Song Podong, Jin Wiseong, Chen Shaowei, Hu Xufang, Min Kwisik, Li Shengchao, Li Senmong
Kunming University, Kunming City, China.
Macao Polytechnic University, Macao, China.
PLoS One. 2024 Dec 5;19(12):e0308317. doi: 10.1371/journal.pone.0308317. eCollection 2024.
Cyberspace is emerging as a critical living environment, significantly influencing sustainable human development. Internet public opinion is a crucial aspect of cyberspace governance, serving as the most important form of expressing popular will. However, perceiving public opinion can be challenging due to its complex and elusive nature. In this paper, we propose a novel framework for perceiving popular will, managing public opinion, and influencing people's behavior, based on machine learning and game theory approaches. Our framework leverages deep learning techniques to analyze public opinion, active learning methods to reduce costs, and game theory to make optimal management decisions. We verify the effectiveness of our framework using empirical data collected from Chinese provinces Y and G, and provide theoretical support by analyzing the interrelationship between public opinion, online public opinion, and people's behavior. Our framework can be applied inexpensively to studies in other regions, thereby offering valuable insights into cyberspace governance and public opinion management.
网络空间正在成为一个至关重要的生活环境，对人类可持续发展产生重大影响。网络舆论是网络空间治理的关键方面，是表达民意的最重要形式。然而，由于其复杂且难以捉摸的性质，感知舆论可能具有挑战性。在本文中，我们基于机器学习和博弈论方法，提出了一个用于感知民意、管理舆论和影响人们行为的新颖框架。我们的框架利用深度学习技术分析舆论，采用主动学习方法降低成本，并运用博弈论做出最优管理决策。我们使用从中国Y省和G省收集的实证数据验证了我们框架的有效性，并通过分析舆论、网络舆论和人们行为之间的相互关系提供了理论支持。我们的框架可以低成本地应用于其他地区的研究，从而为网络空间治理和舆论管理提供有价值的见解。
