Paper ID

356fb62f0f9acf17c5f3a8c2669436e8c4ebd632

Title

Integrating Graph Attention Networks with Semantic-enhanced Multimodal Fusion for improved fake news detection in low-resource languages.

Introduction

Problem Statement

Integrating Graph Attention Networks with a Semantic-enhanced Multimodal Fusion Network will improve the accuracy of fake news detection in low-resource languages like Urdu by effectively capturing contextual relationships and multimodal data interactions.

Motivation

Existing methods for fake news detection, particularly in low-resource languages like Urdu, often rely heavily on either textual or visual data, without effectively integrating the two. While multimodal approaches exist, they often overlook the potential of graph-based methods to capture complex propagation structures and contextual relationships. This gap is significant because understanding how misinformation spreads and interacts with different modalities can enhance detection accuracy. The proposed hypothesis addresses this gap by integrating Graph Attention Networks (GAT) with a Semantic-enhanced Multimodal Fusion Network (SMFN) to leverage both graph-based propagation and multimodal data fusion, which has not been extensively explored in the context of low-resource languages.

Proposed Method

The research aims to test the hypothesis that combining Graph Attention Networks (GAT) with a Semantic-enhanced Multimodal Fusion Network (SMFN) can significantly enhance fake news detection in low-resource languages like Urdu. The GAT will model the propagation structure of misinformation, capturing contextual relationships within the data. This involves using attention mechanisms to weigh the importance of different nodes in a graph, allowing the model to focus on the most relevant parts of the data. The SMFN will address the semantic gap between visual features and high-level semantic expression by processing word embeddings, semantic extraction, and visual extraction, fusing features into a common space using convolutional neural networks (CNN). This dual approach leverages the strengths of both graph-based and multimodal fusion techniques, providing a comprehensive framework for understanding and detecting fake news. The expected outcome is an improvement in detection accuracy, precision, recall, and F1-score, particularly in the context of low-resource languages where data scarcity and linguistic diversity pose significant challenges.

Background

Graph Attention Networks (GAT): GATs are used to model the propagation structure of misinformation by capturing contextual relationships in fake news detection. This involves using attention mechanisms to weigh the importance of different nodes in a graph, which represents the dissemination patterns of news articles. The integration of GATs with BERT allows for the extraction of both linguistic and network-based features, enhancing the model's ability to detect fake news. Implementation involves constructing a graph from the FakeNewsNet dataset, which includes news articles, user interactions, and source metadata. The attention-based fusion mechanism effectively integrates textual and graph embeddings, resulting in superior performance metrics such as accuracy, precision, recall, and F1-score.

Semantic-enhanced Multimodal Fusion Network (SMFN): The SMFN aims to bridge the semantic gap between visual features and high-level semantic expression by using a convolutional neural network (CNN) to fuse multimodal information. The network processes word embedding, semantic extraction, and visual extraction to generate post-representation. A domain adaptation network is used to remove unique features between different events, allowing the model to learn shared characteristics of fake news. The implementation involves training the CNN on a multimodal dataset and using the domain adaptation network to generalize across different events. This approach enhances the model's ability to detect fake news by leveraging both textual and visual information.

Implementation

The proposed method involves integrating Graph Attention Networks (GAT) with a Semantic-enhanced Multimodal Fusion Network (SMFN) to improve fake news detection. The GAT will model the propagation structure of misinformation by capturing contextual relationships within the data. This involves constructing a graph from the FakeNewsNet dataset, which includes news articles, user interactions, and source metadata. The attention mechanisms in GAT will weigh the importance of different nodes, allowing the model to focus on the most relevant parts of the data. The SMFN will process word embeddings, semantic extraction, and visual extraction, fusing features into a common space using convolutional neural networks (CNN). A domain adaptation network will be used to remove unique features between different events, allowing the model to learn shared characteristics of fake news. The integration of GAT and SMFN will be implemented using PyTorch and Hugging Face Transformers. The model will be evaluated on the FakeNewsNet dataset, with performance measured by accuracy, precision, recall, and F1-score. The expected outcome is an improvement in detection accuracy, particularly in the context of low-resource languages like Urdu.

Experiments Plan

Operationalization Information

Please implement an experiment to test whether integrating Graph Attention Networks (GAT) with a Semantic-enhanced Multimodal Fusion Network (SMFN) improves fake news detection in low-resource languages like Urdu. The experiment should compare three models:

  1. Baseline 1: Text-only BERT model for fake news detection
  2. Baseline 2: GAT-only model that leverages graph structure of news propagation
  3. Experimental: Integrated GAT-SMFN model that combines graph attention with multimodal fusion

The experiment should be implemented with three pilot modes controlled by a global variable PILOT_MODE which can be set to 'MINI_PILOT', 'PILOT', or 'FULL_EXPERIMENT':

Start with MINI_PILOT first, then if everything looks good, run the PILOT. After the pilot, stop and do not run the FULL_EXPERIMENT as a human will manually verify the results and make the change.

