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对网络舆论逆转过程进行研究具有十分重要的意义, 它有助于管理者有效引导舆论朝良性方向发展. 目前, 网络舆论逆转研究主要集中于动力学模型构建与仿真实验分析, 其研究结果具有一定的理论价值. 然而, 这是否适用于真实社交网络环境, 还尚未经过测试. 为了对舆论逆转过程进行研究, 构建了符合实际的模型, 并对网络舆论逆转典型事例进行了深入分析. 通过观察统计, 发现网络舆论逆转具有自身的规律: 新曝光冲突性消息是导致舆论发生逆转的根本原因; 消息的传播影响着群体的发声与沉默; 消息的属性包括传播率、可信度、观点倾向、起始传播时间和消息源中心度决定着舆论逆转的幅度. 依据这一规律, 设置了消息属性参数, 并将消息传播与观点演化过程相结合, 构建了网络舆论逆转模型. 模型的仿真实验结果表明, 新冲突性消息的传播率、可信度和消息源中心度正向影响着舆论逆转幅度, 其中可信度较传播率影响更大. 新的冲突性消息曝光的时间越早, 舆论逆转的速率越快, 幅度越大. 该模型与实际相符, 可为理解和解释网络舆论逆转过程、引导网络舆论提供理论依据.Studying the process of network public opinion reversal is of great significance for guiding public opinion toward a positive direction. Currently, the research on opinion reversal mainly focuses on the construction of dynamic models and analysis of simulations and the results of which have a certain theory value. However, whether these models are applicable to the real social network environment has not been tested. For studying the process of public opinion reversal, we build a model according with the realities, and make an in-depth analysis of the typical case of opinion reversal. Some rules are found from the observation and statistics: the fundamental reason of public opinion reversal is the conflicting news. Spreading of news affects the opinions of the group. The news properties, including transmission rate, credibility, opinion polarity, publication date and the degree of message source determine the extent to reverse. Based on these rules, parameters of news properties are set, and a model of opinion reversal is proposed by combing the information dissemination with opinion evolution. Simulation results show that the transmission rate of news, the credibility of news, and the degree of message source have a positive influence on the margin of reversal. The influence of credibility is more dramatic than that of transmission rate. Moreover, the public opinion would be reversed more quickly and completely if the conflicting news is released more easily. The proposed model can fit the actual data, which is helpful for understanding and explaining the process of network public opinion reversal, and provides theoretical basis for guiding the network public opinion.
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Keywords:
- conflicting news /
- opinion reversal /
- social network /
- information transmission
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[3] Qian C, Cao J D, Lu J Q, Kurths J 2011 Chaos 21 025116
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[6] Crokidakis N, de Oliveira P M C 2014 Physica A 409 48
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[9] Huang G, Cao J, Wang G, Qu Y 2008 Physica A 387 4665
[10] Huang G, Cao J, Qu Y 2009 Physica A 388 3911
[11] Galam S, Jacobs F 2007 Physica A 381 366
[12] Shen B, Liu Y 2010 Int. J. Mod. Phys. C 21 1001
[13] Xie J, Sreenivasan S, Korniss G, Zhang W, Lim C 2011 Phys. Rev. E 84 011130
[14] Sznajd-Weron K, Tabiszewski M, Timpanaro A M 2011 Eur. Lett. 96 48002
[15] Wu Y, Hu Y, He X H, Deng K 2014 Chin. Phys. B 23 060101
[16] Min L, Liu Z, Tang X Y, Chen M 2014 Acta Phy. Sin. 64 88901 (in Chinese) [闵磊, 刘智, 唐向阳, 陈矛 2014 物理学报 64 88901]
[17] Ding Z Y, Tia Y, Zhou B 2015 J. Comput. Res. Develop. 51 691 (in Chinese) [丁兆云, 贾焰, 周斌 2015 计算机研究与发展 51 691]
[18] Wu T F, Zhou C L, Wang X H, Huang X X, Chen Z Q, Wang R B 2014 Acta Phy. Sin. 63 240501 (in Chinese) [吴腾飞, 周昌乐, 王小华, 黄孝喜, 谌志群, 王荣波 2014 物理学报 63 240501]
[19] Kermack W O, McKendrick A G 1991 Bulletin of mathematical biology 53 33
[20] Holme P, Kim B J 2002 Phys. Rev. E 65 026107
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[1] Java A, Song X, Finin T, Tseng B 2007 Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis San Jose, USA, August 12, 2007 p56
[2] Huang F H, Peng J, Ning L M 2014 Acta Phy. Sin. 63 160501 (in Chinese) [黄飞虎, 彭舰, 宁黎苗 2014 物理学报 63 160501]
[3] Qian C, Cao J D, Lu J Q, Kurths J 2011 Chaos 21 025116
[4] Liu C, Li J 2013 J. Changzhou Normal Univ. 29 91 (in Chinese) [刘冲, 李杰 2013 沧州师范学院学报 29 91]
[5] Galam S 2004 Physica A 336 56
[6] Crokidakis N, de Oliveira P M C 2014 Physica A 409 48
[7] Galam S 2002 Eur. Phys. J. B 25 403
[8] Crokidakis N, Forgerini F L 2010 Phys. Lett. A 374 3380
[9] Huang G, Cao J, Wang G, Qu Y 2008 Physica A 387 4665
[10] Huang G, Cao J, Qu Y 2009 Physica A 388 3911
[11] Galam S, Jacobs F 2007 Physica A 381 366
[12] Shen B, Liu Y 2010 Int. J. Mod. Phys. C 21 1001
[13] Xie J, Sreenivasan S, Korniss G, Zhang W, Lim C 2011 Phys. Rev. E 84 011130
[14] Sznajd-Weron K, Tabiszewski M, Timpanaro A M 2011 Eur. Lett. 96 48002
[15] Wu Y, Hu Y, He X H, Deng K 2014 Chin. Phys. B 23 060101
[16] Min L, Liu Z, Tang X Y, Chen M 2014 Acta Phy. Sin. 64 88901 (in Chinese) [闵磊, 刘智, 唐向阳, 陈矛 2014 物理学报 64 88901]
[17] Ding Z Y, Tia Y, Zhou B 2015 J. Comput. Res. Develop. 51 691 (in Chinese) [丁兆云, 贾焰, 周斌 2015 计算机研究与发展 51 691]
[18] Wu T F, Zhou C L, Wang X H, Huang X X, Chen Z Q, Wang R B 2014 Acta Phy. Sin. 63 240501 (in Chinese) [吴腾飞, 周昌乐, 王小华, 黄孝喜, 谌志群, 王荣波 2014 物理学报 63 240501]
[19] Kermack W O, McKendrick A G 1991 Bulletin of mathematical biology 53 33
[20] Holme P, Kim B J 2002 Phys. Rev. E 65 026107
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