Tweetorial TAGS analysis

So happy that Clare got this working. Let’s have a look at what’s happened then:

First of all the numbers haven’t changed, Phillip Downey still has the most tweets and was linked to the most conversations. This is a quantifiable fact so it is not surprising that it shows up in both of the analytics systems.

Another question that reappears when I look at the TAGS sheet is how useful can analytics be if we don’t understand the reasoning behind the calculations. I can see Ben Williams in the centre of the mess fairly near me, Dirk and Nigel, but what does this proximity signify? Did we really mention him or tweet him that often? In reality I imagine he has a lot more to do with Jeremy Knox on account of them writing e-books together and using each others’ lectures in courses, but the data doesn’t show that.

One thing that did surprise me was that amongst the mess of nodes I could see both Coldplay and Ed Sheeran. I instantly equated this with physical presence in a conversation, “when the hell were we talking with Coldplay? What on earth about?”. This reflects my instinctive, embodied conception of Twitter rather than a data led view. By clicking on the node I actually found out they were just mentioned once by Dirk in passing but because he hashtagged them it showed up in the data. I’m still a little unsure why anyone would bother to hashtag things like that. Or in fact hashtag anything at all. Why do people choose to reveal themselves to algorithms? I mean it’s not just a case of writing in shorthand. You could abbreviate things without hashtagging them. Maybe they imagine they’ll search through the data at a later date, but does anyone ever do that?

I’m such a Twitter noob.

Anyway I do appreciate the bizarre modernist poetry of the courses top hashtags. Another way to abstract algorithms perhaps:

Ed Sheeran Dwarf fortress!

Quads, quads, quads!

Duckduckgogoduckduck

lastel! Cyborg emmental!

Rollerderby Time Machine!

Stilton Analytics! Brie! Poss! Poss!

 

 

 

 

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