AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Journal of Social Computing Article
PDF (1.2 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

Collaborative Diffusion Model of Information and Behavior in Social Networks

School of Economics and Trade, Anhui Finance and Trade Vocational College, Hefei 230601, China
School of Computer and Information, Anqing Normal University, Anqing 246011, China
Sun Create Electronics Co., Ltd, Hefei 230092, China
School of Business, East China University of Science and Technology, Shanghai 200237, China
Show Author Information

Abstract

Information diffusion may lead to behaviors related to information content. This paper considers the co-existence of information and behavior diffusion in social networks. The state of users is divided into six categories, and the rules and model of collaborative diffusion of information and behavior are established. The influence of different parameters and conditions on the proportions of behavior diffusion nodes and information diffusion ones is analyzed experimentally. The results show that the proportion of nodes taking action in uniform networks is higher than that in non-uniform networks. Although users are more likely to take actions related to information content after spreading or knowing information, the results show that it has little influence on the proportion of users taking action. The proportion is mainly affected by the probability that users who do not take action become ones who take. The greater the probability, the less the proportion of nodes who know information. In addition, compared with choosing the same node as the initial information and behavior diffusion node, choosing different nodes is more beneficial to the diffusion of behaviors related to information content.

Keywords

social networkinformation diffusionbehavior diffusion

References

[1]

N. Zhang, H. Huang, B. Su, J. Zhao, and B. Zhang, Dynamic 8-state ICSAR rumor propagation model considering official rumor refutation, Phys. A Stat. Mech. Appl., vol. 415, pp. 333–346, 2014.
Crossref Google Scholar
[2]

Q. S. Sun, Y. Li, H. B. Hu, and S. L. Cheng, Modeling and simulation for information diffusion in online social networks via considering users’ forwarding habits and preferences, (in Chinese), Info. Sci., vol. 39, no. 2, pp. 120–128, 2021.
Google Scholar
[3]

G. Liu, Z. Liu, and Z. Jin, Dynamics analysis of epidemic and information spreading in overlay networks, J. Theor. Biol., vol. 444, pp. 28–37, 2018.
Crossref Google Scholar
[4]

Y. Lu and J. Liu, The impact of information dissemination strategies to epidemic spreading on complex networks, Phys. A Stat. Mech. Appl., vol. 536, p. 120920, 2019.
Crossref Google Scholar
[5]
X. -X. Zhan, C. Liu, G. -Q. Sun, and Z. -K. Zhang, Epidemic dynamics on information-driven adaptive networks, Chaos Solitons Fractals, vol. 108, pp. 196–204, 2018.
Crossref
[6]
Y. H. Cen, L. L. Tao, D. D. Ma, and X. S. Wang, A summary of behavior diffusion in social networks: Theory, influencing factors and two-tier research framework, (in Chinese), Info. The. and Pra., vol. 39, no. 8, pp. 133–138, 2016.
[7]
Z. W. Tian, Research on microblog information dissemination based on complex networks, PhD dissertation, School of Management, Harbin Institute of Technology, Harbin, China, 2012.
[8]

Q. Su, J. Huang, and X. Zhao, An information propagation model considering incomplete reading behavior in microblog, Phys. A Stat. Mech. Appl., vol. 419, pp. 55–63, 2015.
Crossref Google Scholar
[9]
Y. -P. Xiao, S. -Y. Li, and Y. -B. Liu, An information diffusion dynamic model based on social influence and mean-field theory, Acta Phys. Sin., vol. 66, no. 3, p. 030501, 2017.
Crossref
[10]
J. -P. Zhang, H. -M. Guo, W. -J. Jing, and Z. Jin, Dynamic analysis of rumor propagation model based on true information spreader, Acta Phys. Sin., vol. 68, no. 15, p. 150501, 2019.
Crossref
[11]

P. Sridevi, S. Niduthavolu, and L. N. Vedanthachari, Analysis of content strategies of selected brand tweets and its influence on information diffusion, J. Adv. Manag. Res., vol. 18, no. 2, pp. 227–249, 2021.
Crossref Google Scholar
[12]

J. Wang, B. Zhou, and W. Wang, Information spreading on multirelational networks, Phys. A Stat. Mech. Appl., vol. 517, pp. 21–28, 2019.
Crossref Google Scholar
[13]

H. Xu, T. Li, X. Liu, W. Liu, and J. Dong, Spreading dynamics of an online social rumor model with psychological factors on scale-free networks, Phys. A Stat. Mech. Appl., vol. 525, pp. 234–246, 2019.
Crossref Google Scholar
[14]

X. J. Ding and L. X. Li, The spreading dynamics of rumors in social media under the anti-silence spiral effect, (in Chinese), Sys. Eng. The. and Pra., vol. 40, no. 12, pp. 3149–3163, 2020.
Google Scholar
[15]
P. Jia, C. Wang, G. Zhang, and J. Ma, A rumor spreading model based on two propagation channels in social networks, Phys. A Stat. Mech. Appl., vol. 524, pp. 342–353, 2019.
Crossref
[16]

Q. Wang, Y. Jin, Z. Lin, S. Cheng, and T. Yang, Influence maximization in social networks under an independent cascade-based model, Phys. A Stat. Mech. Appl., vol. 444, pp. 20–34, 2016.
Crossref Google Scholar
[17]

