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It's distributions all the way down!: Second order changes in statistical distributions also occur

Published online by Cambridge University Press:  26 February 2014

Mark T. Keane  and
Aaron Gerow
Mark T. Keane
Affiliation:
School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland. [email protected]/users/mark-keane
Aaron Gerow
Affiliation:
School of Computer Science and Statistics, University of Dublin, Trinity College, College Green, Dublin 2, Ireland. [email protected]/~gerowa
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Abstract

The textual, big-data literature misses Bentley et al.’s message on distributions; it largely examines the first-order effects of how a single, signature distribution can predict population behaviour, neglecting second-order effects involving distributional shifts, either between signature distributions or within a given signature distribution. Indeed, Bentley et al. themselves under-emphasise the potential richness of the latter, within-distribution effects.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 37 , Issue 1 , February 2014 , pp. 87
Copyright
Copyright © Cambridge University Press 2014 

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References

Asur, S. & Huberman, B. A. (2010) Predicting the future with social media. In: Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), pp. 492–99. IEEE.Google Scholar
Bentley, R. A., Garnett, P., O'Brien, M. J. & Brock, W. A. (2012) Word diffusion and climate science. PLoS ONE 7(11):e47966.Google Scholar
Bentley, R. A. & Ormerod, P. (2010) A rapid method for assessing social versus independent interest in health issues: A case study of “bird flu” and “swine flu”. Social Science and Medicine 71(3):482–85.Google Scholar
Choi, H. & Varian, H. (2012) Predicting the present with Google Trends. Economic Record 88(Suppl. s1):2–9.Google Scholar
Gerow, A. & Keane, M. T. (2011) Mining the web for the voice of the herd to track stock market bubbles. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, ed. Walsh, T., pp. 2244–49. AAAI Press.Google Scholar
Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S. & Brilliant, L. (2009) Detecting influenza epidemics using search engine query data. Nature 457(7232):1012–14.Google Scholar
Huang, J. H. & Chen, Y. F. (2006) Herding in online product choice. Psychology and Marketing 23(5):413–28.Google Scholar
Michel, J. B., Shen, Y. K., Aiden, A. P., Veres, A., Gray, M. K., Googles Book Team, Pickett, J. P., Hoiberg, D., Clancy, D., Norvig, P., Orwant, J., Pinker, S., Nowak, M. A. & Aiden, E. L. (2011) Quantitative analysis of culture using millions of digitized books. Science 331(6014):176–82.Google Scholar
Onnela, J.-P. & Reed-Tsochas, F. (2010) Spontaneous emergence of social influence in online systems. Proceedings of the National Academy of Sciences USA 107:18375–80.Google Scholar
Salganik, M. J., Dodds, P. S. & Watts, D. J. (2006) Experimental study of inequality and unpredictability in an artificial cultural market. Science 311(5762):854–56. doi:10.1126/science.1121066.Google Scholar
Shiller, R. J. (1995) Conversation, information and herd behaviour. American Economic Review 85:181–85.Google Scholar
Surowiecki, J. (2005) The wisdom of crowds. Anchor Books.Google Scholar