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Human-Like Multimodal Fake News Detection via Reflective Summarization and Large-Small Model Collaboration.

Boyue Wang, Yihan Gao, Tengfei Liu

IEEE Transactions on Neural Networks and Learning Systems

|May 13, 2026

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This study introduces a novel framework for multimodal fake news detection, enhancing accuracy by integrating large vision-language models (LVLMs) with smaller models for deeper context analysis and improved reliability.

Area of Science:

Artificial Intelligence
Natural Language Processing
Computer Vision

Background:

Multimodal fake news detection struggles with background context, emotional tone, and plausibility.
Existing methods face challenges in deep semantic analysis of news cues.

Purpose of the Study:

To propose a human-like collaborative framework for improved multimodal fake news detection.
To leverage large vision-language models (LVLMs) for enhanced analysis of news content.

Main Methods:

Utilized LVLMs with chain-of-thought (CoT) prompting for comprehensive news analysis (image credibility, text analysis, factual verification).
Implemented reflective summarization to condense lengthy analytical outputs from multimodal inputs.
Developed a progressive fusion mechanism for collaboration between large and small models.

Related Experiment Videos

Main Results:

The proposed framework demonstrated superior performance on benchmark datasets.
Achieved consistent outperformance against state-of-the-art baselines in fake news detection.
Showcased the effectiveness and robustness of the human-like collaborative approach.

Conclusions:

The novel framework significantly enhances multimodal fake news detection accuracy and reliability.
Integrating LVLMs and a collaborative model architecture offers a promising direction for combating sophisticated fake news.
The method provides a robust solution for analyzing complex multimodal news content.