Applying Blockchain Technology in Network Public Opinion Risk Management System in Big Data Environment

Area of Science:

• Computational Social Science
• Information Systems
• Network Science

Background:

• Online public opinion significantly influences policy and national judgment, necessitating effective management strategies.
• Existing research on network public opinion risk management lacks integration of theory, practical cases, and interdisciplinary approaches.
• Traditional management systems face technical defects, hindering efficient risk identification and control.

Purpose of the Study:

• To overcome technical limitations in traditional systems for network public opinion risk management.
• To improve the efficiency and effectiveness of managing online public opinion risks.
• To develop an integrated system combining infectious disease modeling, optimal control theory, and blockchain technology.

Main Methods:

• Proposed an isolation strategy for network public opinion based on infectious disease propagation models.
• Utilized optimal control theory to develop a functional control model aimed at maximizing social utility.
• Developed a network public opinion risk management system using blockchain technology.
• Implemented Chinese word segmentation using a Long Short-Term Memory (LSTM) network and text emotion recognition via a convolutional neural network.

Main Results:

• The isolation control strategy significantly reduced the number of susceptible individuals, decreasing from 1,000 to 250 within two days.
• The number of 'lurkers' (individuals exposed but not yet actively spreading opinion) increased substantially, stabilizing around 620 within three days.
• Optimal control strategies reduced the scope of public opinion influence, making control more manageable.
• The study demonstrated the effectiveness of isolation and quarantine control strategies, drawing parallels to their impact during the COVID-19 pandemic.

Conclusions:

• The proposed network public opinion isolation strategy, integrated with blockchain technology, is effective in managing and controlling online discourse.
• The combination of infectious disease modeling and deep learning techniques offers a promising approach for future research in network public opinion control.
• The system provides a robust framework for identifying, perceiving, and mitigating online public opinion risks.