Implementation Details:

  1. Data Preparation:
  2. Download and preprocess the FakeNewsNet dataset
  3. Extract text content, images, and user interaction metadata
  4. For Urdu content, use appropriate tokenization and preprocessing
  5. Split data into train (70%), validation (15%), and test (15%) sets

  1. Baseline 1 (Text-only BERT):
  2. Use Hugging Face Transformers to load a pre-trained BERT model
  3. Fine-tune BERT on the text content of news articles
  4. Add a classification head to predict fake/real news

  1. Baseline 2 (GAT-only):
  2. Construct a graph from news articles where nodes represent articles and edges represent relationships (e.g., shared sources, user interactions)
  3. Implement a Graph Attention Network using PyTorch Geometric
  4. Use node features derived from text embeddings (from BERT)
  5. Train the GAT to classify nodes (articles) as fake or real

  1. Experimental Model (GAT-SMFN):
  2. Implement the GAT component as in Baseline 2
  3. Implement the SMFN component with the following sub-components:
    a. Text Processing: Extract word embeddings using BERT
    b. Visual Processing: Extract visual features using a pre-trained CNN (e.g., ResNet)
    c. Semantic Extraction: Process text to extract high-level semantic features
    d. Multimodal Fusion: Use a CNN to fuse text and visual features into a common space
    e. Domain Adaptation: Implement a domain adaptation network to remove event-specific features
  4. Integrate GAT and SMFN by using GAT attention scores to guide the feature fusion process in SMFN
  5. Train the integrated model end-to-end

  1. Training Procedure:
  2. Use PyTorch for all implementations
  3. Use Adam optimizer with learning rate 1e-4
  4. Use cross-entropy loss for classification
  5. Implement early stopping based on validation loss
  6. Save model checkpoints for best validation performance

  1. Evaluation:
  2. Evaluate all models on the test set using accuracy, precision, recall, and F1-score
  3. Perform statistical significance testing (e.g., McNemar's test) to compare models
  4. Generate confusion matrices for each model
  5. Analyze performance specifically on Urdu content
  6. Perform ablation studies to understand the contribution of each component

  1. Visualization and Analysis:
  2. Visualize attention weights from GAT to understand which connections are most important
  3. Visualize the multimodal fusion process to understand how text and visual features are combined
  4. Analyze examples where the experimental model outperforms baselines and vice versa

The code should be well-documented with comments explaining each step. Include logging to track training progress and evaluation results. Generate plots and tables to summarize the results. The final output should include a comprehensive report comparing the three models and discussing the implications for fake news detection in low-resource languages.

Please implement this experiment using the specified codeblocks and ensure that the code is modular and reusable.

End Note:

The source paper is Paper 0: "Bend the truth": Benchmark dataset for fake news detection in Urdu language and its evaluation (56 citations, 2020). This idea draws upon a trajectory of prior work, as seen in the following sequence: Paper 1 --> Paper 2 --> Paper 3 --> Paper 4 --> Paper 5. The progression of research from the source paper to the related papers shows a clear trend towards improving fake news detection through advanced model architectures and multimodal data integration. Each paper builds on the previous by introducing novel techniques to enhance feature representation and fusion, addressing challenges such as noise, information loss, and inter-modal relations. A research idea that advances this field could focus on further enhancing the integration of multimodal data, particularly in low-resource languages like Urdu, by exploring novel fusion techniques that do not rely on external datasets or pre-trained models, thus addressing the constraints of the ASD Agent.
The initial trend observed from the progression of related work highlights a consistent research focus. However, the final hypothesis proposed here is not merely a continuation of that trend — it is the result of a deeper analysis of the hypothesis space. By identifying underlying gaps and reasoning through the connections between works, the idea builds on, but meaningfully diverges from, prior directions to address a more specific challenge.

References

  1. "Bend the truth": Benchmark dataset for fake news detection in Urdu language and its evaluation (2020)
  2. A Novel Stacking Approach for Accurate Detection of Fake News (2021)
  3. Multi-Level Multi-Modal Cross-Attention Network for Fake News Detection (2021)
  4. Game-on: graph attention network based multimodal fusion for fake news detection (2022)
  5. Multimodal false information detection method based on Text-CNN and SE module (2022)
  6. Multi-Modal Fake News Detection Based on Image Captions (2024)
  7. Dual stream graph augmented transformer model integrating BERT and GNNs for context aware fake news detection (2023)
  8. Overview of the Shared Task on Fake News Detection in Urdu at FIRE 2021 (2021)
  9. Ax-to-Grind Urdu: Benchmark Dataset for Urdu Fake News Detection (2023)
  10. A Multimodal Adaptive Graph-based Intelligent Classification Model for Fake News (2024)
  11. A Multimodal Semantic-Enhanced Attention Network for Fake News Detection (2023)
  12. Detecting fake news by enhanced text representation with multi-EDU-structure awareness (2022)
  13. Hybrid Deep Learning Model for Fake News Detection in Social Networks (2021)
  14. Semantic‐enhanced multimodal fusion network for fake news detection (2022)