Info Traders, Innovation and Information Spread

  • Biser ZlatanovEmail author
  • Maya Koleva
Part of the Media Business and Innovation book series (MEDIA)


The main aim of this chapter is to define and explore the effects of the information spread and the actors who introduce fundamental changes in the information stream direction across the converging media channels. The actors in social networks and influence thresholds are important determinants that affect behaviour of the rest of the nodes in online social networks. Contrary to the popular notion it was proved during the study that the expression of opinion and high activity do not create influence. The most vocal nodes in the graphs were not the most influential or even next to the most influential.

The research presents evidence how technological convergence produces social divergence which is based on newly introduced “segregation” rules in the online social networks. The models of technology adoption and network structure are important and determine the effects of the information diffusion and the rate of contagion that affect the process of convergence, namely the shifts in cultural and social paradigms. While the quantity of information rises, the ability to process and the competence to operate with is bounded, this leads to the superiority of the most technologically and/or communicationally advanced individuals and organizations—the info traders. We define three categories (dimensions) of info traders and explain their role in the networks through the presented case studies.


Influence networks Information diffusion Rumours Influentials Social network analysis Info trader 


  1. Altucher, J. (2011a). 10 unusual things you didn’t know about Steve Jobs. Available from
  2. Altucher, J. (2011b). 10 unusual things you didn’t know about Steve Jobs. Available from
  3. Arthur, W. B. (1997). Competing technologies, increasing returns, and lock-in by historical small events. In B. Arthur (Ed.), Increasing returns and path dependence in the economy (pp. 13–32). Michigan: Michigan University Press.Google Scholar
  4. Barabasi, L., & Reka, A. (1999, October 15). Emergence of scaling in random networks. Science, 286, 509–512.Google Scholar
  5. Bass, F. (1969). A new product growth model for consumer durables. Management Science, 15, 215–228.CrossRefGoogle Scholar
  6. BBC News. (2008, January 7). Warner backs Sony blu ray format. Available from
  7. (2008). Discussion forum. Available:
  8. Bijker, W. E. (1995). Of bicycles, bakelites and bulbs: Towards a theory of sociotechnical change. Cambridge, MA: MIT Press.Google Scholar
  9. Bijker, W. E., & Pinch, J. T. (1984). The social construction of facts and artifacts: Or how the sociology of science and the sociology of technology might benefit of each other. In W. E. Bijker, P. T. Hughes, & J. T. Pinch (Eds.), The social construction of technological systems (pp. 17–50). Cambridge, MA: MIT Press.Google Scholar
  10. Bloch, F. (1995). Endogenous structures of association of oligopolies. The RAND Journal of Еconomics, 26(3), 537–556.CrossRefGoogle Scholar
  11. Bonacich, P. (1979). The common structure semigroup: А replacement for the Boorman and White joint reduction. American Journal of Sociology, 86, 159–166.CrossRefGoogle Scholar
  12. Callon, M. (1986). Some elements of a sociology of translation: Domestication of the scallops and the fishermen of St Brieuc Bay. In J. Law (Ed.), Power, action and belief: A new sociology of knowledge. London: Routledge & Kegan Paul.Google Scholar
  13. Chan, K., & Misra, S. (1990). Characteristics of the opinion leader: A new dimension. Journal of Advertising, 19, 53–60.CrossRefGoogle Scholar
  14. Christ, J., & Slowak, A. (2009). Why Blu-Ray vs. HD-DVD is not VHS vs. Betamax: The co-evolution of standard-setting consortia. FZID Discussion Papers, University of Hohenheim.Google Scholar
  15. Daidj, N., Grazia, C., & Hammoudi, A. (2010). Introduction to the non-cooperative approach to coalition formation: The case of the Blu-Ray/HD-DVD standards’ war. Journal of Media Economics, 23(4), 192–215.CrossRefGoogle Scholar
  16. Еasly, D., & Kleinberg, J. (2010). Networks, crowds, and markets: Reasoning about a highly connected world. Cambridge: Cambridge University Press.Google Scholar
  17. Granovetter, M. (1978). Threshold models of collective behavior. American Journal of Sociology, 83, 1420–1443.CrossRefGoogle Scholar
  18. Hart, S., & Kurz, M. (1983). Endogenous formation of coalitions. Econometrica, 51, 1047–1064.CrossRefGoogle Scholar
  19. Judd, S., & Kearns, M. (2008). Behavioral experiments in networked trade. In T. Sandholm, J. Riedl, & L. Fortnow (Eds.), Proceedings of the 2008 ACM Conference on Electronic Commerce (pp. 150–159). New York: Association for Computing Machinery.Google Scholar
  20. Katz, E., & Lazersfeld, P. (1955). Personal influence: The part played by people in the flow of mass communications. Glencoe, IL: Free Press.Google Scholar
  21. Kearns, M., Suri, S., & Montfort, N. (2006). An experimental study of the coloring problem on human subject networks. Science, 313, 824–827.CrossRefGoogle Scholar
  22. Latour, B. (2005). Reassembling the Social: An introduction to actor-network-theory. New York: Oxford University Press.Google Scholar
  23. Liebowitz, S. J., & Margolis, S. E. (1995). Path dependence, lock-in, and history. Journal of Law, Economics and Organization, 11(1), 205–226.Google Scholar
  24. May, C., & Finch, T. (2009). Implementation, embedding, and integration: An outline of normalization process theory. Sociology, 43(3), 535–554.CrossRefGoogle Scholar
  25. Merton, R. (1968). Patterns of influence: Local and cosmopolitan influentials. In R. Merton (Ed.), Social theory and social structure (pp. 441–474). New York: FreePress.Google Scholar
  26. Miller, J., & Page, S. (2004). Standing ovation problem. Available from
  27. Mizruchi, M. S., & Bunting, D. (1981). Influence in corporate networks: An examination of four measures. Administrative Science Quarterly, 26(3), 475–489.CrossRefGoogle Scholar
  28. North, D. (1981). Structure and change in economic history. New York: Norton.Google Scholar
  29. Nowotny, H., Scott, P., & Gibbons, M. (2001). Re-thinking science: Knowledge and the public in an age of uncertainty. Cambridge, UK: Polity Press.Google Scholar
  30. Rogers, E. (2003). Diffusion of innovations. New York: Free Press.Google Scholar
  31. Schelling, T. (1978). Micromotives and macrobehavior. New York: Norton.Google Scholar
  32. The Economist. (2011). Steve Jobs: Beauty justifies wealth. Available from
  33. Tidd, J. (2006). A review of innovation models. Imperial College London, 16 Google Scholar
  34. Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  35. Watts, D., & Dodd, P. (2007). Influentials, networks and public opinion formation. Journal of Consumer Research, 34, 441–458.CrossRefGoogle Scholar
  36. Wigand, R. T., & Frankwick, G. L. (1989). Inter–organizational communication and technology transfer: Industry–government–university linkages. International Journal of Technology Management, 4(1), 63–76.Google Scholar
  37. Willamson, O. (1985). The economic institutions of capitalism (pp. 55–60). New York: The Free Press.Google Scholar
  38. Zhang, Y., Zhou, S., Zhang, Z., Guan, J., Zhou, S. (2013). Rumor evolution in social networks. Physical Review E87. Available from
  39. Zlatanov, B., & Koleva, M. (2014). Networks of collective power: (Non)movements and semantic networks. Conference paper, ECREA Conference, Lisbon.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Sofia UniversitySofiaBulgaria

Personalised recommendations