Abstract
Messages often spread within a population through unofficial - particularly web-based - media. Such ideas have been termed “memes.” To impede the flow of terrorist messages and to promote counter messages within a population, intelligence analysts must understand how messages spread. We used statistical language processing technologies to operationalize “memes” as latent topics in electronic text and applied epidemiological techniques to describe and analyze patterns of message propagation. We developed our methods and applied them to English-language newspapers and blogs in the Arab world. We found that a relatively simple epidemiological model can reproduce some dynamics of observed empirical relationships.
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References
Allen, L.J.: An Introduction to Mathematical Biology. Prentice Hall, Englewood Cliffs (2007)
Anderson, R.M., May, R.M.: Infectious Diseases of Humans, Dynamics and Control. University Press, Oxford (1992)
BlogTrackers: Combining Domain Knowledge and Novel Search Capabilities, http://www.public.asu.edu/~huanliu/projects/BlogTrackers/
Dawkins, R.: The Selfish Gene. Oxford University Press, Oxford (1976)
Diekmann, O., Heesterbeek, J.A.P.: Mathematical Epidemiology of Infectious Diseases: Model Building, Analysis, and Interpretation. John Wiley & Son, Ltd., Chichester (2000)
Edelstein-Keshet, L.: Mathematical Models in Biology. McGraw-Hill, Boston (1988)
Griffiths, T.L., Steyvers, M., Tenenbaum, J.B.: Topics in semantic representation. Psychological Review 114(2), 211–244 (2007)
Hoffman, T.: Probabilistic latent semantic indexing. In: Proceedings of the 22nd annual international ACMSIGIR conference on research and development in information retrieval, Berkeley, California, USA, pp. 50–57 (1999)
JSpider, http://j-spider.sourceforge.net/
Leskovec, J., Backstrom, L., Kleinberg, J.: Meme-tracking and the dynamics of the news cycle. In: Proc. 15th ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining (2009)
Intel Math Kernel Library, http://software.intel.com/en-us/intel-mkl/
Kermack, W.O., McKendrick, A.G.: A Contribution to the Mathematical Theory of Epidemics. Proc. Roy. Soc. Lond. 115A, 700–721 (1927)
Malthus, T.R.: An Essay on the Principle of Population, as it Affects the Future Improvement of Society, 6th edn., Murray, London, UK (1826)
Volterra, V.: Variations and fluctuations of the number of individuals in animal species living together. In: Animal Ecology. McGraw-Hill, New York (1931)
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McCormack, R., Salter, W. (2010). An Application of Epidemiological Modeling to Information Diffusion. In: Chai, SK., Salerno, J.J., Mabry, P.L. (eds) Advances in Social Computing. SBP 2010. Lecture Notes in Computer Science, vol 6007. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12079-4_48
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DOI: https://doi.org/10.1007/978-3-642-12079-4_48
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