Twitter Graph Analysis Results for #mw2009

Just a quick post about another conference's Twitter backchannel I analyzed recently. Take a look at my posts on #swineflu and #09ntc to get a full picture of what I'm up to here. Basically, I'm looking at the network formed by replies and retweets in Twitter inside of a particular hashtag. Here, I'll go over the results of Museums and the Web 2009, a.k.a. #mw2009.

From roughly the start of the conference through the end, I found 2039 tweets. The top authority was Nina Simon who writes for the influential Museum 2.0 blog--no surprise that she would be the tweeter who was replied to by the highest-ranking hubs. Speaking of hubs, the highest score for those went to Shelley Mannion. Mannion writes a blog that focuses on tech in museums. Since a hub, as I understand it, has a lot of the properties of a journalist, it makes sense that a tweeter like Mannion, who makes extensive use of the "@" character in her many posts, would become a high-scoring hub.

Interestingly, there don't seem to be as many crossovers, at least in the top ten, between the authorities and hubs. This would indicate that the people doing the replying were not necessarily also the people being replied to. Some of that is borne out by looking at the indegree and outdegree centralities, which are simpler measures that more or less count inbound and outbound links. There are some matches in those latter lists, though, such as kpfefferle and 5easypieces, both of whom show up as hubs, too. Why aren't they authorities? Perhaps because the people linking back to them weren't high scoring hubs (such as smannion)--or because they didn't get as many links from those hubs as other tweeters did. Dunno.

The subgraph of the largest strongly connected component is pretty big, especially given the relatively smaller number of tweets (compared to, say, GDC). Still not sure if this indicates, as I mentioned in the #09ntc post, that the linkages and community are stronger in that channel's conference than at events like GDC, where there might be more strangers or more atomized groups. Hard to say, really. Hit me up on Twitter or leave a comment if you have a better grasp on this kind of analysis than I do.

Anyway, the raw data:

Betweenness centrality

publichistorian 0.015
ninaksimon 0.016
frankieroberto 0.018
bwyman 0.019
mw2009 0.019
anarchivist 0.022
5easypieces 0.027
sebchan 0.030
kpfefferle 0.037
smannion 0.038

Closeness centrality in largest SCC

bwyman 0.412
sebchan 0.415
frankieroberto 0.415
musebrarian 0.415
publichistorian 0.421
kpfefferle 0.427
anarchivist 0.433
nancyproctor 0.436
smannion 0.455
5easypieces 0.488

Indegree centrality

5easypieces 0.032
frankieroberto 0.035
brooklynmuseum 0.035
anarchivist 0.035
kpfefferle 0.037
bwyman 0.037
sebchan 0.045
mw2009 0.045
rjstein 0.048
ninaksimon 0.074

Outdegree centrality

mia_out 0.032
musebrarian 0.035
sebchan 0.037
bwyman 0.040
nancyproctor 0.048
5easypieces 0.048
publichistorian 0.053
anarchivist 0.056
kpfefferle 0.061
smannion 0.096


cmoad 0.014
rjstein 0.014
bwyman 0.015
frankieroberto 0.018
mw2009 0.019
sebchan 0.021
kpfefferle 0.022
smannion 0.023
5easypieces 0.024
ninaksimon 0.028


museologiste 0.02
musebrarian 0.02
brooklynmuseum 0.03
bwyman 0.03
anarchivist 0.03
nancyproctor 0.04
5easypieces 0.04
kpfefferle 0.04
sebchan 0.08
smannion 0.27


tehm 0.02
zbartrout 0.02
bwyman 0.02
rlooseley 0.03
psamis 0.03
georginab 0.04
publichistorian 0.05
rjstein 0.06
frankieroberto 0.11
ninaksimon 0.14


HT (heard through)
OH (overheard)
RT (retweets)

Copyright Mike Edwards 2006-2009. All content available under the Creative Commons Attribution ShareAlike license, unless otherwise noted.