"Did QuillConnect tell you anything you didn't know?" @katrinafee I felt it tried to algorithmically 'normalise' my social media use #mscedc

— Sian Bayne (@sbayne) March 6, 2015

Reading PJ’s blog, however, inspired me to go back and try again.

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To start, some numbers:

You have been a Twitter user for four years and you tweet more than most of your followers. You post 66 tweets a week while your followers average 7 per week.  Further, you have 916 followers listening to you … You are in the 96th percentile of Twitter users measured by followers.

During MScEDC I’ve been tweeting more than normal–this week I tweeted 86 times, last week I tweeted over 150 times. The numbers are correct, as far as I can measure them. I was surprised that someone with less than 1000 followers was so influential, but it would explain why some accounts with just over 1000 followers were rated influential enough to appear alongside major platforms like The New York Times or The Economist on my Discovery timeline.

I have manually created my own ‘youloop’–most of the people I follow and those who follow me are interested in the same topics as me: “Your important topics match those most tweeted about by followers who are similar to you.” “The hashtags most often used by your followers similar to you have been #mscedc, #phdchat, and #talkhe.” This is unsurprising!

Second, issues of catagorisation.  Some of my catagories are much more specific than QuillConnect’s (History, Writing, Academia probably all count as ‘Education’). I made no distinction between sources of interest, where QuillConnect lists ‘Entertainment, Arts, Music, Television, Celebrity’. On the other hand, I make a distinction between ‘Geek culture’ and ‘Popular culture’ and ‘Writing’. QuillConnect does not list ‘Food’ as a catagory, which is surprising (though perhaps less so on Twitter than Instagram or Facebook).

Third, issues of sentiment.  QuillConnect is interested in ‘positive’, ‘negative’ or neutral’ language. I listed content analysis of genre. However, it would not be hard to suggest that Diversity, Hard News, Online Security are likely to skew negative, where as Humour, Inspiration, Visually attractive are likely to skew positive.

QuillConnect analysis of my tweets

I therefore recoded my tweets to try to ‘reverse engineer’ my Discovery timeline analysis. I then compared them to the QuillAnalysis report.

I mostly tweet about politics (8%) according to QuillConnect. From my own content analysis, Twitter reads this as more likely to mean ‘diversity politics’ than ‘hard news politics’.

I regularly tweet about Education, and education tweets are most likely to be served to me in my Discovery timeline (both mobile and desktop).

Twitter thinks I’m more interested in Technology than Science, while QuillConnect thinks it’s the other way around. (I tend to agree with Twitter).

I tweet less than most people about Entertainment, Television, Music and Celebrity. As I don’t own a television and films give me migraines, this is not surprising. Discovery tends to agree–my tweets that did reference television tended to do so obliquely. A gif referencing Star Trek was really about technology, a tweet mentioning television marathons was really a joke about tenure (education), a tweet about HBO was more about structures of technology companies than the content of any shows.

I tweet about ‘Arts’ according to QuillConnect.  Discovery suggests that this means, sculpture, historical artifacts, photography, literature.

My Twitter is regarded as ‘neutral’ by QuillConnect, and “for #mscedc, tweets containing the hashtag are predominantly neutral in tone.” When I re-coded my Discovery timeline, I counted 6 positive and 7 negative tweets on mobile (some of the tweets, for example, were humourous tweets about a negative situation, thus they were counted twice). This suggests a balance . However, only 3 tweets in the Desktop timeline were truly neutral (neither positive nor negative) and none of the mobile tweets.

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So QuillConnect wasn’t wrong, but it was reductive. It was limited by lack of detail, which a human user or Twitter’s own algorithm seems able to deliver on. The difference between ‘balanced both positive and negative neutral’ and ‘not expressing positive or negative neutral’, for example, is significant.

QuillConnect is unable to analyse sophisticated or contextual content. It suggests: “For example, the most retweeted of your followers have utilized #westconnex in their recent tweets.” This is because I follow a number of accounts campaigning against the East-West Link (an unpopular, expensive proposed toll road through Melbourne), and WestConnex is a similar road in Sydney. Some of the people I follow (who follow me back) are active campaigners against both roads. Me jumping on that bandwagon to increase my reach would be strange and inappropriate.

Twitter’s Discovery timeline alogorithm, on the other hand, assumes I am more interested in historical artifacts, more likely to click on an article about social justice, or online security, than on a campaign going on in my backyard. They are probably right.

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So, algorithmically narrative science is still ‘uncanny‘. It is creepy.

The QuillConnect algorithm is still too far off, and is therefore producing negative emotions.

Uncanny Valley, via Wikimedia. See http://www.androidscience.com/theuncannyvalley/proceedings2005/uncannyvalley.html

The ‘uncanny valley’ suggests that a ‘bunraku puppet’ is clearly unhuman but would still garner a ‘positive’ familiarity response.