L. Yang, Z. Li, and A. Giua, Containment of rumor spread in complex social networks, Inf. Sci., vol. 506, pp. 113–130, 2020.
Crossref Google Scholar
[18]

X. Lin, Q. Jiao, and L. Wang, Competitive diffusion in signed social networks: A game-theoretic perspective, Automatica, vol. 112, p. 108656, 2020.
Crossref Google Scholar
[19]
L. Wu, P. Sun, Y. Fu, R. Hong, X. Wang, and M. Wang, A neural influence diffusion model for social recommendation, in Proc. 42nd Int. ACM SIGIR Conf. Research and Development in Information Retrieval, Paris, France, 2019, pp. 235–244.
Crossref
[20]

S. Forouzandeh, A. R. Aghdam, S. Forouzandeh, and S. Xu, Addressing the cold-start problem using data mining techniques and improving recommender systems by cuckoo algorithm: A case study of Facebook, Comput. Sci. Eng., vol. 22, no. 4, pp. 62–73, 2020.
Crossref Google Scholar
[21]

S. Forouzandeh, A. Sheikhahmadi, A. R. Aghdam, and S. Xu, New centrality measure for nodes based on user social status and behavior on Facebook, Int. J. Web Inf. Syst., vol. 14, no. 2, pp. 158–176, 2018.
Crossref Google Scholar
[22]

M. Sharma, D. Kaushal, and S. Joshi, Adverse effect of social media on generation Z user’s behavior: Government information support as a moderating variable, J. Retail. Consum. Serv., vol. 72, p. 103256, 2023.
Crossref Google Scholar
[23]

S. Stieglitz and L. Dang-Xuan, Emotions and information diffusion in social media—Sentiment of microblogs and sharing behavior, J. Manag. Inf. Syst., vol. 29, no. 4, pp. 217–248, 2013.
Crossref Google Scholar
[24]
T. Xiang, Q. Li, W. Li, and Y. Xiao, A rumor heat prediction model based on rumor and anti-rumor multiple messages and knowledge representation, Inf. Process. Manag., vol. 60, no. 3, p. 103337, 2023.
Crossref
[25]

H. Liang, Decreasing social contagion effects in diffusion cascades: Modeling message spreading on social media, Telematics Inform., vol. 62, p. 101623, 2021.
Crossref Google Scholar
[26]
Y. Lee, S. -H. Kim, and K. C. Cha, Impact of online information on the diffusion of movies: Focusing on cultural differences, J. Bus. Res., vol. 130, pp. 603–609, 2021.
Crossref
[27]

S. Choudhary, R. Nayak, S. Kumari, and H. Choudhury, Analysing acculturation to sustainable food consumption behaviour in the social media through the lens of information diffusion, Technol. Forecast. Soc. Change, vol. 145, pp. 481–492, 2019.
Crossref Google Scholar
[28]

X. Sun, S. Hou, N. Cai, and W. Ma, Product information diffusion model and reasoning process in consumer behavior, Heliyon, vol. 6, no. 12, p. e05636, 2020.
Crossref Google Scholar
[29]

Q. Yin, Z. S. Wang, C. Y. Xia, and C. T. Bauch, Impact of co-evolution of negative vaccine-related information, vaccination behavior and epidemic spreading in multilayer networks, Com. in Non. Sci. and Num. Sim., vol. 109, p. 106312, 2022.
Crossref Google Scholar
[30]

L. Huo and Y. Yu, The impact of the self-recognition ability and physical quality on coupled negative information-behavior-epidemic dynamics in multiplex networks, Chaos Solitons Fractals, vol. 169, p. 113229, 2023.
Crossref Google Scholar
[31]

H. Guo, Z. Wang, S. Sun, and C. Xia, Interplay between epidemic spread and information diffusion on two-layered networks with partial mapping, Phys. Lett. A, vol. 398, p. 127282, 2021.
Crossref Google Scholar
[32]

P. He, S. Lovo, and M. Veronesi, Social networks and renewable energy technology adoption: Empirical evidence from biogas adoption in China, Energy Econ., vol. 106, p. 105789, 2022.
Crossref Google Scholar
[33]

B. P. Carvalho, C. Custódio, B. Geys, D. Mendes, and S. Peralta, Information, perceptions, and electoral behaviour of young voters: A randomised controlled experiment, Elect. Stud., vol. 84, p. 102625, 2023.
Crossref Google Scholar
[34]

W. Li, L. Tian, and H. Batool, Impact of negative information diffusion on green behavior adoption, Resour. Conserv. Recycl., vol. 136, p. 337–344, 2018.
Crossref Google Scholar
[35]

W. Li, L. Tian, X. Gao, and B. Pan, Impacts of information diffusion on green behavior spreading in multiplex networks, J. Clean. Prod., vol. 222, p. 488–498, 2019.
Crossref Google Scholar
Journal of Social Computing
Volume 4 Issue 3,
September 2023
Pages 243-253
DOI: 10.23919/JSC.2023.0016
Cite this article:
Sun Q, Wang Y, Sun G, et al. Collaborative Diffusion Model of Information and Behavior in Social Networks. Journal of Social Computing, 2023, 4(3): 243-253. https://doi.org/10.23919/JSC.2023.0016

333

Views

25

Downloads

0

Crossref

0

Scopus

Altmetrics
Received: 19 April 2023
Revised: 15 July 2023
Accepted: 22 August 2023
Published: 30 September 2023
© The author(s) 2023.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
Return