Multimodal Fake News Detection: When ResNet, GNN, and Fuzzy Logic Join Forces to Combat Information Epidemics

This article introduces an open-source project for multimodal fake news detection that integrates ResNet, Transformer, LSTM, Graph Neural Networks (GNN), and fuzzy logic. Addressing the problem that single-modal detection struggles to handle complex forgery methods, the project builds an interpretable AI system, offering new technical ideas for combating information epidemics.

In the information age dominated by social media, fake news has become a global digital plague, involving political manipulation, health rumors, and other fields. Traditional single-modal detection (text-only or image-only) struggles to handle complex forgeries that combine text and images, spurring the direction of multimodal fake news detection, which requires systems to simultaneously understand text semantics, analyze visual content, and capture contradictions between the two.

The core innovation of the project lies in its hybrid architecture, integrating five core components:

1. ResNet50: Extracts image authenticity, semantic content, and style features to identify deepfakes or edited images;
2. Transformer: Analyzes text semantics and identifies rumor language patterns (emotional wording, absolute assertions, etc.);
3. LSTM: Models local text structure and temporal features of propagation;
4. GNN: Analyzes news propagation paths, social relationship networks, and user interaction patterns to identify rumor propagation characteristics;
5. Fuzzy logic system: Reasons in a human-like way, fuses multimodal evidence, and generates interpretable judgment bases (e.g., "80% probability of being fake due to image tampering + emotional text").

Multimodal fusion faces challenges such as feature space alignment (different feature spaces for vision, text, graphs, etc.) and data imbalance (far more real news than fake news). Countermeasures:

• Hierarchical fusion: Features are extracted independently for each modality at the bottom layer, aligned via attention mechanisms at the middle layer, and comprehensively judged via fuzzy reasoning at the top layer;
• Data augmentation and loss function adjustment: Address data imbalance to avoid mediocre model predictions.

The project emphasizes interpretability, and the fuzzy logic system makes decisions transparent. Its value is reflected in:

• Platform moderation: Providing a basis for rapid verification;
• Users: Cultivating media literacy;
• Research: Revealing typical patterns of fake news;
• Compliance with the ethical requirements of responsible AI.

The application prospects are broad: social media moderation, news aggregation rating, government public opinion monitoring, and educational media literacy training. However, technology has limitations: it may be bypassed or deceived by adversarial samples, and cannot solve the fundamental problems of the information ecosystem (cognitive bias, social division, etc.), so it needs to be part of a comprehensive governance strategy.

This open-source project demonstrates the potential of AI technology integration. By combining ResNet's visual insight, Transformer's semantic understanding, GNN's structural analysis, and fuzzy logic's interpretable reasoning, it builds a powerful and transparent detection system, providing a strong weapon to safeguard a clean digital public space.