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A Rumor Detection Model Incorporating Propagation Path Contextual Semantics and User Information.

Lin Bai1, Xueming Han1, Caiyan Jia1

  • 1School of Computer and Information Technology and Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, 100044 China.

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Summary
This summary is machine-generated.

This study introduces DAN-Tree and DAN-Tree++ for rumor detection on social media. These models effectively identify key features in rumor propagation and integrate user profiles for superior early detection performance.

Keywords:
Attention mechanismPropagation structureRumor detectionUser feature

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Social Media Analysis

Background:

  • Rumor detection is crucial for mitigating misinformation spread on social media.
  • Existing methods often overlook critical structural and user-specific features in propagation patterns.
  • This leads to suboptimal performance in identifying and flagging false information.

Purpose of the Study:

  • To develop advanced models for more accurate and efficient rumor detection.
  • To address the limitations of current methods by incorporating structural and user information.
  • To improve early detection capabilities for online rumors.

Main Methods:

  • Proposed a Dual-Attention Network on propagation Tree structures (DAN-Tree) utilizing a node-and-path dual-attention mechanism.
  • Implemented path oversampling and structural embedding to enhance the learning of deep propagation structures.
  • Introduced DAN-Tree++ by integrating user profiles into the propagation trees for enhanced performance.

Main Results:

  • DAN-Tree demonstrated superior performance compared to state-of-the-art models on four rumor datasets based on propagation structures.
  • DAN-Tree++ significantly outperformed other models when incorporating both user profiles and propagation structures on two datasets.
  • Both DAN-Tree and DAN-Tree++ achieved top performance in early rumor detection tasks.

Conclusions:

  • The proposed DAN-Tree and DAN-Tree++ models offer significant advancements in rumor detection.
  • Integrating structural and user-specific features is vital for improving model accuracy and early detection.
  • These models provide a robust framework for combating the spread of online misinformation.