Discovery on mobile is also somewhat creepy–these would not have been the tweets I picked out for myself as being the ones I’d like most to see. But the Discovery timeline (when it has enough data) is starting to climb out of the uncanny valley.

It is not yet human, I still probably won’t use it, but it didn’t annoy me much and it’s analysis of my interests was ‘close enough’. 

When we were watching the films, I was most struck by the consistently melancholy tone, each of them reflected on some gulf or gap between actual life (remembered or experienced) and the virtual memories or experiences they were able to attain. Each of the films had, at it’s core, some kind of loss or lack.

Having woken up a bit more, I thought about the ‘wrongness’ in each of the films, the awkward or slightly ‘off’, as portrayed by the false-memory Sophie in Memory 2.0, or the almost inhuman replicant Roy as acted by Rutger Hauer in Bladerunner. This was prompted by a comment made by one of the other students about Address is Approximate, about the ‘uncanny valley‘. (I wanted to go back and attribute it correctly, but the Temporary Room in TogetherTube doesn’t keep the discussion, which I’ll have to remember for next time).

But as I was making dinner that night, my academic training kicked in, and I started thinking about money.

I couldn’t shake the line from Memory 2.0 where Steve, the lab tech, races after Henry to find out what happened.  ‘I’ve got my uncle screaming at me, claims they’re asking for their money back‘ he says. (All film clips are designed to start at the relevant line… you shouldn’t have to watch the whole clip for each quote).

• Who is paying for Henry’s memories of Sophie? Why is this worth something for them?

It’s clear that ‘they’ pay the lab enough for the lab to pay Henry, as we see earlier.

This made me think again about the desk in Address is Approximate. 

• How does the fact that this is a workspace make it different from toys-coming-to-life stories like The Velveeteen Rabit or Toy Story?

And of course this is the backbone of Bladerunner. Replicants are made by a corportation, and destroyed by the state.

• What financial or market forces led to the creation of really believable androids? Who benefits (cui bono) from their destruction?

Therefore, is the crucified Roy, with a nail in his hand, a Christ-figure dying to redeem the world; or a follower of Spartacus, the slave revolutionary?

And thinking about slavery took me back to Caliban in Address is Approximate.

The cute little white mono-brow robot gets to live out his dream, a version of the American dream, by riding his mini-fig convertible along the Pacific Coast.

Screenshot from Address is Approximate: http://vimeo.com/32397612

Screenshot from Address is Approximate http://vimeo.com/32397612

But who is driving the engine of the machine?

A small, monsterous- or native-looking black robot Caliban with pointy teeth.

The fact that I can’t work out if the robot is supposed to be a monster, a monkey, an indigenous person, or an Afro-Carribean person basically says it all about the representational Othering going on here.

Screenshot from ‘Address is Approximate’ by The Theory: http://vimeo.com/32397612

(For more on this, see Edward Said.)

The comments beneath the video, and the responses of the class (and my own) were overwhelmingly positive: ‘cute’, ’emotional’, ‘heartwarming’. Yet, clearly, all of this white affective labour is being made possible by the invisible others in the engine room.

Historical technology, knowledge and machinery are often coded as ‘transparent, self-invisible’, and implicitly white and male, Haraway reminds us (p.29). A focus on the virtual mind enables physical difference to be erased, making ‘most men and all women… simply invisible’ (p.29). Making difference invisible, she suggests, does not liberate those whose difference is written on their bodies, but rather continues to force those differences away from the public sphere, even as the different continue to sustain the public sphere from the background. (Embodying Learners in New Media Literacies: Cyborgs, Androids and the Dance Apocalyptic I)

From Star, Susan Leigh. “Power, technology and the phenomenology of conventions: on being allergic to onions.” The Sociological Review 38.S1 (1990): 26-56.

As a recent post from danah boyd demonstrated, the tech industry ‘whitewashes’ what teens do on social media, for example “critiques of youth use of Twitter are often seen in a negative light because of the heavy use by low-status black and brown youth”. The significance of ‘Black Twitter‘ to Twitter is not to be overlooked.

There are responses to this problem that take us further, that give us ideas of how to not just notice the problem, but perhaps deal with it. Ta Nehsi Coates in “The Case for Reparations”, notes that (especially if, as Picketty has argued, inherited capital is a major force for income inequality) a long history of dispossessing black people of the fruits of their labour, means they continue to be disadvantaged.

Another route is Janelle Monáe’s Electric Ladies cyborgs. For a more academic survey of how Monáe embodies the theoretical positions of Philip K Dick, Octavia Butler, and Donna Haraway, see The Dance Apocalyptic II. But for now, watch how she combines product placement, black middle-class life (with cupcakes, sororities, majorettes and her own convertible), and the life of an escaped revolutionary cyborg.