Public opinion co-performance model based on maximizing influence of positive information sources

LI Chuan; WANG Yaqiong; YAN Ying; CHEN Jingliang

doi:10.12299/jsues.22-0210

Volume 37 Issue 1

Mar. 2023

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Article Navigation > Journal of Shanghai University of Engineering Science > 2023 > 37(1): 88-95

LI Chuan, WANG Yaqiong, YAN Ying, CHEN Jingliang. Public opinion co-performance model based on maximizing influence of positive information sources[J]. Journal of Shanghai University of Engineering Science, 2023, 37(1): 88-95. doi: 10.12299/jsues.22-0210

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LI Chuan, WANG Yaqiong, YAN Ying, CHEN Jingliang. Public opinion co-performance model based on maximizing influence of positive information sources[J]. Journal of Shanghai University of Engineering Science, 2023, 37(1): 88-95. doi: 10.12299/jsues.22-0210

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Public opinion co-performance model based on maximizing influence of positive information sources

doi: 10.12299/jsues.22-0210

LI Chuan^1
,,
WANG Yaqiong^{2
,
,},
YAN Ying^{1, 2},
CHEN Jingliang¹

1.
Business School, University of Shanghai for Science and Technology, Shanghai 200093, China
2.
School of Economic and Management, Shanghai University of Political Science and Law, Shanghai 201701, China

Received Date: 2022-07-09
Publish Date: 2023-03-31

Abstract

Abstract

With the popularity of mobile social networking platforms, individuals can rapidly receive, disseminate and communicate information through mobile devices. However, the widespread dissemination of misinformation on these platforms exacerbates the frequency and extent of crisis propagation. Based on the maximization of the influence of positive information sources, a co-performance of strong-ties and weak-ties social platform (CSWSP) dissemination model was constructed, and the use of influential individuals in social networks to improve the dissemination efficiency of positive information was explored. Through systematic simulation experiments, it was found that the influence of information on individuals and the efficiency of information dissemination in the weak-ties social layer play a crucial role in the process of online crisis co-performance. Increasing the proportion of influential individuals can mitigate or suppress further escalation of public sentiment, thus enhancing public crisis management.
- network public opinion,
- information source influence,
- crisis governance

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References(26)

