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Related Experiment Videos

GC2MFND: Multi-Granularity Conflict and Domain-Guided Calibration for Multimodal Fake News Detection.

Yanming Sun1, Mingyue Zhang2, Fujun Zhang3

  • 1School of Transportation, Shandong University of Science and Technology, Qingdao 266590, China.

Entropy (Basel, Switzerland)
|June 26, 2026
PubMed
Summary
This summary is machine-generated.

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This study introduces a new model for detecting multimodal fake news across different domains. The proposed method enhances accuracy by focusing on conflicting information between text and images, outperforming existing techniques.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Data Science

Background:

  • Multimodal fake news is prevalent on social media, posing challenges for detection.
  • Existing methods often fail to capture cross-modal conflicts and domain-specific nuances.

Purpose of the Study:

  • To propose a novel model for multi-domain fake news detection.
  • To address limitations in current methods regarding cross-modal conflict extraction and domain dependency.

Main Methods:

  • Developed a Multi-Granularity Conflict and Domain-Guided Calibration for Multimodal Fake News Detection (GC²MFND) model.
  • Incorporated domain-aware multi-granularity conflict extraction and domain-guided multimodal feature calibration.
  • Utilized domain-adaptive aggregation, multi-view evidence integration, and supervised contrastive learning.
Keywords:
fake news detectionmulti-domain learningmultimodal learning

Related Experiment Videos

Main Results:

  • GC²MFND demonstrated superior performance compared to existing multi-domain baseline methods.
  • Achieved high accuracy rates: 95.3% on Weibo, 95.7% on Weibo21, and 81.2% on FineFake.
  • Showcased improvements of 1.1%, 1.2%, and 1.4% over baselines on respective datasets.

Conclusions:

  • The proposed GC²MFND model effectively detects multimodal fake news across domains.
  • The method successfully addresses challenges related to cross-modal conflicts and domain-specific features.
  • Results indicate significant advancements in multi-domain fake news detection accuracy.