The web is full of algorithms, often underpinning the social media services, search engines and retailers that we visit online. However, their presence tends to be invisible. Algorithms are meant to give us the exact information we are looking for, make our social interactions better, suggest music or videos we might like, or correct our spelling, but we are not really supposed to see them or how they operate. Indeed, we often only acknowledge the presence of algorithms when strange results are produced.
Commercial interests often ensure that algorithms remain unseen, and the complex mathematical formulas usually involved mean that even if we could see them our ability to understand how they work is limited. Nevertheless, interest in algorithms is growing, with Netflix publicising some basic information about its recommendation algorithm, and Apple revealing more technical details about the iTunes Genius service. Even without technical information, what we can always see are the results an algorithm produces, and it is these results that we’d like you to document and consider.
Week 8 task: playing with algorithms
This week your task is to play with some algorithms and document the results. This task is not formally assessed, and is primarily chance to have some fun exploring the kinds of algorithms we’ll be reading about in this block. However, you should record your results in a way that feeds into your lifestream blog, so that your explorations will be included in the lifestream assessment.
A great place to start your algorithmic play is Christian Sandvig’s blog post Show and Tell: Algorithmic Culture, featuring three super examples of algorithms shaping our attention: Google Instant, Facebook News Feed, and Google Ads. Try out these examples for yourself, before choosing somewhere to start your own algorithmic play.
Below are some suggestions, although you are free to choose any space for this task – there is likely to be some kind of algorithmic operation behind most of the web content you encounter. The important thing is to find an example that your personal interaction can affect, usually requiring you to be a logged in user with an account.
- Google Search and Ads, or another search engine
Google’s PageRank is one of the most famous web algorithms, primarily for the way it goes about ‘optimising’ search engine results. You might use Sandvig’s example above to document your own play with Google Instant, or you could explore the difference in search results depending on whether you are logged in to your Google account (if you have one) when you search. You might also explore how your search terms affect the adverts presented to you within Google, but also within other sites that you subsequently visit.
Following Sandvig’s example above, Facebook’s news feed presents an interesting site for your algorithmic play. Can you ‘reverse engineer’ the EdgeRank algorithm, and speculate about why certain posts are being hidden from view? What other algorithms might be operating underneath the Facebook hood?
- YouTube comments and recommended videos
Following some of the discussion in the Knox (2014) reading this week (see below), you might explore YouTube recommended videos, which appear as a list on the right of the YouTube page. As above, logging into your Google account may give different results. If you have a Google+ account, you might also investigate any alterations to the YouTube video comments based on your social network.
- Spotify, Netflix, or similar music/video streaming services.
Exploring the algorithms within these services will probably work best if you already have accounts and some history of use. What kind of entertainment is offered to you in the recommended lists? What happens if you select something you wouldn’t normally choose?
- Or, you might try automatically generating your ‘Twitter story’ using a service called QuillConnect by Narrative Science. Is this an accurate story of your Twitter activity? What is the advantage of a written ‘story’ over a visualisation?
- What other algorithms are recommending content to you or optimising your communications?
As well as documenting the results of your algorithmic play, we also want you to get critical. Rather than simply assuming that algorithms automatically give us the best answers, we want you to interrogate and question the result you see. Think about the following questions when you comment on your algorithmic play:
- How has the algorithm affected the options you are given or what you can see?
- How have your actions changed what the algorithm has done?
- How have other people been involved in shaping results?
- Do results feel personal or limiting? Is this optimisation, or a ‘you loop’?
- What are the ethical issues at stake with your chosen algorithm? Is there data here that should be private?
- What are the implications for digital education implied by your chosen algorithm?
As with your micro-ethnography task in the previous block, we encourage you to think creatively about how you might present your algorithmic play. Would a blog post, slideshow, or timeline work best? Remember to choose a format that will feed into your lifestream blog.
Week 8 readings
While our task this week is focussed more on the social web, the following readings will provide important educational contexts for the burgeoning algorithmic culture.
Knox, J. K. (2014). Active algorithms: sociomaterial spaces in the E-learning and Digital Cultures MOOC. Campus Virtuales, 3(1): 42-55.
Discussing the E-learning and Digital Cultures MOOC in some detail, this paper analyses the algorithms at work in recommending YouTube videos, suggesting ways that the spatial qualities of the course are shaped by the technology. Interesting here are the claims of ‘non-human’ agency attributed to algorithms.
Eynon, R. (2013) The rise of Big Data: what does it mean for education, technology, and media research? Learning, Media and Technology. 237-240.
This editorial provides an important review of the implications of Big Data for education. The critique of the promises of ‘technical fixes’ will have relevance to our explorations of broader algorithmic culture this week.
Gillespie, T. 2012. The Relevance of Algorithms. forthcoming, in Media Technologies, ed. Tarleton Gillespie, Pablo Boczkowski, and Kirsten Foot. Cambridge, MA: MIT Press.
While the core readings are focussed on educational concerns, Gillespie’s essay provides a useful consideration of algorithms in public culture more generally. Particularly interesting here will be the claims made about how algorithms take part in censoring content.