[1]熊尧,李弼程,王子玥.基于两级传播理论的舆论超网络传播分析[J].智能系统学报,2020,15(5):870-879.[doi:10.11992/tis.201903011]
XIONG Yao,LI Bicheng,WANG Ziyue.Analysis of public opinions in network communication based on the two-level communication theory[J].CAAI Transactions on Intelligent Systems,2020,15(5):870-879.[doi:10.11992/tis.201903011]
点击复制
XIONG Yao,LI Bicheng,WANG Ziyue.Analysis of public opinions in network communication based on the two-level communication theory[J].CAAI Transactions on Intelligent Systems,2020,15(5):870-879.[doi:10.11992/tis.201903011]
基于两级传播理论的舆论超网络传播分析
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
15
期数:
2020年第5期
页码:
870-879
栏目:
学术论文—智能系统
出版日期:
2020-09-05
- Title:
- Analysis of public opinions in network communication based on the two-level communication theory
- Keywords:
- social network; two-stage communication theory; super-network; evolution analysis; dynamics; public opinions’ brewing period; network structure measurement; topic traction
- 分类号:
- N949;TP18
- 文献标志码:
- A
- 摘要:
- 社交网络挖掘可以使人们更好地认识信息在网络中的传播规律，分析信息在事件中的传播特点。现有的文献研究主要集中于舆论事件社交网络的静态建模，以及针对一些共性特点的仿真验证，而对舆论事件模型结构变化的讨论较少。本文尝试从两级传播理论出发，采用三层超网络结构对舆论事件不同时段构建传播分析模型，给出舆论演化分析度量指标，挖掘超网络结构变化的特点，探索舆论酝酿期积蓄力量的潜在因素。以长生疫苗事件进行分析，发现需要在酝酿期有多样化的意见领袖不断在各个话题中进行牵引，在积累了潜在的舆论人群之后才能促成舆论爆发。
- Abstract:
- Social network mining can help people better understand the law of information dissemination and analyze the characteristics of information dissemination in events. Existing literature primarily focus on static modeling of public opinion in social network and simulation verification for some common characteristics and giving less importance to discussions on the structural change of public opinion based on events. This paper attempts to use the two-stage communication theory to construct communication analysis model for different periods (events) of public opinion based on a three-layer super-network structure. Indexes of public opinion in social network are measured by evolution analysis, the characteristics of super network structure change are uncovered, and potential factors of public opinions accumulated strength during the gestation period are explored. Taking Changsheng vaccine’s incident as an example for analysis, this paper finds that during the incubation period, diversified opinion leaders are needed to constantly feed on various topics and gather or combine potential public opinion groups before leading to the upsurge of public opinion.
- 参考文献/References:
-
[1] 陈钢, 严亚. 当代传播体系视野中的舆论场结构演变[J]. 中国广播电视学刊, 2018(11): 37-40
[2] 庄宫萃. 关于网络舆情事件的传播引导策略研究与思考[J]. 新闻研究导刊, 2018, 9(20): 95
[3] 王茜茜. 网络舆论演变机制及网民社会心理流变探究[J]. 视听, 2018(12): 167-168
[4] ALVES S G, NETO N M O, MARTINS M L. Electoral surveys’ influence on the voting processes: a cellular automata model[J]. Physica A: statistical mechanics and its applications, 2012, 316(1/2/3/4): 601-614.
[5] GALAM S. Minority opinion spreading in random geometry[J]. The European physical journal B-condensed matter and complex systems, 2002, 25(4): 403-406.
[6] SUDBURY A. The Proportion of the population never hearing a rumour[J]. Journal of applied probability, 1985, 22(2): 443-446.
