Difference between revisions of "Decision making in social networks"
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===Long Tail law of speed of propogation=== | ===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%) | + | 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<ref>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).</ref> |
===Common opinion hub make meme propogate faster=== | ===Common opinion hub make meme propogate faster=== | ||
Revision as of 06:03, 24 March 2015
Contents
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]. According to Prato law, 20% of the people creates 80% of the massaging[4].
Network topology
number of hops between members
Networks tend to connect all members in maximum of six hops[5].
Max size of personal network
In twitter it seems that Dunbar's number is valid[6]
Diffusion hops=
References
<references>- ↑ 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.
- ↑ 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).
- ↑ 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.
- ↑ 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).
- ↑ 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).
- ↑ Gonçalves, B., Perra, N., & Vespignani, A. (2011). Modeling users’ activity on twitter networks: Validation of dunbar's number. PloS One, 6(8), e22656.