Abstract
Agent-based modeling (ABM) is a common computational analysis tool to study system dynamics. In the framework of ABM, the system consists of multiple autonomous and interacting agents. We can explore emergent collective patterns by simulating the individual operations and interactions between agents. As a case study, we present an experiment using an agent-based model to study how competition for limited user attention in a social network results in collective patterns of meme popularity. The model is inspired by the long tradition that represents information spreading as an epidemic process, where infection is passed along the edges of the underlying social network. The model also builds upon empirical observations on how individual humans behave online. The combination of social network structure and finite agent attention is sufficient for the emergence of broad diversity in meme popularity and lifetime. The case study illustrates how one can analyze the kind of emergent human computation that makes some memes very popular.
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References
Asur S, Huberman BA, Szabo G, Wang C (2011) Trends in social media: persistence and decay. In: Proceedings of international AAAI conference on weblogs and social media, Menlo Park
Axelrod R (1997) The complexity of cooperation: agent-based models of competition and collaboration. Princeton University Press, Princeton
Bakshy E, Mason WA, Hofman JM, Watts DJ (2011) Everyone’s an influencer: quantifying influence on twitter. In: Proceedings of ACM international conference on web search and data mining, Hong Kong
Barabási A-L, Albert R (1999) Emergence of scaling in random graphs. Science 286:509–511
Bonabeau E (2002) Agent-based modeling: methods and techniques for simulating human systems. Proc Natl Acad Sci 99(Suppl 3):7280–7287
Castellano C, Fortunato S, Loreto V (2009) Statistical physics of social dynamics. Rev Mod Phys 81(2):591
Crane R, Sornette D (2008) Robust dynamic classes revealed by measuring the response function of a social system. Proc Natl Acad Sci 105(41):15649–15653
Dawkins R (1989) The selfish gene. Oxford University Press, Oxford
Dunbar RIM (1998) The social brain hypothesis. Evol Anthr 6:178–190
Erdös P, Rényi A (1960) On the evolution of random graphs. Magyar Tud. Akad. Mat. Kutató Int. Közl 5:17–61
Goetz M, Leskovec J, McGlohon M, Faloutsos C (2009) Modeling blog dynamics. In: Proceedings of international AAAI conference on weblogs and social media, San Jose
Gonçalves B, Perra N, Vespignani A (2011) Validation of dunbar’s number in twitter conversations. PLOS One 6:e22656
Granovetter M (1973) The strength of weak ties. Am J Sociol 78:1360–1380
Holme P, Newman MEJ (2006) Nonequilibrium phase transition in the coevolution of networks and opinions. Phys Rev E 74(5)
Ienco D, Bonchi F, Castillo C (2010) The meme ranking problem: maximizing microblogging virality. In: Proceedings of IEEE international conference on data mining workshop, Sydney, pp 328–335
Lazer D, Pentland A, Adamic L, Aral S, Barabási AL, Brewer D, Christakis N, Contractor N, Fowler J, Gutmann M, Jebara T, King G, Macy M, Roy D, Alstyne MV (2009) Computational social science. Science, 323(5915):721–723
Lerman K, Ghosh R (2010) Information contagion: an empirical study of the spread of news on digg and twitter social networks. In: Proceedings of international AAAI conference on weblogs and social media, Washington DC
Leskovec J, Backstrom L, Kumar R, Tomkins A (2008) Microscopic evolution of social networks. In: Proceedings of SIGKDD international ACM conference on knowledge discovery and data mining, Las Vegas, pp 462–470
McPherson M, Lovin L, Cook J (2001) Birds of a feather: homophily in social networks. Annu Rev Sociol 27(1):415–444
Moussaid M, Helbing D, Theraulaz G (2009) An individual-based model of collective attention. In: Proceedings of European conference on complex systems, Warwick
Pastor-Satorras R, Vespignani A (2001) Epidemic spreading in scale-free networks. Phys Rev Lett 86:3200–3203
Simon H (1971) Designing organizations for an information-rich world. In: Greenberger M (eds) Computers, communication, and the public interest. Johns Hopkins, Baltimore, pp 37–52
Shi X, Adamic LA, Strauss MJ (2007) Networks of strong ties. Physica A 378:3347
Sun X, Kaur J, Milojevic S, Flammini A, Menczer F (2013) Social dynamics of science. Sci Rep 3(1069)
Vespignani A (2009) Predicting the behavior of techno-social systems. Science 325(5939):425–428
Watts DJ (2002) A simple model of global cascades on random networks. Proc Natl Acad Sci 99(9):5766–5771
Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393: 440–442
Weng L, Flammini A, Vespignani A, Menczer F (2012) Competition among memes in a world with limited attention. Sci Rep 2:335
Wu F, Huberman BA (2007) Novelty and collective attention. Proc Natl Acad Sci 104(45):17599–17601
Yang L, Sun T, Mei Q (2012) We know what @you #tag: does the dual role affect hashtag adoption? In: Proceedings of international ACM world wide web conference, Lyon
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Weng, L., Menczer, F. (2013). Computational Analysis of Collective Behaviors via Agent-Based Modeling. In: Michelucci, P. (eds) Handbook of Human Computation. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8806-4_61
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DOI: https://doi.org/10.1007/978-1-4614-8806-4_61
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