Linking cyber and physical spaces through community detection and clustering in social media feeds

被引:46
作者
Croitoru, Arie [1 ]
Wayant, N. [2 ]
Crooks, A. [3 ]
Radzikowski, J. [1 ]
Stefanidis, A. [1 ]
机构
[1] George Mason Univ, Dept Geog & Geoinformat Sci, Ctr Geospatial Intelligence, Fairfax, VA 22030 USA
[2] US Army Geospatial Res Lab, Alexandria, VA 22315 USA
[3] George Mason Univ, Dept Computat Social Sci, Fairfax, VA 22030 USA
关键词
Social media; Spatiotemporal clustering; Social network analysis; Community detection; Geospatial analysis; SPATIAL PRESENCE; TWITTER; NETWORKS; SENSE;
D O I
10.1016/j.compenvurbsys.2014.11.002
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Over the last decade we have witnessed a significant growth in the use of social media. Interactions within their context lead to the establishment of groups that function at the intersection of the physical and cyber spaces, and as such represent hybrid communities. Gaining a better understanding of how information flows in these hybrid communities is a substantial scientific challenge with significant implications on our ability to better harness crowd-contributed content. This paper addresses this challenge by studying how information propagates and evolves over time at the intersection of the physical and cyber spaces. By analyzing the spatial footprint, social network structure, and content in both physical and cyber spaces we advance our understanding of the information propagation mechanisms in social media. The utility of this approach is demonstrated in two real-world case studies, the first reflecting a planned event (the Occupy Wall Street - OWS - movement's Day of Action in November 2011), and the second reflecting an unexpected disaster (the Boston Marathon bombing in April 2013). Our findings highlight the intricate nature of the propagation and evolution of information both within and across cyber and physical spaces, as well as the role of hybrid networks in the exchange of information between these spaces. (C) 2014 Elsevier Ltd. All rights reserved.
引用
收藏
页码:47 / 64
页数:18
相关论文
共 97 条
[1]  
Aggarwal CC, 2003, P 2003 VLDB C, V29, P81, DOI DOI 10.1016/B978-012722442-8/50016-1
[2]  
Aggarwal CC, 2014, CH CRC DATA MIN KNOW, P231
[3]   On Density-Based Data Streams Clustering Algorithms: A Survey [J].
Amini, Amineh ;
Teh, Ying Wah ;
Saboohi, Hadi .
JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY, 2014, 29 (01) :116-141
[4]  
[Anonymous], HARPERS MAGAZINE
[5]  
[Anonymous], 2012, Forbes
[6]  
[Anonymous], P 19 ACM INT C INF K
[7]  
[Anonymous], 1996, Proceedings of the 1996 ACM Conference on Computer Supported Cooperative Work, DOI [DOI 10.1145/240080.240193, 10.1145/240080.240193]
[8]  
[Anonymous], POLICING SOC INT J R
[9]  
[Anonymous], CBS NEWS
[10]  
[Anonymous], 2011, Why Americans use social media