Name
Abstract | ||
|---|---|---|
This paper compares the social properties of Twitter users' networks with the spatial proximity of the networks. Using a comprehensive analysis of network density and network transitivity we found that the density of networks and the spatial clustering depends on the size of the network; smaller networks are more socially clustered and extend a smaller physical distance and larger networks are physically more dispersed with less social clustering. Additionally, Twitter networks are more effective at transmitting information at the local level. For example, local triadic connections are more than twice as likely to be transitive than those extending more than 500 km. This implies that not only is distance important to the communities developed in online social networks, but scale is extremely pertinent to the nature of these networks. Even as technologies such as Twitter enable a larger volume of interaction between spaces, these interactions do not invent completely new social and spatial patterns, but instead replicate existing arrangements. (C) 2014 Elsevier Ltd. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.compenvurbsys.2014.07.002 | Computers, Environment and Urban Systems |
Keywords | Field | DocType |
Social network analysis,Twitter,Geospatial analysis | Dynamic network analysis,Data mining,Social network,Social network analysis,Evolving networks,Hierarchical network model,Complex network,Cluster analysis,Geography,Transitive relation | Journal |
Volume | ISSN | Citations |
53 | 0198-9715 | 9 |
PageRank | References | Authors |
0.69 | 4 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Monica Stephens | 1 | 54 | 2.70 |
| Ate Poorthuis | 2 | 10 | 2.45 |