I just attended a PhD-course in informetrics in Umea, Sweden. It was a really good course, and I got excellent comments on my project.
Here is a pdf of my presentation: presentation-of-charles-phd-project-folksonomies-umea-2008
It is a very open ended short presentation, so that I could get a lot of feed back. IT worked ;-)
And here is a selection of all the comments. Thank you Tanja for the minute, I wouldn’t have managed to get all points without help.
Comments:
Jesper: It’s important that you look closer on yours research questions.
What’s your unit of analysis?
The dataset should answer the research question.
Maybe your study is to general.
You could split it up in 2 or 3 parts: tags, Information objects and functions
- Then you can get a better overview over the connection to the users.
Kim: “the anonymous professor” has had a blogpost of the visualization of very large datasets.
About connectivity… There is a collection in WoS [Web of Science] named network connectivity.
Raf: When you make a social network analysis, whats the link between two users?
You can “follow” users with similar interests.
Sara: Can you see inlinks or subscriptions as a object for connectivity (How users are connected through their subscriptions)
Tjeck bloglines, maybe you can follow users subscription here.
Kim: I’m not sure you can do that, these data are most often private.
Birger: Try to triangulate / cross check your study
Björn [Swedish]: If you have a group of users, maybe you can:
See how they connect through an information object
and
See how they defines the same information object
And lastly compare the two?
Kim: Can you not just study the users who employed notes in their posts?
You can then make a co-word analysis on that data…
Birger: Find focus! What do you want to test? Maybe your case should be defined differently, with a group of users in center.
Birger: How will you measure success?
Find criteria!
Jesper: See diffusion theory - how well does people connect through a 3. person
Richard Klavans: Adoption theory are better, a more precise speed of adoption
Sara: Has the dates you collected any meaning?
Can you reduce the data on the basis of the dates?
Birger: You can also cut a year back in the data, and then cut another year. Then you will have a benchmark.
Jesper: You need to define the central concepts in your study.
Connectivity!
Operationalize the concept.
How will you connect the users you investigate?
The way you connect them will reflect their connectivity.
THANK YOU ALL FOR THESE VALUABLE COMMENTS
