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Related Experiment Video

Updated: Sep 22, 2025

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Blockchain-based rumor detection approach for COVID-19.

Poonam Rani1, Vibha Jain1, Jyoti Shokeen2

  • 1Department of Computer Engineering, Netaji Subhas University of Technology, Delhi, India.

Journal of Ambient Intelligence and Humanized Computing
|May 25, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel framework using blockchain and Bi-directional Long Short Term Memory (Bi-LSTM) to detect and prevent the spread of false information on social media. The Bi-LSTM model achieved 99.63% accuracy in identifying and stopping rumors.

Keywords:
BlockchainCOVID-19LSTMRumor

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Area of Science:

  • Computer Science
  • Information Security
  • Artificial Intelligence

Background:

  • Social media facilitates rapid information sharing, often without veracity checks, leading to widespread false information propagation.
  • Detecting rumors in massive unstructured social media data is challenging.
  • Existing machine learning and deep learning methods primarily focus on detection, not prevention.

Purpose of the Study:

  • To propose a novel model for the detection and prevention of transmitted rumors on social media networks.
  • To leverage blockchain technology for information credibility verification.
  • To develop a multi-layered framework to mitigate rumor propagation.

Main Methods:

  • A four-layered framework comprising network, blockchain, machine, and device layers was designed.
  • Blockchain technology was employed to verify information credibility.
  • Deep learning, specifically the Bi-directional Long Short Term Memory (Bi-LSTM) model, was used for anomaly identification and rumor prevention by monitoring incoming messages.

Main Results:

  • The proposed Bi-LSTM model demonstrated superior performance compared to state-of-the-art methods.
  • The model achieved an accuracy of 99.63%.
  • The false positive rate was significantly low at 0.13%.

Conclusions:

  • The integrated framework effectively detects and prevents the propagation of rumors on social media.
  • The Bi-LSTM model shows high efficacy in identifying and mitigating false information.
  • This approach offers a robust solution for enhancing information integrity in online networks.