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Novel Deep Learning Framework for Cross-Platform Rumor Detection Provides Effective Solution for Information Pollution

Strategic Insights

— 1 min read

Researchers at the Dalian University of Technology in Liaoning, People's Republic of China, have developed a novel deep learning framework to detect cross-platform rumors (CPRs) effectively. The framework, named Cross Platform Rumor Detection based on Improved Federated Learning (CPRDIFL), integrates and improves federated learning and the pre-trained Masked and Contextualized BERT (MacBERT). This study provides an effective solution for CPR detection and marks a significant step toward the automated detection of cross-OSP information pollution.

Key Takeaways:

  • The anonymity and widespread popularity of online social platforms (OSPs) allow users to share uncertain posts freely, leading to numerous rumors, which spread widely across OSPs, resulting in frequent cross-platform rumors (CPRs).
  • The dual challenges of data privacy protection constraints and differences in the data and detection capabilities of OSPs exacerbate the difficulty of CPR detection.
  • The researchers designed and implemented a novel deep learning framework named CPRDIFL, which integrates and improves federated learning and the pre-trained MacBERT.
  • The framework uses federated learning to analyze data from OSPs independently, thus avoiding the need for data integration and ensuring the data privacy protection of OSPs.
  • MacBERT is deployed on the clients of CPRDIFL to extract contextual features from posts and dynamically update local weights based on the data and detection performance.
  • The framework was used in six comprehensive experiments in different scenarios, and the experimental results showed that it achieved the best results in CPR detection.
  • This study not only provides an effective solution for CPR detection but also marks a significant step toward the automated detection of cross-OSP information pollution.
  • The research was funded by the National Natural Science Foundation of China (NSFC) and the Humanities and Social Sciences Research Project of the Ministry of Education of China.

Sources:

  • A Cross-platform Rumor Detection Framework Considering Data Privacy Protection and Different Detection Capabilities of Online Social Platforms. Decision Support Systems, 2025;198.
  • Decision Support Systems. Elsevier. Radarweg 29, 1043 Nx Amsterdam, Netherlands. (Elsevier - www.elsevier.com; Decision Support Systems - www.journals.elsevier.com/decision-support-systems/)

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