ACM Home Page
Please provide us with feedback. Feedback
Topological analysis of an online social network for older adults
Full text PdfPdf (366 KB)
Source
Conference on Information and Knowledge Management archive
Proceeding of the 2008 ACM workshop on Search in social media table of contents
Napa Valley, California, USA
SESSION: Social network analysis table of contents
Pages 51-58  
Year of Publication: 2008
ISBN:978-1-60558-258-0
Authors
Marcella Wilson  University of Maryland Baltimore County, Baltimore, MD, USA
Charles Nicholas  University of Maryland Baltimore County, Baltimore, MD, USA
Sponsors
ACM: Association for Computing Machinery
SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
SIGIR: ACM Special Interest Group on Information Retrieval
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 48,   Downloads (12 Months): 373,   Citation Count: 0
Additional Information:

abstract   references   index terms   collaborative colleagues  

Tools and Actions: Request Permissions Request Permissions    Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/1458583.1458596
What is a DOI?

ABSTRACT

Social network systems on the Internet, such MySpace and LinkedIn, are growing in popularity around the world. The level of such activity is now comparable to that associated with email and blogs. Our research addresses the question of whether people in different demographic groups use these systems in the same way. Older Americans tend to use email the same way as Americans in general. The usage of blogs, however, is different, with significant differences in the topological and structural patterns of post and response in blogs being evident in different demographics. We discover important information about the topological structures of online social networks by examining topological patterns in blog posts, also known as cascades. To accomplish this, we create and study the blogosphere, blog and post networks of an online social network used primarily by older adults. We also study the topological patterns of cascades in greater detail, reporting their common shapes, properties and size distribution. Our research has implications for the design of social network software for older Americans, as well as the algorithms used in search engines for such systems.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

1
2
 
3
Holme, P., Edling, C. and Liljeros, F. Structure and Time Evolution of an Internet Dating Community. 2004, Social Networks, Vol. 26, pp. 155--174.
4
 
5
Lenhart, A. and Fox, S. Bloggers: A portrait of the Internet's new storytellers. Pew Internet and American Life Project. Washington, D.C. : Pew Internet and American Life Project, 2006. p. 25.
 
6
Gruhl, D., Guha, R., Liben-Nowell, D., & Tomkins, A. Information Diffusion Through Blogspace. New York : s.n., 2004. WWW 2004.
 
7
Adamic, L. A. and Adar, E. How to search a social network. 3, July 2005, Social Networks, Vol. 27, pp. 187--203.
 
8
Leskovec, J., McGlohon, M., Faloutsos, C., Glance, N., & Hurst, M. Patterns of Cascading Behavior in Large Blog Graphs. Minneapolis: s.n., 2007. Proceedings of the Seventh SIAM International Conference on Data Mining.
 
9
Fox, S. Older Americans and the Internet. Pew Internet and American Life Project. Washington, D.C. : Pew Internet and American Life Project, 2004. p. 16.
 
10
Albert, R., Jeong, H., & Barabasi, A.-L. Diameter of the World Wide Web. September 1999, Nature, Vol. 401, pp. 130--131.
 
11
Newman, M. E. J. Models of the small world. 3-4, November 2000, Journal of Statistical Physics, Vol. 1, pp. 819--841.
 
12
Newman, M. E. J. The structure and function of complex networks. 2, 2003, SIAM Review, Vol. 45, pp. 165--381.
13
 
14
Mislove, A., Gummadi, K., & Druschel, P. Exploiting social networks for Internet search. Irving, CA. : s.n., 2006. Proceedings of the 5th Workshop on Hot Topics in Networks (HotNets-V).
 
15
 
16
Day, J. C., Janus, A. and Davis, J. Computer and Internet Use in the United States. Washington, D.C. : U.S. Census Bureau, 2003.
 
17
Resident Internet Gateway. Erickson Retirement Communities. {Online} 2003. {Cited: February 20, 2008.} http://www.touchtown.us/erickson.
18
19
 
20
Leskovec, J., Singh, S. and Kleinberg, J. Patterns of Influence ina Recommendation Network. Singapore : s.n., 2006. Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD).
 
21
Borgatti, S. P., Everett, M. G. and Freeman, L. C. Ucinet for Windows: Software for Social Network Analysis. s.l., Harvard, MA : Analytic Technologies.
 
22
Wasserman, S, and Faust, K. Social Network Analysis: Methods and Applciations. s.l. : Cambridge University Press, 1994.
 
23
Erdos, P., & Renyi, A. On random graphs. s.l. : Pulbicationes Mathematicae, 1959, Vol. 6, pp. 290--297.
 
24
Wilson, M. The Comparison of Online Social Networks in Terms of Structure and Evolution. Baltimore, Maryland : s.n., 2008.
 
25
SAS Institute Inc. Cary, NC : s.n.
 
26
Lipsman, A. More than Half of MySpace Visitors are Now Age 35 or Older, as Site's Demographic Composition Continues to Shift. ComScore: Measuring the Digital World. {Online} October 5, 2006. {Cited: February 21, 2008.} http://www.comscore.com/press/release.asp?press=1019
 
27
Yahoo! MyWeb. {Online} 2008. {Cited: March 28, 2008.} http://myweb2.search.yahoo.com.
 
28
Google Co-op. {Online} 2008. {Cited: July 14, 2008.} http://www.google.com.
 
29

Collaborative Colleagues:
Marcella Wilson: colleagues
Charles Nicholas: colleagues