Name
Abstract | ||
|---|---|---|
Information spreading in online social communities has attracted tremendous attention due to its utmost practical values in applications. Despite that several individual-level diffusion data have been investigated, we still lack the detailed understanding of the spreading pattern of information. Here, by comparing information flows and social links in a blog community, we find that the diffusion processes are induced by three different spreading mechanisms: social spreading, self-promotion and broadcast. Although numerous previous studies have employed epidemic spreading models to simulate information diffusion, we observe that such models fail to reproduce the realistic diffusion pattern. In respect to users behaviors, strikingly, we find that most users would stick to one specific diffusion mechanism. Moreover, our observations indicate that the social spreading is not only crucial for the structure of diffusion trees, but also capable of inducing more subsequent individuals to acquire the information. Our findings suggest new directions for modeling of information diffusion in social systems, and could inform design of efficient propagation strategies based on users behaviors. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1371/journal.pone.0126894 | PLOS ONE |
Keywords | Field | DocType |
biomedical research,bioinformatics | Data science,Broadcasting,Graph,Social network,Computer science,Probability distribution,Social system,Bioinformatics,The Internet | Journal |
Volume | Issue | ISSN |
10 | 5 | 1932-6203 |
Citations | PageRank | References |
6 | 0.50 | 20 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pei Sen | 1 | 144 | 9.23 |
| Lev Muchnik | 2 | 118 | 7.40 |
| Shaoting Tang | 3 | 6 | 0.50 |
| Zhiming Zheng | 4 | 128 | 16.80 |
| Hernán A. Makse | 5 | 343 | 17.95 |