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Decision making in social networks

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The speed of meme propogation

Hi delta of percived Value make meme propogate faster

In a theoretical model on decisions in social networks (also called innovation adaptation), it was found that as the gap between the individual ́s perception of the options is high, the adoption speed increases if the dispersion of early adopters grows[1].

Long Tail law of speed of propogation

Meme propagate through 50% of a network in in short time, and reach 80% in longer time (in students connected through a mutual social network it takes 10 minutes to reach 50% and about 100 minutes to reach 80%). In Sina, a chinese microblogging network it was also found that memes propagate in bursts of power-law distribution, from very small hubs of opinion leaders[2]

Common opinion hub make meme propogate faster

It was also found that when there are hubs of common opinion the spread of adopting new ideas become faster[3].

Volume of propogation

According to Prato law, 20% of the people creates 80% of the massaging[4] and complat yo the power-law distrebution[5].

Network topology

number of hops between members

Networks tend to connect all members in maximum of six hops[6][7].

Max size of personal network

In twitter it seems that Dunbar's number is valid[8]

Opinion Change

Theory: Social influence networks and opinion change‏[9].

Opinion will change accroding to the following dinamic:

  • Cognitive personal weighing average: the influence of a decision on them.
  • The social relation and connection to other actors and the delta of decision from the other actors.
  • Determinism (groupthink): the relations to the others will yield groupthink.
  • Continuance: The changing of opinion will occur until the available option will play themselves up.

References

<references>
  1. ↑ Laciana, C. E., & Rovere, S. L. (2011). Ising-like agent-based technology diffusion model: Adoption patterns vs. seeding strategies. Physica A: Statistical Mechanics and Its Applications, 390(6), 1139–1149.
  2. ↑ Yuanyuan, B., & Zhanhong, X. (2011). Human activity pattern on microblogging interaction. In Information Management, Innovation Management and Industrial Engineering (ICIII), 2011 International Conference on (Vol. 3, pp. 303–306).
  3. ↑ Laciana, C. E., & Rovere, S. L. (2011). Ising-like agent-based technology diffusion model: Adoption patterns vs. seeding strategies. Physica A: Statistical Mechanics and Its Applications, 390(6), 1139–1149.
  4. ↑ Shirky, C. (2003). Power laws, weblogs, and inequality. Clay Shirky’s Writings about the Internet, 8.
  5. ↑ Zhu, K., Hui, P., Chen, Y., Fu, X., & Li, W. (2011). Exploring user social behaviors in mobile social applications. In Proceedings of the 4th Workshop on Social Network Systems (p. 3).
  6. ↑ Travers, J., & Milgram, S. (1969). An experimental study of the small world problem. Sociometry, 425–443.
  7. ↑ Zhu, K., Hui, P., Chen, Y., Fu, X., & Li, W. (2011). Exploring user social behaviors in mobile social applications. In Proceedings of the 4th Workshop on Social Network Systems (p. 3).
  8. ↑ Gonçalves, B., Perra, N., & Vespignani, A. (2011). Modeling users’ activity on twitter networks: Validation of dunbar's number. PloS One, 6(8), e22656.
  9. ↑ Friedkin, N. E., & Johnsen, E. C. (1999). Social influence networks and opinion change. Advances in Group Processes, 16(1), 1–29.
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