School of Journalism and Communication, Renmin University of China, Beijing 100872, China.
College of Movie and Media, Sichuan Normal University, Chengdu 610066, China.
Comput Intell Neurosci. 2023 Mar 10;2023:5212712. doi: 10.1155/2023/5212712. eCollection 2023.
Network public opinion represents public social opinion to a certain extent and has an important impact on formulating national policies and judgment. Therefore, China and other countries attach great importance to the study of online public opinion. However, the current researches lack the combination of theory and practical cases and lack the intersection of social and natural sciences. This work aims to overcome the technical defects of traditional management systems, break through the difficulties and pain points of existing network public opinion risk management, and improve the efficiency of network public opinion risk management. Firstly, a network public opinion isolation strategy based on the infectious disease propagation model is proposed, and the optimal control theory is used to realize a functional control model to maximize social utility. Secondly, blockchain technology is used to build a network public opinion risk management system. The system is used to conduct a detailed study on identifying and perceiving online public opinion risk. Finally, a Chinese word segmentation scheme based on Long Short-Term Memory (LSTM) network model and a text emotion recognition scheme based on a convolutional neural network are proposed. Both schemes are validated on a typical corpus. The results show that when the system has a control strategy, the number of susceptible drops significantly. Two days after the public opinion is generated, the number of susceptible people decreased from 1,000 to 250; 3 days after the public opinion is generated, the number of susceptible people stabilized. 2 days after the public opinion is generated, the number of lurkers increased from 100 to 620; 3 days after the public opinion is generated, the number of lurkers stabilized. The data demonstrate that the designed isolation control strategy is effective. Changes in public opinion among infected people show that quarantine control strategies played a significant role in the early days of Corona Virus Disease 2019. The rate of change in the number of infections is more affected when quarantine controls are increased, especially in the days leading up to the outbreak. When the system adopts the optimal control strategy, the influence scope of public opinion becomes smaller, and the control becomes easier. When the dimension of the word vector of emergent events is 200, its accuracy may be higher. This method provides certain ideas for blockchain and deep learning technology in network public opinion control.
网络舆情在一定程度上代表了公众的社会意见，对国家政策的制定和判断具有重要影响。因此，中国和其他国家都非常重视对网络舆情的研究。然而，目前的研究缺乏理论与实际案例的结合，也缺乏社会科学与自然科学的交叉。本工作旨在克服传统管理系统的技术缺陷，突破现有网络舆情风险管理的难点和痛点，提高网络舆情风险管理的效率。首先，提出了一种基于传染病传播模型的网络舆情隔离策略，利用最优控制理论实现了最大化社会效用的功能控制模型。其次，利用区块链技术构建了网络舆情风险管理系统。该系统用于对在线舆情风险进行识别和感知进行详细研究。最后，提出了一种基于长短期记忆（LSTM）网络模型的中文分词方案和一种基于卷积神经网络的文本情感识别方案，并在典型语料库上进行了验证。结果表明，当系统具有控制策略时，易感染人群的数量显著下降。舆情产生后两天，易感人数从 1000 人降至 250 人；舆情产生后 3 天，易感人数趋于稳定。舆情产生后两天，潜伏人数从 100 人增加到 620 人；舆情产生后 3 天，潜伏人数趋于稳定。数据表明，设计的隔离控制策略是有效的。感染人群的舆情变化表明，在 COVID-19 早期，检疫控制策略发挥了重要作用。当增加检疫控制时，感染人数的变化率受到更大影响，特别是在疫情爆发前几天。当系统采用最优控制策略时，舆情的影响范围变小，控制变得更容易。当突发事件的词向量维度为 200 时，其准确率可能更高。该方法为区块链和深度学习技术在网络舆情控制中的应用提供了一定的思路。
