Much of the early stages of the blog, and continuing (though less so) throughout the course, I found were spend engaging with the limits of the technologies I wanted to use. Spam, IFTTT, issues with tags and categories, the design of the blog, the issues of trying to embed Storify, were all challenges that were publicly wrestled with in the lifestream. I also sometimes had issues with synching and cloud storage–I blogged from my laptop at home, but often took notes or photographs on my phone, tablet or other computer, and glitches in synching could mean that trying to upload the images or notes to the blog was delayed.

This wrestling surprised me, because I have been using most of these technologies in my leisure time, or for work, for some time. I think the pressure to blog nearly every day while only having a day or two to focus on my studies meant I was always in a rush, so any lag or glitch could throw the whole thing off.  After semester started in my day job, too, I found it difficult to spend evenings and weekends on the course, since I often had to be at work. I had expected to be able to vary my working days to counteract that, but that was not possible for structural reasons, and that was a challenge.

In the first few weeks, there was too much Twitter and not enough of anything else. I often discuss learning, teaching and technology on that ‘academic Twitter’, so the #mscedc hashtag was a natural extension of those conversations, and fitted comfortably with the people I already follow and the people who already follow me. During the course, I gained nearly 100 followers overall, suggesting that I didn’t alienate most of my audience, and that they appreciated the contributions I was making to their streams.

On the other hand, my Instagram community is small, it’s made up of people I personally know, and they share pictures of their lunch, of their holidays, of their homes. It’s a leisured and personal space, so sharing technology news or reflections on the digital would be less appropriate. I eventually worked out that I could use it to document my own embodied working practices. I could photograph my experiences blogging with a bowl of pie, or reading in a café. These would contribute to my community but also to the lifestream.

I was often unsure about the value of the lifestream as a public blog. The learning journal blog focuses on a very small audience, primarily myself, my tutor, and sometimes the other members of the course. The format of the lifestream is difficult for an outsider to navigate or read.

Instead, I longed to craft things from what we’ve learned in the course to share with the world. I enjoyed live-blogging the readings, and continue to find that one of the most useful ways to engage with the course, the medium and the content. I wonder if this might be a valuable way of thinking about learning in public in the future.

It’s autumn here. Warm during the day, cooler in the evenings. We are just beginning our academic year. We have not yet ‘fallen back’ out of daylight savings time. Scotland is a long way away.

To be ‘with’ the class synchronously, I need to get up very early, or stay up late. But my job requires me to get up early, and stay up late at this time of year.

On Thursday, instead of being at the Hangout, I was trying to complete the tutorial timetable, interview and place new tutors, and do spot checks to check all the classes were running smoothly.

On Friday, instead of being at the Hangout, I was teaching at ArtsWrite, an intensive doctoral writing weekend at the University of Melbourne. (This is a picture of the stamps to reward attendees for meeting their goals).

I wonder if my embodied reality here on the other side of the world is too different, too far away, too out of synch with Edinburgh. I wonder if digital learning can overcome those distances and disjuncts. I wonder if it should.

This year, my job has little to do with online learning. This year, the questions I find raised by the readings make me more critical of the university, rather than more hopeful. This year, the sun is shining, and I am inside on my computer. I wonder if I’ve got that wrong.

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

***

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.

"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.

***

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.

***

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.

***

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’. 

Every semester, there are three weeks that are absolute hell: O-week to the end of week 2. Week 3 is busy, but less stressful. Every year, I make sure I see my friends before O-week starts, and then tell them I’ll see them again in a month.

This blog has had the same problem. It’s not that I haven’t been doing anything, but it’s been out of synch, late, or patchy.

I’ve updated the things I was supposed to have done, or had started but not published, in week 8 only this weekend, as we turn the corner into week 9. I managed a couple of tweets in the tweetorial (and neither of them had space for the hashtag, #fail).

I did have a fascinating conversation with Nicholas and Jeremy earlier:

https://storify.com/katrinafee/foucault-and-algorithms

I had trouble finding an algorithim I could play with, so after a few abortive attempts, I finally worked out I could look at Twitter’s Discovery timeline. I commented on Jin’s and PJ’s bogs.

