1. David Haynes City, University of London @jdavidhaynes
Discourse about Knowledge Organization on Twitter – a narrative on current themes
David Haynes
City, University of London
@jdavidhaynes
I am not a great Tweeter – only last week did I pass the 1000 Tweet mark
However it occurs to me that many researchers use Twitter:
To follow other researchers
To identify research centres
To share conference experiences
To find out what’s trending

2. Background Lots of work on bibliometric Plenty on web analytics
Some work on social media analytics for research
ISKO luminaries have pioneered domain analysis

3. Research Questions
What is the nature of the discourse about Knowledge Organization on Twitter?
Are there distinct sub-domains within KO?
Can Tweets be used as a means of mapping a domain?
I shall begin to explore the first of these questions. The second requires a longer-term view of Tweets and a more retrospective view of Tweets
The last of these questions is a longer-term ambition. Comparison between social media analytics and bibliometrics

4. First Cluster Analysis
This is a first representation of the data I gathered
Let’s unpack this a little
Each circle or square or triangle represents a different Twitter user
In a social media network this is called a vertex
The grey line connecting two users is known as an edge
Some of these grey line are arrows –suggesting that there is a direction. If someone quotes someone else or re-tweets or likes someone else’s tweet a new edge is created.
So how did I get to this point?

5. NodeXL is a tool for social media analysis – works as an add-in to Microsoft Excel
Import Tweets via an API
Search on the following terms

6. Term Relevance Knowledge organization 86% Knowledge organisation 70%
Indexing
50%
Ontology
32%
Taxonomy
22%
Classification
10%
ISKO 2%
Sample searches analyzed to assess relevance of Tweets to KO. Surprisingly only 2% of searches on ISKO are relevant. It is also a popular name in Indonesia – I think there is a celebrity with that name, and it seems to be a word in Turkish and a transliteration of a word in Arabic. Clearly Knowledge Organization did not stand a chance.
Ended up with 9670 edges (connections) to analyze
A total of 8257 vertices (people or organizations with a Twitter handle)
Twitter is a social medium – it’s about relationship and connections

7. That’s how we ended up with this
We asked NodeXL to automatically cluster together vertices that formed natural groups
How connected are they to one-another?
Not necessarily dealing with the same subject – but they may be – that is part of the test

8. Went through a number of transformations and ended up with this
Went through a number of transformations and ended up with this. People in groups of 1 were excluded from this
You can see that there are some obvious grouping that are worth exploring
Vertex opacity 10%-100%
Ignore outliers

9. The most cohesive group seemed to be focused on Bloom’s Taxonomy
The most cohesive group seemed to be focused on Bloom’s Taxonomy. Although it is a taxonomy – the nature of the discourse was about Teaching theory and the Taxonomy of need. I was more interested in the study of classification rather than a specific taxonomy

10. This is what happens when we removed that group
Not a great deal of difference
EigenVector Centrality<0.001 to remove most references to Bloom’s Taxonomy

11. We then decided to focus only on the larger groups identified by the software
This is all those groups with more than 50 edges defined
Hiding all groups with fewer than 50 unique edges

12. Clumping of large groups
Multiple iterations (about 50) of the Fructerman-Rheingold algorithm
We can now navigate interactively around this landscape to see what the main clusters are and whether is any discernable theme associated with each cluster

13. Emergent themes