Peter-Paul van Maanen
ABSTRACT
The aim of this study is to better understand social influence in online social media. Therefore, we propose a method in which we implement, validate and improve an individual behavior model. The behavior model is based on three fundamental behavioral principles of social influence from the literature: 1) liking, 2) social proof and 3) consistency. We have implemented the model using an agent-based modeling approach. The multi-agent model contains the social network structure, individual behavior parameters and the scenario that are obtained from empirical data. The model is validated by comparing the output of the multi-agent simulation with empirical data. We demonstrate the method by evaluating five versions of behavior models applied to the use case of Twitter behavior about a talent show on Dutch television.
- J. Holland, Hidden Order: How Adaptation Builds Complexity. Reading, MA: Addison Wesley, 1995. Google Scholar
Digital Library
- M. Johns, "Human behavior modeling within an integrative framework," Ph.D. dissertation, University of Pennsylvania, 2007. Google ScholarDigital Library
- P. R. Monge and N. S. Contractor, Theories of Communication Networks. Oxford University Press, 2003.Google Scholar
- C. M. Macal, "To agent-based simulation from system dynamics," in Proceedings of the 2010 Winter Simulation Conference, B. Johansson, S. Jain, J. Montoya-Torres, J. Hugan, and E. Ycesan, Eds., 2010. Google ScholarDigital Library
- E. Bonabeau, "Agent-based modeling: Methods and techniques for simulating human systems," Proceedings of the National Academy of Sciences of the United States of America, vol. 99, no. 3, pp. 7280--7287, May 14 2002.Google ScholarCross Ref
- M. Remondino, "Reactive and deliberative agents applied to simulation of socio-economical and biological systems," International Journal of Simulation, vol. 6, no. 12--13, pp. 11--25, 2005.Google Scholar
- T. C. Schelling, Micromotives and Macrobehavior. Norton, 1978, vol. 17.Google Scholar
- F. M. Bass, "A new product growth for model consumer durables," Management Science, vol. 15, p. 215227, 1969.Google ScholarDigital Library
- J. D. Watts and P. S. Dodds, "Influentials, networks, and public opinion formation," Journal of Consumer Research, vol. 34, no. 4, pp. 441--58, 2007.Google ScholarCross Ref
- L. Weng, A. Flammini, A. Vespignani, and F. Menczer, "Competition among memes in a world with limited attention," Scientific Reports, vol. 2, 2012, 29 March.Google Scholar
- H. Simon, "A behavioral model of rational choice," Quarterly Journal of Economics, vol. 69, p. 99188, 1955.Google ScholarCross Ref
- M. Bratman, Intention, plans, and practical reason. Harvard University Press Cambridge, MA., 1987.Google Scholar
- A. Rao and M. P. Georgeff, "BDI-agents: From theory to practice," in Proceedings of the First International Conference on Multiagent Systems (ICMAS'95), 1995, pp. 312--319.Google Scholar
- I. Ajzen, "The theory of planned behavior," Organizational behavior and human decision processes, vol. 50 (2), pp. 179--211, 1991.Google Scholar
- Z. D. Zhang T., "Agent-based simulation of consumer purchase decision-making and the decoy effect," Journal of Business Research, vol. 60, pp. 912--922, 2007.Google ScholarCross Ref
- R. B. Cialdini, Influence: Science and practice, 4th ed. Boston: Allyn & Bacon, 2001.Google Scholar
- S. Koster, "Modelling individual and collective choice behaviour in social networks: An approach combining a nested conditional logit model with latent classes and an agent based model," Master's thesis, Erasmus University Rotterdam, 2012.Google Scholar
- J. Goldenberg, O. Lowengart, and D. Shapira, "Zooming in: Self-emergence of movements in new product growth," Marketing Science, vol. 28, no. 2, p. 274292, 2009. Google ScholarDigital Library
- "The Voice Kids," Talpa Media Holding, February-March (season 1) 2012, premiered on RTL 4. {Online}. Available: http://www.thevoicekids.nlGoogle Scholar
- M. Wedel and W. DeSarbo, "A mixture likelihood approach for generalized linear models," Journal of Classification, vol. 12, no. 1, pp. 21--55, 1995.Google ScholarCross Ref
- M. North, N. Collier, J. Ozik, E. Tatara, M. Altaweel, C. Macal, M. Bragen, and P. Sydelko, "Complex adaptive systems modeling with repast simphony," Complex Adaptive Systems Modeling, vol. 1, no. 3, 2013.Google Scholar
Index Terms
- An agent-based approach to modeling online social influence
Recommendations
Homophily and social influence among online casual game players
We examine homophily and social influence processes among online game players.Players tend not to initiate ties, unless there are other desirable properties.Players tend to have reciprocated ties.No strong homophily and social influence processes were ...
The future of online social networks (OSN)
Use of media content promotes the social usage patterns between the connected users.OSN cultivates the growing trend of video sharing more distinctly than photo content.Online videos alone domain the diffusion patterns of interactive activities on ...
How "social" is Social TV? The influence of social motives and expected outcomes on the usage of Social TV applications
The study analyzes the predictors of Social TV usage via different platforms.Social TV usage is spurred by the motive to communicate and the expectation of enjoyment and social gratification.Besides Twitter, instant messaging systems emerge as important ...
Comments