Linguistic creativity is a manifestation of communities’ and cultures’ innovativeness. The initial results will be presented of an empirical project analysing the character and speed of the social spread of winged words and neologisms in a microblogging site, using the tools of social network analysis applied to big-scale data. Investigating the diffusion of linguistic innovation requires an approach pooling competences from human, social, and computational sciences. Such a complex systems perspective can lead to a deeper understanding of how mutual relations and communication between Internet users impact the cultural evolution of language in time and space, and the shape and dynamics of the interactions themselves, delivering quantitative estimates on the expansion of linguistic expressions and allowing the prediction of future trends and their scale.
Altmann, E.G., Pierrehumbert, J.B., & Motter, A.E. (2011). Niche as a determinant of word fate in online groups. PLoS ONE, 6 (5): e19009.
Bandura, A. (1977). Social learning theory. Englewood Cliffs, NJ: Prentice Hall. Benson, P. (2010). SLA after YouTube: New literacies and new language learning. Inv. talk, Univ. Warsaw.
Blank, A. & Solomon, S. (2000). Power laws in cities population, financial markets and internet sites: Scaling and systems with a variable number of components. Physica A, 287, 279-288.
Castells, M. (2007). Communication, power and counter-power in the network society. International Journal of Communication, 1 (1), 238-266.
Castells, M. (2009). Communication power. New York: Oxford University Press. Clauset, A., Shalizi, C.R., & Newman, M.E.J. (2009). Power-law distributions in empirical data. SIAM Review, 51 (4), 661-703.
Dawkins, R. (1976). The selfish gene. Oxford: Oxford University Press.
Davies, J. (2007). Display, identity and the everyday: Self-presentation through digital image sharing. Discourse, Studies in the Cultural Politics of Education, 28 (4), 549-564.
Dennett, D.C. (1990). Memes and the Exploitation of Imagination. Journal of Aesthetics and Art Criticism, 48 (2), 127-135.
Gabaix, X. (1999). Zipf’s law for cities: An explanation. Quarterly Journal of Economics, 114, 739-67.
Gibrat, R. (1931). Les Inégalités économiques. Paris: Librairie du Recueil Sirey.
Gladwell, M. (2010). Small change: Why the revolution will not be tweeted. The New Yorker, October 4, 2010.
Global Language Monitor (2009). Death of Michael Jackson. Retrieved from:
Granovetter, M. (1978). Threshold models of collective behavior. The American Journal of Sociology, 83 (6), 1420-1443.
Henrich, J., & Boyd, R. (2002). On modeling cognition and culture: Why cultural evolution does not require replication of representations. Journal of Cognition and Culture 2(2), 87-112.
Newman, M.E.J. (2005). Power laws, Pareto distributions and Zipf’s law. Contemporary Physics, 46 (5), 323-351.
O’Dell, J. (2010). Does online buzz mean better TV ratings? Retrieved from:
Paradowski, M.B. (2009). Applying a complexity science approach to analysing and modelling language phenomena. Invited lecture, Higher English Language Seminar, Dept English, Stockholm Univ.
Paradowski, M.B. & Jonak, L. (2012). Understanding the social cascading of geekspeak and the upshots for social cognitive systems. In A. Galton & Z. Wood (Eds.), Understanding and modelling collective phenomena (pp. 27-32). AISB/ IACAP World Congress, 2-6 July 2012, Birmingham, UK.
Paradowski, M.B., Jonak, L., & Kuscsik, Z. (2010). Tracking the diffusion of lexical innovation in online social networks. Workshop on Data-Driven Dynamical Networks, l’Ecole de Physique des Houches.
Project for Excellence in Journalism (2010). New media, old media. How Blogs and Social Media Agendas Relate and Differ from Traditional Press. Retrieved from:
Rogers, E.M. (2003). Diffusion of innovations. New York: Free Press.
Sornette, D. & Cont, R. (1997). Convergent multiplicative processes repelled from zero: Power laws and truncated power laws. Journal of Physics I, 7 (3), 431-444.
Sperber, D. (2000). An objection to the memetic approach to culture. In R. Aunger (Ed.), Darwinizing culture: The status of memetics as a science (pp. 163-173). Oxford: Oxford University Press.
Tabor, W. & Tanenhaus, M.K. (2001). Dynamical systems for sentence processing. In M.H. Christiansen & N. Chater (Eds.), Connectionistpsycholinguistics (pp. 177211). Westport, CT: Ablex. de Tarde, G. (1890). Les lois de limitation: etude sociologique. Paris: Felix Alcan.
The “Five Graces Group”, Beckner, C., Blythe, R., Bybee, J., Christiansen, M.H., Croft, W., Ellis, N.C., Holland, J., Ke, J., Larsen-Freeman, D., & Schoenemann, T. (2009). Language is a complex adaptive system: Position paper. Language Learning, 59 (Suppl. 1), 1-26.
Valente, T.W. (1995). Network models of the diffusion of innovations. Cresskill, NJ, Hampton Press.
Van Geert, P. (2009). A comprehensive dynamic systems theory of language development. In K. De Bot & R.W. Schrauf (Eds.), Language development over the life span (pp. 60-104). New York/London: Routledge.
Vander Wal, Th. (2007, Feb 2). Folksonomy coinage and definition. Retrieved from:
Watts, D.J. (2007). The accidental influentials. Harvard Business Review, 85 (2), 22-23.
Winters, M.E., Tissari, H., & Allan, K. (2010). Historical cognitive linguistics. Berlin: Mouton de Gruyter.
Zipf, G.K. (1949). Human behavior and the principle of least effort: An introduction to human ecology. Cambridge, MA: Addison-Wesley.