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Modality Perception Learning-Based Determinative Factor Discovery for Multimodal Fake News Detection.

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|September 20, 2024

Summary

This summary is machine-generated.

The new MoPeD model effectively detects fake news by analyzing both text and image features, addressing limitations in current methods. It enhances fake news detection by focusing on modality heterogeneity and discovering key determinative factors.

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Area of Science:

Computer Science
Artificial Intelligence
Information Science

Background:

Fake news dissemination poses significant risks to public safety and societal opinion.
Existing multimodal fake news detection methods often overlook modal heterogeneity, limiting their ability to identify crucial determinative information.
There is a need for advanced models that can effectively handle diverse information within fake news articles.

Purpose of the Study:

To propose a novel model, modality perception learning-based determinative factor discovery (MoPeD), for enhanced fake news detection.
To address the limitations of existing methods by focusing on modal heterogeneity and extracting decisive information.
To improve the accuracy and robustness of fake news detection systems.

Main Methods:

The MoPeD model integrates a dual encoding module combining a contrastive language-image pre-training (CLIP) encoder and a modal-specific encoder.
A multilevel cross-modality fusion module is employed to handle modality heterogeneity and comprehend implicit meanings between text and image.
A modality perception learning module dynamically emphasizes decisive features based on cross-modal content heterogeneity scores.

Main Results:

Experimental evaluations on three public datasets demonstrate the superiority of the MoPeD model over state-of-the-art fake news detection methods.
The model effectively extracts determinants from both unimodal and multimodal features.
MoPeD shows improved performance in identifying fake news by considering modality-specific and cross-modal information.

Conclusions:

The proposed MoPeD model offers a significant advancement in fake news detection by effectively addressing modal heterogeneity.
The modality perception learning approach allows for adaptive emphasis on decisive features, leading to more accurate detection.
MoPeD provides a robust framework for uncovering determinative factors in fake news, outperforming existing approaches.