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January 1, 2026International Journal of Autonomous and Adaptive Communications Systems

Cross-Modal Attention for Fake News Detection: Integrating Text and Image Features with Multi-Modal Fusion and Advanced Methods

Yamini Devi Jonnala J. Sirisha Devi

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Key Points

The aim is to develop an advanced method that detects fake news by integrating text and image features using cross-modal attention.
Utilized cross-modal attention techniques to assess text and image data.
Implemented multi-modal fusion to enhance feature integration.
Conducted comparative analyses of detection accuracy.
Demonstrated improved accuracy in fake news detection compared to traditional methods.
Showed effectiveness of combining text and images for better classification results.

Implication

This research integrates text and image features for enhanced fake news detection, indicating effective multi-modal approaches.

Abstract

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Cite This Study

Jonnala et al. (2026) studied this question.

synapsesocial.com/papers/69bf8692f665edcd009e8e00 https://doi.org/10.1504/ijaacs.2026.10077164

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Keep reading

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1Multimodal fake news detection through intra-modality feature aggregation and inter-modality semantic fusion2024 · 22 citations
2Multimodal Inverse Attention Network with Intrinsic Discriminant Feature Exploitation for Fake News Detection2025 · 7 citations
3A unified approach for fake news detection: dynamic fusion of uni-modal and multi-modal featuresOriginal Research · 2026
4Multimodal Social Media Fake News Detection Based on 1D-CCNet Attention Mechanism2024 · 14 citations
5User Comment-Guided Cross-Modal Attention for Interpretable Multimodal Fake News Detection2025 · 3 citations

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