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:
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