References

[1]	董靖巍. 基于复杂网络的网络舆情动态演进影响机制研究[D]. 哈尔滨: 哈尔滨工业大学, 2016.
[2]	LIU J G, REN Z M, GUO Q. Ranking the spreading influence in complex networks[J] . Physica A: Statistical Mechanies and its Applications,2013,392:4154 − 4159. doi: 10.1016/j.physa.2013.04.037
[3]	GAO S, MA J, CHEN Z M, et al. Ranking the spreading ability of nodes in complex networks based on local structure[J] . Physica A: Statistical Mechanics and its Applications,2014,403:130 − 147. doi: 10.1016/j.physa.2014.02.032
[4]	BAE J, KIM S. Identifying and ranking influential spreaders in complex networks by neighborhood coreness[J] . Physica A: Statistical Mechanics and its Applications,2014,395:549 − 559. doi: 10.1016/j.physa.2013.10.047
[5]	CHEN D B, XIAO R, ZENG A, et al. Path diversity improves the identification of influential spreaders[J] . EPL,2013,104(6):68006.
[6]	DODDS P S, PAYNE J L. Analysis of a threshold model of social contagion on degree-correlated networks[J] . Physical Review E,2009,79(6):066115. doi: 10.1103/PhysRevE.79.066115
[7]	TANG S T, TENG X, PEI S, et al. Identification of highly susceptible individuals in complex networks[J] . Physica A: Statistical Mechanics and its Applications,2015,432:363 − 372. doi: 10.1016/j.physa.2015.03.046
[8]	WEI D J, DENG X Y, ZHANG X G, et al. Identifying influential nodes in weighted networks based on evidence theory[J] . Physica A: Statistical Mechanics and its Applications,2013,392(10):2564 − 2575. doi: 10.1016/j.physa.2013.01.054
[9]	BOTTCHER L, WOOLLEY-MEZA O, GOLES E, et al. Connectivity disruption sparks explosive epidemic spreading[J] . Physical Review E,2016,93(4):042315.
[10]	CURATO G, LILLO F. Optimal information diffusion in stochastic block models[J] . Physical Review E,2016,94(3):032310.
[11]	LIU Q H, LU F M, ZHANG Q, et al. Impacts of opinion leaders on social contagions[J] . Chaos:An Interdisciplinary Journal of Nonlinear Science,2018,28(5):053103. doi: 10.1063/1.5017515
[12]	SRIVASTAVA A, CHELMIS C, PRASANNA V K. Computing competing cascades on signed networks[J] . Social Network Analysis and Mining,2016,6(1):82. doi: 10.1007/s13278-016-0392-3
[13]	GALSTYAN A, MUSOYAN V, COHEN P. Maximizing influence propagation in networks with community structure[J] . Physical Review E,2009,79(5):056102. doi: 10.1103/PhysRevE.79.056102
[14]	GALSTYAN A, COHEN P. Cascading dynamics in modular networks[J] . Physical Review E,2007,75(3):036109. doi: 10.1103/PhysRevE.75.036109
[15]	WANG S, LI B, LIU X J, et al. Division of community-based influence maximization algorithm[J] . Computer Engineering and Applications,2016,52(19):42 - 47.
[16]	KITSAK M, GALLOS L K, HAVLIN S, et al. Identification of influential spreaders in complex networks[J] . Nature physics,2010,6(11):888 - 893. doi: 10.1038/nphys1746
[17]	胡庆成. 基于复杂网络的信息传播模型研究[D]. 北京: 清华大学, 2015.
[18]	DOMINGOS P, RICHARDSON M. Mining the network value of customers [C] //Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. SanFrancisco: ACM, 2001: 57−66.
[19]	KEMPE D, KLEINBERG J, TARDOS É. Maximizing the spread of influence through a social network[C]//Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Washington: ACM, 2003: 137 − 146.
[20]	LESKOVEC J, KRAUSE A, GUESTRIN C, et al. Cost-effective outbreak detection in networks[C]//Proceedings of the 13th ACM SIGKDD International Conference on Knowledge D iscovery and data mining. San Jose: ACM, 2007: 420−429.
[21]	LIU C, ZHOU L X, FAN C J, et al. Activity of nodes reshapes the critical threshold of spreading dynamics in complex networks[J] . Physica A: Statistical Mechanics and its Applications,2015,432:269 − 278. doi: 10.1016/j.physa.2015.03.054
[22]	PASTOR-SATORRAS R, VESPIGNANI A. Epidemic spreading in scale-free networks.[J] . Physical Review Letters,2001,86(14):3200 − 3203. doi: 10.1103/PhysRevLett.86.3200
[23]	FAN C H, JIN Y, HUO L A, et al. Effect of individual behavior on the interplay between awareness and disease spreading in multiplex networks[J] . Physica A:Statistical Mechanics and its Applications,2016,461:523 − 530. doi: 10.1016/j.physa.2016.06.050
[24]	PASTOR-SATORRAS R, VESPIGNANI A. Evolution and structure of the Internet: A statistical physics approach[M]. Cambridge: Cambridge University Press, 2007.
[25]	PASTOR-SATORRAS R, VESPIGNANI A. Epidemic dynamics and endemic states in complex networks[J] . Physical Review E,2001,63(6):066117. doi: 10.1103/PhysRevE.63.066117
[26]	BARABASI A L, ALBERT R, JEONG H. Mean-field theory for scalefree random networks[J] . Physica A: Statistical Mechanics and its Applications,1999,272(1):173 − 187.