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Exploring Algorithms

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Algorithms play an increasing role in our online lives. In their neverending quest to accurately profile their users in order to maximise ad revenues IT companies employ more and more sophisticated data mining methods incorporating information from  your activity history, your friends, your location and even strangers with similar interests to you.

Ever since I’ve watched Gary Kovac’s shocking TED talk “Tracking the Trackers” I’ve become increasingly privacy conscious and have taken several precautions to not be as easily tracked. From activating the ‘Do not track’ option in my browser to installing privacy enhancing browser extension such as Disconnect to opting out of targeted ads within the Google settings.

Thanks to these steps my advertisement profile with Google is now rather unspecific. Without taking such measures however, Google has been able to profile people surprisingly well.

I actively try to avoid the personalised features that sites present me with. In Facebook I never use their EdgeRank algorithm that sorts my feed according to “Top Stories” – I use “Most Recent” instead – simply because most of the Top Stories unsurprisingly is paid content from pages I subscribed to, not posts from my friends. Another reason is that I prefer to keep an open mind and personalised filters tend to create a filter bubble which not only distort people’s view of the outside world according to their own preferences and beliefs, they also do so invisibly.

For this week’s exercise of exploring algorithms I have decided to take a look at YouTube, since I have a long standing history of using that site. My main interaction with the site is wih the “My Subscriptions” tab which is always more relevant (and recent) than the algorithmically populated “What to watch” feature.

Logged into my account, this is what my front page looks like

what_to_watch

I can immediately tell why YouTube is recommending these videos to me. All of these videos are closely related to videos I have watched on YouTube within the last 48 hours. I watched one “CinemaSins” video, one Pink Floyd song, one Kygo song, a fail video and a Strokes song. While the songs that I played were actively sought out the other videos showed up on my subscription feed which made me click them.

If I scroll further down, it seems that YouTube still takes the same 3 or 4 videos from earlier and shows more related videos. Additionally, it suggests videos by la belle musique, a channel I am already subscribed to. what_to_watch2

While the suggested videos generally meet my taste, they don’t necessarily entice me to watch them now, especially since they don’t lead me to interesting new channels which I might want to subscribe to.

If I log out and visit YouTube in an incognito mode I am greeted with the following suggestions.

what_to_watch3

None of these videos have any relevance to my search or watched videos history but looking at the channel names (Ad Council, RadioKRONEHIT) one can assume that these videos have been placed on the front page because someone had paid for it.

Let’s take a look at the comments section of YouTube which has long been famous for its disastrous reputation. Apparently Google sorts the comments according to your Google+ profile which I, however, never use. This shows in the comparison between the two comments pages of a random video I clicked on.

Logged in:
comments1

Logged out:
comments2

As we can see, probably due to the lack of usable profile data Google has from my Google+ account, the comments shown are the same both when logged in and logged out. Again, the combative nature of YouTube comments shines through once more. It seems that no matter how sophisticated the algorithms in place, unless YouTube actively censors comments, it will always fight an uphill battle against the culture that has developed within the YouTube comments universe.

In conclusion, suggestion algorithms like the one used by YouTube can somewhat enhance your user experience to a certain extent, provided that you are okay with sharing enough data about yourself. Given the problems associated with filter bubbles and privacy concerns however, at present I still prefer a carefully manually selected subscriptions list to algorithmically derived suggestions.

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Comment on Songwriting MOOC – An Ethnographic Song

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Thank you everyone for your feedback, I really appreciate it.

I thought I would share some additional comments about the course and the community that I observed.

Early on in the course you learn that song writing is all about making choices – what idea do you want to convey and how do you do it? Ideally you have one central idea and everything else supports that central idea while the buildup of the song adds more heft to the central idea – you go from the general to the specific. Since I couldn’t pack nearly as much information as I wanted into the song without breaking its structure and flow I will add some details here that I found out about the MOOC’s community.