Posting less often means that some of my posts have been longer. I’ve been continuing to document the places where I study (and increasingly, the food I eat as I study), including my first video:

Anyway, week 3 is coming up. Busy, but no longer crazy. Hopefully that means I’ll have a chance to catch up.

I selected the the top 10 tweets in my Twitter Discovery timeline in both Mobile and Desktop versions, at 1.30pm Sunday 15 March. (They are listed below, under Appendix). I carried out a content analysis of the tweets to see why Twitter might serve them to me.

***

Content analysis: coding

Analysis.

All the tweets included some visual component: a screet shot, picture or gif. Most of the accounts are large, established accounts belonging to publishing platforms or authors.

I did a quick content analysis, where I coded the text, image, embedded content, or information from the link (but not following the link) in each tweet into:

• Geek culture
• Gender or cultural Diversity
• Privacy/online security
• Technology
• Hard News
• Science
• Pop culture
• History
• Writing
• Visual attraction (pictures that accompanied the tweets were pretty, rather than funny or screen texts)
• Food
• Academia
• Humour
• Inspiration

(These categories are in order of identification, not anything more significant).

Tweets were coded with as many of the above catagories as might be relevant. Saladin Ahmed is a fantasy writer and geek/pop culture critic, he writes about racial, gender and religious diversity, and he often posts humourous tweets. He appears at the top of both lists, once more on desktop and twice more on my mobile list. His top tweet registered in 7 of the above catagories (the highest of any tweet).

Twitter thinks this is my ideal Tweet.

There were 5 tweets coded Humour on mobile and 4 on desktop. 4 tweets coded Geek culture (Desktop: 3) and Diversity (Desktop: 2) on my mobile timeline. 3 tweets coded Privacy/online security (Desktop: 2) and History (Desktop: 1). Overall, mobile Discovery tweets registered 34 categories, where as desktop tweets only fitted 28.

Therefore, categories that met my interests were more likely to be served to me in the Discovery timeline of the mobile app than the desktop web version. 

However, this left a large gap–what was influencing the desktop version to rate these particular tweets highly?

I had included the information Twitter shares in grey above a tweet to tell me why it’s in my timeline in the descriptions (See appendix). One clear difference stood out so I added another emergent category:

• High Retweet count

Of the tweets in the desktop Discovery timeline, 5 had a high retweet count (66-197). Of the tweets in the mobile app, there was only 1. This accounted for most of the gap between desktop and mobile.

In the absence of individual engagement data (suggested by what I favourite, RT, click on, reply to etc), Twitter uses general popularity as a category to decide what is going to appear on my Discovery timeline.

And yes, I did make pie for Pi day.

I made pie for 3.1415 day.

***

Appendix 1: The top 10 tweets

1. Saladin Ahmed (who I follow, RTd by another person I follow) on the Telegraph pointing out that now, gasp, white men are being targetted for online abuse too. [Desktop & mobile]

2. Slate (who I follow, RTd by 147 other people) on Pi day 3.1415.  [D]

New York Times (who I follow, RTd by William Gibson) on how the ‘tech titans’ of Silicon Valley who created platforms that require and harvest personal information are protecting their own privacy. [M]

3. Bibliophilia (who I follow, RTd by 66 other people) on an 18th century double bible. [D]

Newsweek (followed by someone I follow) on whether the Antropocene started with the Native American genocides [M]

4. The Economist (who I follow, RTd by 197 other people) on the increased risk of nuclear war.

Classicpics (RTd by someone I follow) on the ‘perfect body’ in 1955 (the woman is a healthy weight, with muscles and curves). [M]

5. New York Times (who I follow, RTd by William Gibson) on how the ‘tech titans’ of Silicon Valley who created platforms that require and harvest personal information are protecting their own privacy. [D]

Slate (who I follow, RTd by 147 other people) on Pi day 3.1415.  [M]

