Abstract
This research presents a thorough study of the strategies deployed for information dissemination on social networks. The strategies are modeled for identifying factors proving pivotal in influencing public opinions. The public is categorized into active and passive participants to discuss the study of these strategies for disseminating informational pieces such as images, sketches, text, and so on social networks, based on computer modeling of the social network structure alongside modeling of the behavior of network users. The objective of this research is to benchmark the proposed model on the effectiveness of information dissemination strategies that can be proposed to spread informational influences on social networks. As a rule, “Opinion Leaders” in the purpose network sectors are used to spread information on social media among the largest number of users in the least amount of time. Experiments have proved the effectiveness of the information dissemination model using “Opinion Leaders”, as well as the efficiency of the methods of information dissemination for different winning structural positions in a social network. Experiments have also confirmed the high effectiveness in attracting “Opinion Leaders” to the dissemination processes.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Aho Alfred V, Hopcroft John E, Ullman Jeffrey D, Bracht Glenn H, Hopkin Kenneth D, Johnson CA (1983) Data structures and algorithms. Addison-Wesley
Arrami S, Oueslati W, Akaichi J (2018) Detection of Opinion Leaders in social networks: a survey International conference on intelligent interactive multimedia systems and services. Springer, pp 362–370
Bakulin O, Romakina M (2019) On the problem of photo verification in modern media. Media Almanac Issue 2(91):31–42
Boccaletti S, Latora V, Moreno Y, Chavez M, Hwang DU (2006) Complex networks: structure and dynamics. Phys Rep 424(4–5):175–308
Chen Y (2019) A novel algorithm for mining opinion leaders in social networks. World Wide Web 22:1279–1295. https://doi.org/10.1007/s11280-018-0586-x
Chkhartishvili AG (2004) Game-theoretic models of information management. PMSOFT, 227
Daley D, Kendall DG (1965) Stochastic rumors. J Inst Math Appl 1:42–55
Golub B, Jackson M (2010) Naive Learning in Social Networks: convergence influence and the wisdom of crowds. Am Econ J Microecon Issue 2(1):112–149
Grabisch M, Li F (2019) Anti-conformism in the threshold model of collective behavior. Dynamic Games Appl 10(2):444–477
Gubanov D, Novikov D, Chkhartishvili A (2009) Models of information influence and information management in social networks. Manag Socio-Econ Syst 5:205–281
Gubanov D, Novikov D, Chkhartishvili A (2010) Social networks: models of informational influence, management and confrontation, 2nd edn. Stereotyped, Publishing house of physical and mathematical literature, p 228
Gupta A, Lamba H, Kumaraguru P, Joshi A (2013) Faking Sandy: characterizing and identifying fake images on Twitter during Hurricane Sandy, In Proceedings of the 22nd International Conference on World Wide Web (WWW ‘13 Companion). Association for Computing Machinery. pp. 729–736
Katz E, Lazarsfeld P (2005) Personal influence: the part played by people in the flow of mass communications paperback. Routledge
Kempe D, Kleinberg J, Tardos E (2003) Maximizing the spread of influence through a social network, proceedings of the 9th ACM SIGKDD international conference on knowledge discovery and data mining, pp. 137–146
Kermack W, McKendric AG (1927) A contribution to the mathematical theory of epidemics, II—the problem of endemicity. Proc R Soc Lond Ser A 115(772):700–721. https://doi.org/10.1098/rspa.1927.0118
Kurban O (2017) Information wars in social online networks. Boris Grinchenko University of Kyiv, p 392
Lazutkina E (2016) Leaders of opinions in the information space of the blogosphere of Runet. Tomsk State Univ J Series History Philol 6:51–59
Lunde D, Graivoronskaya A, Berezin B (2016) A multi-agent model of information dissemination in a social network, Systems of storage and mass distribution of data: Registration, storage, and processing of data, 18(1)
Marra F, Gragnaniello D, Cozzolino D, Verdoliva L (2018) Detection of GAN-generated fake images over social networks. IEEE Confer Multimed Inform Proc Retriev (MIPR). https://doi.org/10.1109/MIPR.2018.00084
Miroshnichenko P, Nesterenko A (2015) (2017) Formationfeatures of public opinion. Soc Commun 4(24):36–41
Sanin M, Barkov E (2016) The effectiveness of blogging as a marketing tool, scientific journal NRU ITMO series. Econ Environ Manag 2:107–1112
Schiff J (2007) Cellular automata: a discrete view of the world. Wiley
Sun G, Bin S (2018) A new Opinion Leaders detecting algorithm in multi-relationship online social networks. Multimed Tools Appl. https://doi.org/10.1007/s11042-017-4766-y
Toropov B (2016) An independent cascades model of repost distribution in an online social network. Cybernet Program 5:199–205
Ulichev O (2018) Mathematical model of information distribution and psychological influences in the segment of the social network. Scientif Works Collection Central Ukra Nat Tech Univ 30:165–174
Ulichev O, Meleshko E (2018) Software modeling of the spread of information-psychological influences in virtual social networks. ScientifIc-Tech J Modern Inform Syst 2(2):35–39
UNIAN information agency (2017) Rating of Ukrainian thought leaders in social networks. https://www.unian.info/society/2246929-rating-of-ukrainian-opinion-leaders-in-social-networks-rockstar-vakarchuk-overtakes-poroshenko.html. Accessed 1 Aug 2019
Waters DP (2012) Von Neumann’s theory of self-reproducing automata: a useful framework for biosemiotics. Biosemiotics 5(1):5–15
Acknowledgements
The authors acknowledge the proofreading provided by Dr. Haithem Mezni, University of Jendouba, and Dr. Sheroz Khan, Onaizah Colleges.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that there are no conflicts of interest regarding the publication of this paper.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Al-Oraiqat, A.M., Ulichev, O.S., Meleshko, Y.V. et al. Modeling strategies for information influence dissemination in social networks. J Ambient Intell Human Comput 13, 2463–2477 (2022). https://doi.org/10.1007/s12652-021-03364-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-021-03364-w