Abstract
The aim of this paper is to discuss the possibility of understanding human social interaction in web communities by analogy with a disease propagation model from epidemiology. When an article is submitted by an individual to a social web service, it is potentially influenced by other participants. The submission sometimes starts a long and argumentative chain of articles, but often does not. This complex behavior makes management of server resources difficult and a more theoretical methodology is required. This paper tries to express these complex human dynamics by analogy with infection by a virus. In this first report, by fitting an epidemiological model to Bulletin Board System (BBS) logs in terms of a numerical triple, we show that the analogy is reasonable and beneficial because the analogy can estimate the community size despite the submitter’s information alone being observable.
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References
Abe, T.: What is Service Science Dec-05, FRI Research Report, No.246 (2005)
Fujiki, T., Nanno, T., Suzuki, Y., Okumura, M.: Identification of Bursts in a Document Stream. In: First International Workshop on Knowledge Discovery in Data Streams (in conjunction with ECML/PKDD 2004) (2004)
Gruhl, D., Guha, R., Kumar, R., Novak, J., Tomkins, A.: The Predictive Power of Online Chatter. In: Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 78–87. ACM Press, New York (2005)
Gruhl, D., Guha, R., Liben-Nowell, D., Tomkins, A.: Information Diffusion through Blogspace. In: Proceedings of the 13th International Conference on the World Wide Web, pp. 491–501. ACM Press, New York (2004)
Jacquez, J.A.: A Note on Chain-Binomial Models of Epidemic Spread: What is Wrong with the Reed–Frost Formulation? Mathematical Biosciences 87(1), 73–82 (1987)
Kleinberg, J.: Bursty and Hierarchical Structure in Streams. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2002)
Krebs, C.J.: Ecology. Benjamin-Cummings Publishing Company (2002)
Naruse, K., Kubo, M.: Lognormal Distribution of BBS Articles and its Social and Generative Mechanism. Web Intelligence, 103–112 (2006)
Newman, M.E.: Spread of Epidemic Disease on Networks. Physical Review E 66, 016128 (2002)
Nishiura, H., Inaba, H.: Prediction of Infectious Disease Outbreaks with Particular Emphasis on the Statistical Issues Using a Transmission Model. In: Proceedings of the Institute of Statistical Mathematics, vol. 54(2), pp. 461–480 (2006) (in Japanese)
Rahmandad, H., Sterman, J.: Heterogeneity and Network Structure in the Dynamics of Contagion: Comparing Agent-Based and Differential Equation Models, MIT Sloan School of Management, Cambridge MA 02142 (2004)
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Kubo, M., Naruse, K., Sato, H., Matubara, T. (2007). The Possibility of an Epidemic Meme Analogy for Web Community Population Analysis. In: Yin, H., Tino, P., Corchado, E., Byrne, W., Yao, X. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2007. IDEAL 2007. Lecture Notes in Computer Science, vol 4881. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77226-2_107
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DOI: https://doi.org/10.1007/978-3-540-77226-2_107
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77225-5
Online ISBN: 978-3-540-77226-2
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