6. Saladin Ahmed (Rtd by 2 people I follow) with a Star Trek reaction gif about Gizmodo, diversity. [D]

Science headline of the week about snail sex (RTd by someone I follow). [M]

7. Science headline of the week about snail sex (RTd by someone I follow). [D]

Saladin Ahmed (Rtd by 2 people I follow) with a Star Trek reaction gif about Gizmodo, diversity. [M]

8. Wall Street Journal (who I follow, RTd by 166 other people) on the return of the business phone call. [D]

Endgadget (followed by someone I follow) on the fact that new HBO still ‘has strings attached’. [M]

9. Advice to Writers (who I follow, RTd by 82 other people) with a quote from Maya Angelou. [D]

Person RTd by someone I follow on 11th century limestone head discovered in Norfolk.[M]

10. An academic I follow (favourited by another academic I follow), joking television marathons should count towards tenure. [D]

Saladin Ahmed, with a reaction gif from Lord of the Rings about having too much work to do. [M]

I only read Facebook via Most Recent stories (though that isn’t possible on mobile, and even then various things are promoted for me that aren’t a pure timestream); I usually use Twitter and never the Discovery timeline because I like the crowdsourced flow. I spent years trying to get Amazon’s Recommended for You to actually recommend me books I liked–‘all books by this author’, ‘no books by this author’, ‘I really don’t ever want to read erotica about shapeshifting bears’, ‘I will sometimes read about werewolves though’. I’d think this was easy, but Recommended for You has gone from being sometimes useful to almost useless.

So I had to go back to the drawing board.

http://goanimate.com/videos/0eaiWBtuMuSw

For more of the research behind this video, see MOOCMOOC webs in the wild and Social Network Analysis.

I know exactly where it fell apart, and why. Orientation Week in a residential college is brutal. My arguments about time off in lieu, or varied working hours, or flex-time were not successful. I wandered around for two weeks in a miasma of insomnia and unhappiness. A friend died suddenly and her funeral was on last Sunday. It was my birthday. I am now exactly twice as old as our new students.

I did collect things and tweet (thank God for Twitter when you can’t sleep at 3am), so I have retrospectively inserted them into my blog as a reconstruction of the week.

I collated my tweets via Storify. I collected mini-scrapbooks of images, via screenshots, drawings on Paper, and photographs. These images considered nostalgia; alogorthims, furturism, bodies; and bots, spam.

I submitted an essay for Research Methods, and it was late. I finally got around to looking at the Research Methods page for Social Network Analysis, only to find that I’d re-invented the wheel in my blog on MOOCMOOC in the wild, it’s called ‘ethology’ and has been widely used (as I reflect on here).

On the one hand I wonder if I’ve bitten off more than I can chew doing two subjects, but I’m learning so much by bouncing between them, and finding so many parallels in what I’m doing in both. That’s why so many of my blog posts on the Research Methods blog are starting to talk about what I’m doing in Digital Culture and vice versa.

Usually I spend Thursday and Sunday focusing on my studies, but this week work ramped up, so I feel really behind.  This post is late,  I couldn’t make either of the Hangouts, and the Twitter roundup didn’t happen this week.

What did happen

This week was intellectually quite heavy, and there were two readings that I live-blogged as I try to get my head around ethnography. There was then a long reflection blog where I discussed my struggles around the whole genre of a mini-ethnography. Finally, there was a post where I explored what a more quantitative analysis might give us, using one of the Twitter analytics tools and some very brief network analysis.

On the Lifestream side, I looked at the MOOCMOOC Scoop.it page, and their live hangout. I also played with a new collage tool on my iPad–thinking about nostalgia, the digital, manual, ethnographies (native/indigenous terminologies).

Looking forward

Being so busy, I thought a couple of times this week that I’d like to get IFTTT back up and running. I need to rethink how I’ll use it, so that it does what I’d like it to, but I’m hoping to have some of it back in my stream in the next week.

I might also do a Storify of my Twitter over the last few days.

O-Week starts next week, so I’m going to have to think about balancing my studies when the students are back.