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Journal of Library and Information Science in Agriculture

   

Modeling and Simulation of Coupled Network Public Opinion Propagation Across Social Media Platforms

Deming ZHENG1, Sijia LI1(), Jianlong ZHENG1, Zhaoxin WANG2   

  1. 1. Research Center for Network Public Opinion Governance, China People's Police University, Langfang 065000
    2. Handan Municipal Public Security Bureau, Handan 056000
  • Received:2024-05-28 Online:2024-11-20
  • Contact: Sijia LI

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Abstract:

[Purpose/Significance] With the advancement of communication technology and the popularization of smart mobile devices, social media platforms have developed rapidly. The increased exchange of information via social media platforms has increased the complexity and the spread of public opinion. Understanding the topological structure of information dissemination networks across platforms and the mechanisms of public opinion dissemination on them is of great significant guiding importance for predicting trends in online public opinion and formulating guiding strategies. [Methods/Processes] Based on the SEIR model of infectious diseases, a model of the spread of public opinion on cross-social platform coupled networks was constructed. Using the Monte Carlo simulation method, the experiment explored the effect of the degree of coupling between users and the type of coupling between platforms on the speed and extent of the spread of public opinion, revealing the laws of the spread of public opinion in cross-social platform coupled networks. [Results/Conclusions] The simulation results indicate that, compared to single-layer networks, the inter-layer edges formed by users across platforms significantly promote the spread of public opinion. As the degree of inter-layer coupling in the network increases, the speed of public opinion diffusion accelerates and the extent of diffusion increases. For inter-layer coupling modes, homophily links are more conducive to the spread of public opinion information than heterophily links. The results of this study provide valuable guidance for public opinion management. In a networked environment, government departments should disseminate accurate and authoritative information promptly through a variety of channels to gain the upper hand in public opinion. Given the positive correlation between inter-layer coupling and the speed at which public opinion spreads, it is crucial to closely monitor other platforms with a high degree of user overlap with the current one, and to accurately understand key data such as user overlap, activity levels, and differentiation between platforms. This will help to identify key users with high influence and activity, and enable more targeted, personalized guidance strategies. For platforms with a high degree of user assortative connectivity, the emergency response to public opinion should be enhanced. Conversely, platforms with a high degree of user disassortative connectivity should implement more stringent information filtering and response measures. The model constructed in this paper can simulate the basic structure of real coupled social networks and the process of spreading public opinion to some extent, but there are still shortcomings. Future research can introduce more factors, such as user behavior characteristics and content attributes, to construct a more refined public opinion diffusion model, thereby enhancing the model's capability to describe the real-world mechanisms of public opinion information diffusion.

Key words: cross-social platform, coupled network, public opinion propagation, infectious disease model, degree of inter-layer coupling, inter-layer coupling pattern

CLC Number: 

  • TP391

Table 1

The overall user overlap rate of typical new media platforms in September 2023"

平台组合 整体重合率/% 重合用户（占前者）/% 重合用户（占后者）/%
抖音&微博 36.4 44.1 67.7
抖音&快手 33.2 40.2 65.4
微博&哔哩哔哩 24.2 27.9 64.4
哔哩哔哩&小红书 24.1 37.9 39.9
抖音&小红书 21.5 22.4 83.7
抖音&哔哩哔哩 20.4 21.8 77.1
微博&小红书 20.2 23.7 57.6
微博&快手 17.7 29.3 31.0
快手&小红书 11.1 14.3 32.9
快手&哔哩哔哩 9.2 12.3 26.9

Fig.1

User profile of typical new media platforms in September 2023"

Fig.2

Percentage of interactive engagement for top content types on typical new media platforms in September 2023"

Fig.3

Schematic diagram of double-layer coupled network"

Fig.4

Schematic diagram of SEIR model"

Fig.5

Schematic diagram of inter-layer propagation in the network"

Fig.6

The evolution over time of the number of public opinion posts on the “Yu Huaying Child Trafficking Case Second Trial” and the density of propagation state nodes on the coupled network"

Fig.7

The process of public opinion propagation on single-layer and coupled networks"

Fig.8

The process of public opinion propagation on coupled networks under different inter-layer coupling levels"

Fig.9

The evolution over time of the number of public opinion posts on the “Yu Huaying Child Trafficking Case Second Trial” across social platforms with different degrees of interconnectivity"

Fig.10

The process of public opinion propagation on coupled networks under different inter-layer coupling modes"

Fig.11

The evolution over time of the number of public opinion posts on the “Yu Huaying Child Trafficking Case Second Trial” across social platforms with different coupling patterns"