The MOOC uses Coursera’s internal discussion forum system and no attempt is being done by the course organisers to move it to other social networks. The forums itself, however, are highly organised. There are four main forums (General Discussions, Study Groups, Video Lectures and Assignments) and each of these main forums itself has 6 subforums, one for every week of the course. Including the “Signature Track” forum this adds to 29 forums in total. As a result each forum is frequented only rarely as people move from one subforum to the next as the weeks progress.

As mentioned in my song, the songs we have to analyse as part of our quizzes are available on YouTube (independently uploaded from the course) and it’s interesting to see that in the video comments it’s almost guaranteed that there is at least one mention of “Coursera” to the tune of “Hi from Coursera” or “Coursera brought me here”. These shout outs to fellow course members show a sense of identification with the community within the course. Futhermore, while Coursera is mentioned, I did not see the answers to the questions related to the songs spoiled in the YouTube comments which shows relatively high maturity (for YouTube standards) and a willingness to learn (and letting others learn). The students are mostly novice songwriters with little to no experience in writing songs but many play at least some type of instrument. People were in general very helpful and honest with each other on the forums.

Analysing the discussion forums on Coursera and other sources like MOOC review sites reveals a major issue that generates a lot of sentiments and reactions: The peer review grading process is under heavy criticism. 40% of the total grade is received from completing multiple choice quizzes while 60% come from peers. Since songwriting is an artform after all people don’t take criticism of their work all too lightly and many showed signs of frustration, especially since feedback is given anonymously and in case a lower grade has been awarded it often lacks proper reasoning. As a result the most discussed forum threads, aside from the one asking students to introduce themselves, are either about expressing discontent with the peer review in general or people asking for higher quality peer feedback that more specifically addresses the issues we are focusing on in our weekly assignments. Overall it looks as though many people are open to sharing their work in the forums and inviting constructive feedback.

Writing the song was much more challenging than I thought but it was definitely a fun experience. We are now getting into the sections where we are learning how to compose melodies to go with our lyrics and I am looking forward to finding out what software tools are recommended to assist with that.

I hope this gives a little better insight into the MOOC’s community than the song ever could and I’ll be happy to answer any further questions!

from Comments for Mihael’s EDC blog http://edc15.education.ed.ac.uk/mkiseloski/2015/02/27/songwriting-mooc-an-ethnographic-song/#comment-421
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Recap: Week 7

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Block 2 has come to an end and I am currently admiring the amazing ethnographies my colleagues have put together over the last few weeks.

While further delving into my songwriting MOOC and internet communities culture I found a very interesting article in The Telegraph about the different “laws” that govern internet culture. In addition to classics like Poe’s Law, Godwin’s Law or Rule 34 the article describes some highly amusing ones like Danth’s Law or The Law of Exclamations. A blatant omission (which was thankfully added by a commenter) is a favourite of mine – Cunningham’s Law: The best way to get the right answer on the Internet is not to ask the question, but rather to post the wrong answer.

Staying on the topic of artificial intelligence which has been following us throughout the course and has recently been present in the media I stumbled upon this speculation about what will happen when the Internet of Things becomes artificially intelligent. It is a fascinating thought to think that  instead of machines having their own individual intellectual capacities (like humans do) the connectedness of all things digital will create one global artificial consciousness. The internet as we know it might very well be the brain of this operation, the underlying infrastructure. It just takes a decade or two more until its consciousness switches on.

Talking about artificial intelligence, DeepMind, a machine learning company owned by Google has anounced this week that its AI has learned to play 49 Atari games from scratch through trial and error. In 29 cases it performed even better than human players. At this pace of development this technology is likely to disrupt society faster than we will be able to react to the changes.

Finally, I posted my micro-ethnography about my songwriting MOOC, fittingly in the form of a song.

Next week we will start a new block on algorithmic cultures. Considering the buzz that machine learning has generated lately I’m sure it will be highly topical and I’m very much looking forward to it.