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复杂系统与复杂性科学  2019, Vol. 16 Issue (1): 1-13    DOI: 10.13306/j.1672-3813.2019.01.001
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观点动力学视角下基于意见领袖的网络舆情反转研究
刘琪, 肖人彬
华中科技大学人工智能与自动化学院,武汉 430074
An Opinion Dynamics Approach to Public Opinion Reversion with the Guidance of Opinion Leaders
LIU Qi, XIAO Renbin
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
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摘要 舆情反转是网络突发事件与热点事件中的一种重要现象。为探究网络舆情反转现象的内在机理,发掘舆情反转事件的演化规律,提出了一种观点动力学视角下基于意见领袖的观点演化模型。以意见领袖为切入点,基于无标度网络结构改进经典有界信任的HK模型,在观点更新中引入意见领袖的作用。运用社会网络分析理论的中心性指标识别意见领袖,对意见领袖引导下的观点演化进行仿真实验。选取新浪微博上的舆情反转案例,对模型仿真结果进行验证。实验结果表明,意见领袖对观点演化结果起着关键性的作用,能够引导群体的观点发生逆转,利用改进的模型能够模拟网络舆情反转事件的演化进程。
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刘琪
肖人彬
关键词 观点动力学意见领袖舆情反转新浪微博    
Abstract:Opinion reversion is an important phenomenon in network emergencies and hot events. In order to explore the internal mechanism and the evolution law of public opinion reversion, we propose an opinion evolution model based on opinion leaders from the perspective of opinion dynamics. The model is applied to simulate the evolution process of public opinion under the guidance of opinion leaders. We take opinion leaders into account, improve the HK model and use social network analysis method to identify the opinion leaders based on scale-free network structure. To verify the simulation results we select the reversal case on sina twitter. The results show that opinion leaders play a key role in the evolution of opinion, which can guide the public opinion to reverse. The results also indicate that the improved model can simulate the evolution process of network opinion reversion.
Key wordsopinion dynamics    opinion leaders    public opinion reversion    sina twitter
收稿日期: 2019-01-19      出版日期: 2019-07-04
ZTFLH:  TP391.9  
基金资助:国家自然科学基金(61540032)
通讯作者: 肖人彬(1965),男,湖北武汉人,博士,教授,主要研究方向为复杂系统、群智能。   
作者简介: 刘琪(1995),女,安徽阜阳人,硕士研究生,主要研究方向为舆情传播、大数据。
引用本文:   
刘琪, 肖人彬. 观点动力学视角下基于意见领袖的网络舆情反转研究[J]. 复杂系统与复杂性科学, 2019, 16(1): 1-13.
LIU Qi, XIAO Renbin. An Opinion Dynamics Approach to Public Opinion Reversion with the Guidance of Opinion Leaders. Complex Systems and Complexity Science, 2019, 16(1): 1-13.
链接本文:  
http://fzkx.qdu.edu.cn/CN/10.13306/j.1672-3813.2019.01.001      或      http://fzkx.qdu.edu.cn/CN/Y2019/V16/I1/1
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