One of the things I found fascinating about the tweet archive is how there was no differentiation between ‘tweeting as normal’ and ‘tweeting to the tweetorial’.
In my reflection on week 9, I wrote:
I managed a couple of tweets in the tweetorial (and neither of them had space for the hashtag, #fail).
The tweet archive, however, suggested that across that week, had contributed 15 tweets, and was the 8th most active user.
Active users 3 March-15 March 2015
Now, the top engagers tweeted four times as much as me, and my tweets would not have contributed to the tweet spike around the tweetorial…
Tweets over time, showing a spike for the tweetorial
I was also fascinated to see that most people were using the Twitter desktop client, and then TweetDeck. As I’ve been exploring recently, almost all my Twitter use is via the mobile app. It is possible that almost half of all iPhone tweets, therefore, were mine.
What these visualisations do not enable us to do, though, is judge value, only quantity. Moreover, there is no differentiation between the joking conversations about the spam (deer antlers, Michael Kors handbags) [sociality value], the sharing of resources [research value], the discussion around content [analysis value].
It’s a pretty blunt instrument. I loved Claire’s reponse:
This is what it feels like after looking at Tweet Archivist and Keyhole. #mscedc pic.twitter.com/vVzwgwU4Oq
— Clare Hampton (@clarehampton) March 19, 2015
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Updated 20 March:
The Keyhole analytics are worse. I don’t have geolocation for Twitter enabled, so I have literally been wiped off the map (zero engagement from Australia!).
The gender engagement seemed off too, so I went and did some analysis of my own. My analysis is that 43% of tweets were from female participants, and that 36% of the participants are female. I’m not sure how Keyhole identifies gender (I tried to search for how Keyhole counts gender, but I couldn’t work it out. Twitter does not require gender for registration). But the algorithm is clearly miscounting, and therefore dangerously misrepresents our engagement. To go from over 1/3 to 1/10? That’s ridiculous.
Algorithmic counting can be reductive, but it is clearly also erasing certain identites, without being explicit about how it makes it’s judgements. Even assuming that algorithms are rough approximations can be extraordinarily problematic, therefore.