Keynote Speech: Bernie Hogan
Capturing Online Social Networks: Techniques, Insights and Challenges
Importance of network analysis. How to tell who’s the most connected, who’s connected to who and how
- Degree: number of links
- Betweenness: shortest paths
- PageRank: links to high degree
- Positions: blockmodelling
Networks can be made up of subgroups/subnetworks, even multiple spare networks somehow connected one to each other by a common node that just bridges them.
- For sampling large networks
- Often regression-based
- Visualizations of networks rarely show person
Network Analysis Tools
- Scraping: Python, Perl, PHP, Java (JUNG)
- Cleaning: same as before + databases
- Analyzing: UClnet, Pajek, JUNG, Egotistics
- Visualizing: NetDraw, Pajek, Guess
Social news: people will filter news for each other. Slashdot, reddit, Digg. Based on collaborative work/research and ways of voting the news/user.
Distribution of stories made popular in Digg: power curve, but really steep.
Limits of Social Networks
- What’s a friend?
- Entity resolution
Comments from the audience
This social networks are kind of a popularity contest
- Where’s the threshold that separates being in the network and being outside of it (in terms of traffic, contributions, etc.)? Ain’t we, in some way, acknowledging… trashholding and not real participation in the network?
- Hogan, B. (2007) Using Information Networks to Study Social Behavior. In IEEE Data Engineering Bulletin. 30(2). Pp.6-14
- Hogan, B. (forthcoming) Analyzing Social Networks Via the Internet. In Fielding, N., Lee, R. and Blank, G. Sage Handbook of Online Research Methods
- Egotistic, via Knowbieâ€™s Weblog, by Maria Chiara Pettenati
Towards a Social Science of Web 2.0 related posts (2007)
If you need to cite this article in a formal way (i.e. for bibliographical purposes) I dare suggest:
Peña-López, I. (2007) “Towards a Social Science of Web 2.0 (I): Bernie Hogan: Capturing Online Social Networks” In ICTlogy,
#48, September 2007. Barcelona: ICTlogy.
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