[7] 贺筱媛, 胡晓峰. 基于Agent的Web网信息传播仿真模型[J]. 系统仿真学报, 2010, 22(10): 2426-2431
HE Xiaoyuan, HU Xiaofeng. Modeling and simulation for agent-based information diffusion on worldwide web[J]. Journal of system simulation, 2010, 22(10): 2426-2431
[8] 刘常昱, 胡晓峰, 罗批, 等. 基于不对称人际影响的舆论涌现模型研究[J]. 系统仿真学报, 2008, 20(4): 990-992
LIU Changyu, HU Xiaofeng, LUO Pi, et al. Study on consensus emergency model based on asymmetric personal relationship influence[J]. Journal of system simulation, 2008, 20(4): 990-992
[9] 贺筱媛, 胡晓峰. 网络信息传播动力学建模研究[J]. 系统仿真学报, 2010, 22(11): 2511-2514
HE Xiaoyuan, HU Xiaofeng. Dynamical modeling of information diffusion on internet[J]. Journal of system simulation, 2010, 22(11): 2511-2514
[10] 李春发, 刘凯, 王晟锴. 基于Multi-Agent的政府干预下虚假舆情传播规律与控制决策[J]. 现代情报, 2018, 38(5): 53-59
LI Chunfa, LIU Kai, WANG Shengkai. Propagation law and control decision of false public opinion in the government intervention based on multi-agent[J]. Journal of modern information, 2018, 38(5): 53-59
[11] 刘锦德, 刘咏梅. 基于改进Deffaunt模型和小世界网络的舆情传播模拟与仿真[J]. 系统工程, 2015, 33(3): 123-129
LIU Jinde, LIU Yongmei. Modeling and simulation of public opinion dissemination based on modified deffaunt model and small-world network[J]. Systems engineering, 2015, 33(3): 123-129
[12] 王众托. 关于超网络的一点思考[J]. 上海理工大学学报, 2011, 33(3): 229-237
WANG Zhongtuo. Reflection on supernetwork[J]. Journal of University of Shanghai for Science and Technology, 2011, 33(3): 229-237
[13] 索琪, 郭进利. 基于超图的超网络: 结构及演化机制[J]. 系统工程理论与实践, 2017, 37(3): 720-734
SUO Qi, GUO Jinli. The structure and dynamics of hypernetworks[J]. Systems engineering-theory & practice, 2017, 37(3): 720-734
[14] 沈乾, 刘怡君. 多层同步网络在舆情仿真研究中的应用[J]. 系统工程理论与实践, 2017, 37(1): 182-190
SHEN Qian, LIU Yijun. Public opinion simulation research based on multilayer synchronization network[J]. Systems engineering-theory & practice, 2017, 37(1): 182-190
[15] 马宁, 刘怡君. 基于超网络的舆情演化多主体建模[J]. 系统管理学报, 2015, 24(6): 785-794
MA Ning, LIU Yijun. Multi-agent modeling of public opinion evolution based on SuperNetwork analysis[J]. Journal of systems & management, 2015, 24(6): 785-794
[16] WANG Fuhong, GUO Jinli, SHEN Aizhong, et al. Evolving hypernetwork model based on WeChat user relations[EB/OL].https: //arxiv.org/abs/1511.01697, 2015.
[17] 田儒雅, 刘怡君, 牛文元. 舆论超网络的领袖引导模型[J]. 中国管理科学, 2014, 22(10): 136-141
TIAN Ruya, LIU Yijun, NIU Wenyuan. Leader-guiding model of online opinion Supernetwork[J]. Chinese journal of management science, 2014, 22(10): 136-141
[18] WHANG J J, GLEICH D F, DHILLON I S. Overlapping community detection using seed set expansion[C]//Proceedings of the 22nd ACM International Conference on Information & Knowledge Management. San Francisco, USA, 2013.
[19] XIE Zheng, YI Dongyun, OUYANG Zhenzheng, et al. Hyperedge communities and modularity reveal structure for documents[J]. Chinese physics letters, 2012, 29(3): 038902.
[20] KAPOOR K, SHARMA D, SRIVASTAVA J. Weighted node degree centrality for hypergraphs[C]//Proceedings of the 2013 IEEE 2nd Network Science Workshop. West Point, USA, 2013.
[21] ZHANG Qianming, Lü Linyuan, WANG Wenqiang, et al. Correction: potential theory for directed networks[J]. PLoS one, 2013, 8(8): e55437.
[22] ESTRADA E. Subgraph centrality and clustering in complex hyper-networks[J]. Physica A: statistical mechanics and its applications, 2006, 364: 581-594.
[23] DENNING P J. The science of computing: what is computer science?[J]. American scientist, 1985, 73(1): 16-19.
[24] 马宁, 田儒雅, 刘怡君, 等. 基于动态网络分析(DNA)的意见领袖识别研究[J]. 科研管理, 2014, 35(8): 83-92
MA Ning, TIAN Ruya, LIU Yijun, et al. Science research management[J]. Science research management, 2014, 35(8): 83-92
[25] LAZARSFELD P F, BERELSON B, GAUDET H. The people’s choice: how the voter makes up his mind in a presidential campaign[M]. New York: Columbia University Press, 1944.
