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Rumor detection based on Attention Graph Adversarial Dual Contrast Learning.

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  • 1School of Information Science and Engineering, Xinjiang University, Urumqi, China.

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Summary
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Detecting online rumors is challenging due to social media manipulation. Our novel Attention Graph Adversarial Dual Contrast Learning (AGAD) model effectively distinguishes rumors from credible information, enhancing online safety.

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Area of Science:

  • Computer Science
  • Social Media Analysis
  • Artificial Intelligence

Background:

  • Social media is a primary news source, increasing vulnerability to misinformation and malicious manipulation.
  • Traditional rumor detection models struggle with disrupted comment section structures and sophisticated manipulation tactics.
  • The spread of rumors poses risks to public health and financial stability.

Purpose of the Study:

  • To develop a novel rumor detection architecture capable of handling complex social media environments.
  • To enhance the accuracy and robustness of misinformation identification in online discussions.
  • To mitigate the negative impacts of rumors on public health and financial markets.

Main Methods:

  • Proposed a novel architecture combining dual comparison learning, adversarial training, and attention filters.
  • Introduced an attention filter module to refine comment data for Graph Attention Networks (GAT).
  • Implemented an adversarial training module (ADV) for robustness against malicious comments and a dual contrast learning (DCL) component to differentiate comment types.

Main Results:

  • The developed AGAD model demonstrated superior performance compared to state-of-the-art algorithms in rumor detection tasks.
  • Experimental findings confirmed the effectiveness of the combined approach in identifying and filtering misinformation.
  • The model successfully enhanced the structural information processed by the GAT network.

Conclusions:

  • The AGAD model offers a significant advancement in rumor detection, particularly in challenging social media contexts.
  • The integration of attention filters, adversarial training, and dual contrast learning provides a robust defense against online misinformation.
  • This research contributes to creating a more reliable online information ecosystem.