1
中国互联网络信息中心. 第52 次中国互联网络发展状况统计报告[EB/OL]. [2024-03-22].
2
QuestMobile研究院. 2023年新媒体生态洞察[EB/OL]. [2023-11-21].
3
KERMACK W O, MCKENDRICK A G. A contribution to the mathematical theory of epidemics[J]. Proceedings of the royal society of London series A, containing papers of a mathematical and physical character, 1927, 115(772): 700-721.
4
李思佳, 张鹏, 夏一雪, 等. 基于信息吸引力和相关性的双网络舆情交互传播建模与仿真研究[J]. 情报杂志, 2023, 42(5): 119-128.
LI S J, ZHANG P, XIA Y X, et al. Interactive propagation mode and simulation of dual network public opinions based on information attraction and relevance[J]. Journal of intelligence, 2023, 42(5): 119-128.
5
马宇彤, 胡平. 考虑“关键用户”影响力及“热点问题”识别的改进SEIR知识传播模型[J]. 预测, 2021, 40(5): 48-55.
MA Y T, HU P. The improved SEIR knowledge dissemination model based on key user and hot spots influences[J]. Forecasting, 2021, 40(5): 48-55.
6
曾荣燊, 李弼程, 陈刚, 等. 基于线上-线下超网络模型的舆论演化仿真分析[J]. 计算机应用研究, 2024, 41(2): 507-514.
ZENG R S, LI B C, CHEN G, et al. Analysis of public opinion evolution based on online-offline supernetwork model[J]. Application research of computers, 2024, 41(2): 507-514.
7
卢友军, 吴森, 魏嘉银, 等. 带时间延迟和强制静默的SICR谣言传播模型[J]. 郑州大学学报(工学版), 2024, 45(6): 83-91.
LU Y J, WU S, WEI J Y, et al. A SICR rumor propagation model with time delay and enforced silence[J]. Journal of Zhengzhou University (engineering science), 2024, 45(6): 83-91.
8
陈帅. 基于多层耦合网络的舆情传播控制研究[J]. 系统仿真学报, 2020, 32(12): 2353-2361.
CHEN S. Research on dissemination and control of public opinion based on multilayer coupled network[J]. Journal of system simulation, 2020, 32(12): 2353-2361.
9
陈帅. 基于SEIR的双层社交网络舆情传播研究[J]. 情报探索, 2020(9): 29-36.
CHEN S. Research on public opinion communication in two-tier social network based on SEIR[J]. Information research, 2020(9): 29-36.
10
卓新建, 李晓燕, 徐文哲. 基于多层网络的舆情传播模型[J/OL]. 系统科学与数学, 2024: 1-18.
ZHUO X J, LI X Y, XU W Z. Public opinion propagation model based on multi-layer network[J/OL]. Journal of systems science and mathematical sciences, 2024: 1-18.
11
杨磊, 封永雪, 侯贵生, 等. 个体因素与外部环境共同作用下的跨平台社交网络舆情传播模型研究[J]. 现代情报, 2021, 41(3): 138-147, 158.
YANG L, FENG Y X, HOU G S, et al. Research on cross-platform social network public opinion propagation model under the joint action of individual factors and external environment[J]. Journal of modern information, 2021, 41(3): 138-147, 158.
12
罗章凯, 裴忠民, 熊伟, 等. 双层均质耦合网络信息传播动力学研究[J]. 计算机仿真, 2023, 40(1): 43-47.
LUO Z K, PEI Z M, XIONG W, et al. Research on dynamics of information spreading on double-layer coupled networks[J]. Computer simulation, 2023, 40(1): 43-47.
13
魏静, 张耀曾, 朱恒民, 等. 基于动态权值和不对称耦合网络的改进Deffuant模型舆情演化解析[J]. 情报杂志, 2021, 40(8): 142-151, 158.
WEI J, ZHANG Y Z, ZHU H M, et al. Analysis of the evolution of improved deffuant model of public opinion based on dynamic weights and asymmetric coupling networks[J]. Journal of intelligence, 2021, 40(8): 142-151, 158.
14
刘继, 郭一凡. 基于耦合网络的舆情观点演化机制分析[J/OL]. 情报杂志, 2024: 1-9.
LIU J, GUO Y F. Analysis of the evolution mechanism of public opinion viewpoints based on coupled neworks[J/OL]. Journal of intelligence, 2024: 1-9.
15
NEWMAN M E J. Assortative mixing in networks[J]. Physical review letters, 2002, 89(20): 208701.
16
田占伟, 王亮, 刘臣. 基于复杂网络的微博信息传播机理分析与模型构建[J]. 情报科学, 2015, 33(9): 15-21.
TIAN Z W, WANG L, LIU C. Information dissemination mechanism analysis and model construction of micro-blog based on complex network[J]. Information science, 2015, 33(9): 15-21.
17
韩一士, 徐雨欣, 卢甜甜. 一种基于耦合网络的RD-IHSAT网络谣言传播模型[J]. 电信科学, 2023, 39(2): 118-131.
HAN Y S, XU Y X, LU T T. A model of RD-IHSAT rumor dissemination based on coupling network[J]. Telecommunications science, 2023, 39(2): 118-131.
18
BULDYREV S V, PARSHANI R, PAUL G, et al. Catastrophic cascade of failures in interdependent networks[J]. Nature, 2010, 464(7291): 1025-1028.
19
BULDYREV S V, SHERE N W, CWILICH G A. Interdependent networks with identical degrees of mutually dependent nodes[J]. Physical review E, Statistical, nonlinear, and soft matter physics, 2011, 83(1 Pt 2): 016112.
20
SIDOROV S, FAIZLIEV A, TIKHONOVA S. An extension of the susceptible–infected model and its application to the analysis of information dissemination in social networks[J]. Modelling, 2023, 4(4): 585-599.
21
XIA L L, JIANG G P, SONG B, et al. Rumor spreading model considering hesitating mechanism in complex social networks[J]. Physica A: Statistical mechanics and its applications, 2015, 437: 295-303.
22
梁冉, 徐雅斌. 基于改进SEIR模型的网络舆情传播研究[J]. 计算机仿真, 2023, 40(5): 333-340.
LIANG R, XU Y B. Research on network public opinion communication based on improved SERI model[J]. Computer simulation, 2023, 40(5): 333-340.
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