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Modeling of false information on microblog with block matching and fuzzy neural network

Author

Listed:
  • Guo Yangyong

    (Key Laboratory of Interior Layout Optimization and Security, Institutions of Higher Education of Sichuan Province, Chengdu Normal University, Chengdu 611130, P. R. China)

  • Wei Juan

    (#x2020;Artificial Intelligence Key Laboratory of Sichuan Province, Yibin 644000, P. R. China)

Abstract

The detection method of microblog false information has been constructed based on block matching and fuzzy neural network to improve the detection accuracy of microblog false information effectively. With this method, we can calculate the rank distance and sample entropy of microblog data according to the evaluation word rank vector of microblog false information, carry out the block matching of false information in fuzzy data set and input the characteristic quantity of microblog false information extracted into the fuzzy neural network classifier for data classification and recognition. So that it has achieved the optimized detection of false information and improved the judgment ability of false information. Finally, the key factors that affect the algorithm are deeply studied through simulation experiments according to the real data of Sina microblog, and the performance state between the proposed algorithm and Fuzzy C-means and Spectral Analysis algorithms is compared and analyzed correspondingly. The results show that the algorithm has good adaptability.

Suggested Citation

  • Guo Yangyong & Wei Juan, 2021. "Modeling of false information on microblog with block matching and fuzzy neural network," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 32(02), pages 1-10, February.
    • Handle: RePEc:wsi:ijmpcx:v:32:y:2021:i:02:n:s0129183121500194
    DOI: 10.1142/S0129183121500194
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