publication . Conference object . Preprint . 2014

Anticipating activity in social media spikes

Higham, D. J.; Alexander V. Mantzaris; Grindrod, P.; Otley, A.; Laflin, P.;
Open Access
  • Published: 08 Jun 2014
Abstract
We propose a novel mathematical model for the activity of microbloggers during an external, event-driven spike. The model leads to a testable prediction of who would become most active if a spike were to take place. This type of information is of great interest to commercial organisations, governments and charities, as it identifies key players who can be targeted with information in real time when the network is most receptive. The model takes account of the fact that dynamic interactions evolve over an underlying, static network that records who listens to whom. The model is based on the assumption that, in the case where the entire community has become aware ...
Subjects
free text keywords: Computer Science - Social and Information Networks, Physics - Physics and Society
Related Organizations
Funded by
RCUK| Horizon: Digital Economy Hub at the University of Nottingham
Project
  • Funder: Research Council UK (RCUK)
  • Project Code: EP/G065802/1
  • Funding stream: EPSRC
22 references, page 1 of 2

[1] S. Aral, Social science: Poked to vote, Nature, 489 (2012), pp. 212{214.

[2] S. Aral and D. Walker, Identifying in uential and susceptible members of social networks, Science, 337 (2012), pp. 337{341.

[3] E. Bakshy, J. M. Hofman, W. A. Mason, and D. J. Watts, Everyone's an in uencer: quantifying in uence on Twitter, in Proceedings of the fourth ACM international conference on Web search and data mining, WSDM '11, New York, NY, USA, 2011, ACM, pp. 65{74.

[4] E. Bakshy, I. Rosenn, C. Marlow, and L. Adamic, The role of social networks in information di usion, in Proceedings of the 21st international conference on World Wide Web, WWW '12, New York, NY, USA, 2012, ACM, pp. 519{528. [OpenAIRE]

[5] J. Borge-Holthoefer, R. A. Baos, S. Gonzalez-Bailon, and Y. Moreno, Cascading behaviour in complex socio-technical networks, Journal of Complex Networks, 1 (2013), pp. 3{24.

[6] D. Centola, The spread of behavior in an online social network experiment, Science, 329 (2010), p. 1194. [OpenAIRE]

[7] M. Cha, H. Haddadi, F. Benevenuto, and K. P. Gummadi, Measuring user in uence in Twitter: The million follower fallacy, in in ICWSM 10: Proceedings of international AAAI Conference on Weblogs and Social, 2010.

[8] F. Ciulla, D. Mocanu, A. Baronchelli, B. Goncalves, N. Perra, and A. Vespignani, Beating the news using social media: the case study of American Idol, EPJ Data Science, 1 (2012), pp. 1{11.

[9] P. Farhi, Oreo's tweeted ad was Super Bowl blackout's big winner, Washington Post, (February 05, 2013).

[10] J. P. Gleeson, J. A. Ward, K. P. O'Sullivan, and W. T. Lee, Competition-induced criticality in a model of meme popularity, Phys. Rev. Lett., 112 (2014), p. 048701.

[11] G. H. Golub and C. F. Van Loan, Matrix Computations, The Johns Hopkins University Press, 3rd ed., 1996.

[12] N. J. Higham, Functions of Matrices: Theory and Computation, Society for Industrial and Applied Mathematics, Philadelphia, PA, USA, 2008.

[13] L. Katz, A new index derived from sociometric data analysis, Psychometrika, 18 (1953), pp. 39{43.

[14] H. Kwak, C. Lee, H. Park, and S. Moon, What is Twitter, a social network or a news media?, in Proceedings of the 19th international conference on World wide web, WWW '10, New York, NY, USA, 2010, ACM, pp. 591{600.

[15] P. Laflin, A. V. Mantzaris, F. Ainley, A. Otley, P. Grindrod, and D. J. Higham, Discovering and validating in uence in a dynamic online social network, Social Network Analysis and Mining, 3 (2013), pp. 1311{ 1323. [OpenAIRE]

22 references, page 1 of 2
Any information missing or wrong?Report an Issue