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Risk prediction and credibility detection of network public opinion using blockchain technology

Author

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  • Wang, Zeyu
  • Zhang, Shuting
  • Zhao, Yuanyuan
  • Chen, Chuan
  • Dong, Xiufang

Abstract

This study aims to improve the accuracy of risk prediction and credibility detection of network public opinion (NPO) and optimize the network environment. Using blockchain technology (BT) network system for optimization, a smart contract is used to build a risk management system of NPO, so that public opinion can be traced through the smart ledger using risk association tree technology. First, BT, NPO, and related theories of risk management are introduced. Second, the network situation of public opinion dissemination in the blockchain environment is discussed. Finally, a BT-based risk management model of the NPO is implemented, and experiments are conducted to verify the effect of the model. Furthermore, the theoretical framework of NPO credibility model detection is optimized according to the public opinion risk prediction model. The research results reveal that the three experimental schemes reached the peak density of the number of public opinion disseminators in the third step, and the highest values were 0.1335, 0.1318, and 0.1296, respectively. Moreover, the density of public opinion dissemination in the three experimental schemes reached a stable state of 0.087, 0.088, and 0.0898, respectively after 48 steps. Under BT, the designed three experimental schemes can reasonably conduct the risk prediction and credibility detection of NPO. This work contributes to optimizing the control measures of the network environment.

Suggested Citation

  • Wang, Zeyu & Zhang, Shuting & Zhao, Yuanyuan & Chen, Chuan & Dong, Xiufang, 2023. "Risk prediction and credibility detection of network public opinion using blockchain technology," Technological Forecasting and Social Change, Elsevier, vol. 187(C).
    • Handle: RePEc:eee:tefoso:v:187:y:2023:i:c:s0040162522006989
    DOI: 10.1016/j.techfore.2022.122177
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    References listed on IDEAS

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    5. Lin, Weiwen & Qin, Shan & Zhou, Xinzhu & Guan, Xin & Zeng, Yanzhao & Wang, Zeyu & Shen, Yaohan, 2024. "Three-dimensional quantitative mineral prediction from convolutional neural network model in developing intelligent cleaning technology," Resources Policy, Elsevier, vol. 88(C).
    6. Yang, Ya & Zhou, Mengru & Hou, Yawei & Tang, Run & Liu, Bo & Deng, Yue, 2023. "Examining the impacts of implicit economic policy on urban environmental pollution: Unveiling pathways for sustainable recovery," Resources Policy, Elsevier, vol. 85(PA).
    7. Gao, Yajuan & Zhang, Congqing & Wang, Yilin & Wang, Shuaihao & Zou, Yunjin & Gao, Junhong & Wang, Zeyu, 2023. "Fiscal decentralization and rural resource utilization efficiency: Evidence from quasi-natural experiment in China," Resources Policy, Elsevier, vol. 87(PB).
    8. Shuhui Yu & Xin Guan & Junfan Zhu & Zeyu Wang & Youting Jian & Weijia Wang & Ya Yang, 2023. "Artificial Intelligence and Urban Green Space Facilities Optimization Using the LSTM Model: Evidence from China," Sustainability, MDPI, vol. 15(11), pages 1-14, June.
    9. Zhang, Jingshen & Zhou, Xinzhu & Bai, Rong & Dong, Haoyang & Tang, Tingting & Wang, Zeyu & Yang, Ya & Huang, Feng, 2024. "Impact of environmental supervision reform on green innovation in mineral enterprises," Resources Policy, Elsevier, vol. 88(C).
    10. Xiaoyan Peng & Xin Guan & Yanzhao Zeng & Jiali Zhang, 2024. "Artificial Intelligence-Driven Multi-Energy Optimization: Promoting Green Transition of Rural Energy Planning and Sustainable Energy Economy," Sustainability, MDPI, vol. 16(10), pages 1-20, May.
    11. Jin, Ting & Liang, Feiyan & Dong, Xiaoqi & Cao, Xiaojuan, 2023. "Research on land resource management integrated with support vector machine —Based on the perspective of green innovation," Resources Policy, Elsevier, vol. 86(PB).

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