%0 Book Section %1 citeulike:3797444 %A Kubo, Masao %A Naruse, Keitaro %A Sato, Hiroshi %A Matubara, Takashi %D 2007 %J Intelligent Data Engineering and Automated Learning - IDEAL 2007 %K analogy, epidemiology, interaction, social\_network, web %P 1073--1080 %R 10.1007/978-3-540-77226-2\_107 %T The Possibility of an Epidemic Meme Analogy for Web Community Population Analysis %U http://dx.doi.org/10.1007/978-3-540-77226-2\_107 %X 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.

@incollection{citeulike:3797444, 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.}, added-at = {2009-12-11T23:34:46.000+0100}, author = {Kubo, Masao and Naruse, Keitaro and Sato, Hiroshi and Matubara, Takashi}, biburl = {https://www.bibsonomy.org/bibtex/2a7be8accdfc6704a7be955ef8c0d03e3/djsaab}, citeulike-article-id = {3797444}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/978-3-540-77226-2\_107}, citeulike-linkout-1 = {http://www.springerlink.com/content/j20n7v433776615m}, description = {djsaab's CiteULike library 20091211}, doi = {10.1007/978-3-540-77226-2\_107}, interhash = {7176c51db80e9ae3a0c4b3df4640c64e}, intrahash = {a7be8accdfc6704a7be955ef8c0d03e3}, journal = {Intelligent Data Engineering and Automated Learning - IDEAL 2007}, keywords = {analogy, epidemiology, interaction, social\_network, web}, pages = {1073--1080}, posted-at = {2008-12-16 15:14:05}, priority = {2}, timestamp = {2009-12-11T23:34:56.000+0100}, title = {The Possibility of an Epidemic Meme Analogy for Web Community Population Analysis}, url = {http://dx.doi.org/10.1007/978-3-540-77226-2\_107}, year = 2007 }