[26] 史波. 公共危机事件网络舆情内在演变机理研究[J]. 情报杂志, 2010, 29(4): 41-45
SHI Bo. A study of the intrinsic evolution mechanism for network sentiment of public crisis events[J]. Journal of intelligence, 2010, 29(4): 41-45
[27] 王光辉, 刘怡君, 迟钰雪. 舆论危机的异化极化效应研究[J]. 管理科学学报, 2017, 20(3): 148-160
WANG Guanghui, LIU Yijun, CHI Yuxue. Alienation mechanism and polarization effect of public opinion crisis[J]. Journal of management sciences in China, 2017, 20(3): 148-160
[28] 王光辉, 刘怡君. 网络舆论危机事件的蔓延扩散效应研究[J]. 中国管理科学, 2015, 23(7): 119-126
WANG Guanghui, LIU Yijun. Study on spread mechanism and diffusion effect of network public opinion crisis[J]. Chinese journal of management science, 2015, 23(7): 119-126
[29] 马宁, 刘怡君. 基于超网络的舆论领袖识别应用研究[J]. 中国科学院院刊, 2012, 27(5): 586-594
MA Ning, LIU Yijun. Recognition of online opinion leaders based on Supernetwork analysis[J]. Bulletin of the Chinese academy of sciences, 2012, 27(5): 586-594
[30] GHOSHAL G, ZLATI? V, CALDARELLI G, et al. Random hypergraphs and their applications[J]. Physical review E, 2009, 79(6): 066118.
- 相似文献/References:
-
[1]孔庆超,毛文吉,张育浩.社交网站中用户评论行为预测[J].智能系统学报,2015,10(3):349.[doi:10.3969/j.issn.1673-4785.201403019]
KONG Qingchao,MAO Wenji,ZHANG Yuhao.User comment behavior prediction in social networking sites[J].CAAI Transactions on Intelligent Systems,2015,10():349.[doi:10.3969/j.issn.1673-4785.201403019]
[2]王景丽,许立波,庞超逸.复杂网络中的在线社交网络演化模型[J].智能系统学报,2015,10(6):949.[doi:10.11992/tis.201507042]
WANG Jingli,XU Libo,PANG Chaoyi.Evolution model of online social networks based on complex networks[J].CAAI Transactions on Intelligent Systems,2015,10():949.[doi:10.11992/tis.201507042]
[3]石磊,杜军平,周亦鹏,等.在线社交网络挖掘与搜索技术研究[J].智能系统学报,2016,11(6):777.[doi:10.11992/tis.201612007]
SHI Lei,DU Junping,ZHOU Yipeng,et al.A survey on online social network mining and search[J].CAAI Transactions on Intelligent Systems,2016,11():777.[doi:10.11992/tis.201612007]
[4]花勇,陈伯伦,朱国畅,等.基于渗流模型的影响力最大化算法[J].智能系统学报,2019,14(6):1262.[doi:10.11992/tis.201906039]
HUA Yong,CHEN Bolun,ZHU Guochang,et al.An influence maximization algorithm based on percolation model[J].CAAI Transactions on Intelligent Systems,2019,14():1262.[doi:10.11992/tis.201906039]
[5]陈伯伦,朱国畅,纪敏,等.代价约束下基于随机游走的负影响力传播抑制方法[J].智能系统学报,2022,17(2):266.[doi:10.11992/tis.202101037]
CHEN Bolun,ZHU Guochang,JI Min,et al.Negative influence propagation suppression method based on a random walk under cost constraint[J].CAAI Transactions on Intelligent Systems,2022,17():266.[doi:10.11992/tis.202101037]
[6]常新功,王金珏.基于图卷积集成的网络表示学习[J].智能系统学报,2022,17(3):547.[doi:10.11992/tis.202107048]
CHANG Xingong,WANG Jinjue.Network representation learning using graph convolution ensemble[J].CAAI Transactions on Intelligent Systems,2022,17():547.[doi:10.11992/tis.202107048]
[7]孟祥福,赖贞祥,崔江燕.集合空间关键字内聚组查询方法[J].智能系统学报,2024,19(3):707.[doi:10.11992/tis.202211013]
MENG Xiangfu,LAI Zhenxiang,CUI Jiangyan.Cohesive group query approach for collective spatial keywords[J].CAAI Transactions on Intelligent Systems,2024,19():707.[doi:10.11992/tis.202211013]
备注/Memo
收稿日期:2019-03-12。
基金项目:国家社会科学基金项目（19BXW110）；福建省社会科学规划项目（FJ2017B073）；华侨大学研究生科研创新基金项目
作者简介:熊尧,硕士研究生,主要研究方向为智能数据管理与分析;李弼程,教授,博士生导师,博士,主要研究方向为大数据与人工智能、网络舆情监测与引导。获省部级科技进步一等奖1项、二等奖6项。发表学术论文250篇,出版著作6部;王子玥,硕士研究生,主要研究方向为智能数据管理与分析
通讯作者:李弼程.E-mail:lbclm@163.com
更新日期/Last Update:
2021-01-15