sample review by retrieving credible and suspicious co-event examples. Experiments on FakeSV and FakeTT show that SRM-FND outperforms strong baselines, produces more reliable and interpretable predictions, and delivers noticeable improvements in cross-dataset performance.
The proliferation of social media has created fertile ground for misinformation, a challenge further intensified by recent advances in generative artificial intelligence. Modern fake news increasingly takes the form of sophisticated multimodal campaigns, where synthetic images and stylistically manipulated text are jointly employed to evade existing detection systems. Despite substantial progress, real-world deployment of multimodal fake news detection models remains constrained by an ''impossible triangle'' of accuracy, inference efficiency, and robustness. To address these challenges, we propose DAR-Lite, a serial two-stage framework that rethinks the detection pipeline through explicit decoupling of representation denoising and contextual reasoning. In the first stage, a variational information bottleneck distills compact and noise-invariant semantic anchors from raw image--text inputs, reducing sensitivity to nuisance factors. In the second stage, an adaptive reasoning engine integrates retrieval-augmented verification, social credibility propagation, and dynamic propagation signals via gated cross-attention to perform structured reasoning over heterogeneous contexts. An auxiliary logical fallacy detection task further encourages reasoning beyond surface-level pattern matching. Extensive experiments on multiple large-scale benchmarks demonstrate that DAR-Lite consistently outperforms state-of-the-art methods, particularly under low false-positive constraints critical for real-world applications. Efficiency analyses further show that the proposed serial architecture achieves a favorable balance between detection performance and computational cost, making DAR-Lite suitable for practical, large-scale misinformation detection.
Maolin Wang, Ziting Mai, Zi-Chun Liu et al.· Proceedings of the 32nd ACM...· 0 citations
Results show that DeepScrub improves fraud review accuracy, reduces first-stage review workload, and provides traceable evidence for production risk-review workflows, showing that domain adaptation can matter more than model scale in this setting.
This study proposes Multi-source Evidence Consen- sus Verification (MECV), a post-hoc hallucination cor- rection framework for AI-generated news summariza- tion. Instead of depending on a single retrieval channel, MECV aggregates evidence from multiple heterogeneous sources, including the source document, Wikipedia, and open-web retrieval. The framework further incorporates a multi-LLM jury mechanism that estimates factual reliabil- ity through contradiction-aware consensus scoring across verifier models. Claims identified as potentially unsup- ported are revised through iterative minimal-edit refine- ment. The proposed framework is evaluated on the SummEd- its benchmark using GPT-4o-mini and DeepSeek-Chat as the verifier jury, with Qwen-Plus as the orchestra- tor. Experimental results show that MECV improves fac- tual consistency while preserving the semantic structure of the original summaries. The findings further suggest that agreement across heterogeneous evidence sources can serve as a useful signal for identifying factual uncertainty in AI-generated summaries, including in information- sensitive domains such as financial news aggregation. This study contributes to research on trustworthy AI and automated journalism by introducing a multi-source verification framework for hallucination correction and demonstrating the value of consensus-based verification for improving factual reliability in AI-generated news summarization.
Fake news detection remains challenging because online news is often accompanied by noisy user responses, heterogeneous textual signals, and limited decision transparency. This paper presents ExplainFake, a comment-aware and interpretable detection framework built on a multi-pointer co-attention strategy. The model first encodes news sentences and user comments with a pre-trained language model to obtain contextual representations. It then selects multiple informative sentence-comment pairs and further analyzes word-level relations within the selected pairs, so that both sentence-level and token-level evidence can be derived. To combine the learned representations, multimodal factorized bilinear pooling is used to model cross-feature interactions before final classification. Experiments conducted on six datasets, including PolitiFact, GossipCop, Twitter15, Twitter16, Weibo20, and Weibo23, show that ExplainFake obtains competitive or better F1-scores than representative baselines. The ablation and interpretability results further indicate that the proposed modules contribute to both prediction performance and explanation quality.
An Liu, Jiang-Feng Zeng· International Conferences on...· 0 citations
Propagation structures provide crucial evidence for fake news detection, yet existing approaches primarily rely on supervised GNN-based models, which require substantial labeled data and exhibit limited generalization. Although large language models (LLMs) exhibit strong reasoning capabilities, directly feeding them raw propagation graphs creates a significant modality mismatch and severe information overload, making structure-aware reasoning unreliable in zero-shot and few-shot settings. To bridge this gap, we propose MAGER, a multi-agent genetic evolution framework that automatically discovers meta-paths optimized for LLM reasoning. By compressing complex propagation graphs into informative subgraphs, the evolved meta-paths alleviate both information overload and modality mismatch, enabling frozen LLMs to perform structure-aware veracity reasoning. We further introduce a graph in-context learning strategy that retrieves semantically and structurally similar demonstrations to strengthen classification and reasoning. Extensive experiments show that MAGER substantially improves frozen LLMs as standalone fake news detectors in data-efficient settings. Our code is available at https://github.com/SenticNet/MAGER.
Zi-Yi Zhou, Xiao-Ming Zhang, Hui Pang et al.· 0 citations
With the rapid growth of short video platforms, the spread of fake news in short video format has become increasingly complex and deceptive. While existing text/image fact-checking methods fail to identify multiple fake claims within a single video, video fake news detection methods do not provide fine-grained evidence-based analysis. To address this gap, we propose a novel task: Fine-grained Fact-Checking Report Generation for Short Videos. Given a short video containing textual, visual, and audio modalities, the goal is to automatically generate a structured report that identifies specific fake claims and provides detailed analyses based on external evidence. We construct a benchmark dataset annotated by domain experts, along with fine-grained evaluation questions. Furthermore, we propose Video Multi-agent Fine-grained Fact-checking (VMFF), a training-free multi-agent framework that simulates the workflow of professional fact-checkers through three modules: (1) Video Understanding, (2) Task Decomposition, and (3) Retrieval, Reasoning, and Generation. Experimental results demonstrate its effectiveness in fine-grained fact-checking report generation.
Jing-Zhe Liu· Poster Volume 0008 The 2026...· 0 citations
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