Algorithms and the Filter Bubble, Take 3

[Update 5 April 2015: There is a more recent version of this lecture, presented in September 2014, but it has not been posted yet.]

Title card for "Algorithms and the Filter Bubble"On Monday 7 April, I delivered an updated version of my guest lecture to media students at the University of Technology Sydney (UTS), “Algorithms and the Filter Bubble”. And here it is.

What’s it about? It’s about what we now call — this year at least — “big data” and how that’s changing how the media works, just like it’s changing every other part of society.

I cruise through what all this data is, where it’s coming from, who’s collecting it and where it’s going; what advertisers and media companies and others can do with this data; and some speculation about how this might unfold in the future.

There’s links to all the references over the fold, and you can follow along with the slides (PDF). The recording picks up immediately after I was introduced by the course coordinator, Dr Belinda Middleweek. A transcript may or may not follow at some point in the future.

Some people mentioned that last time it was difficult to follow some of the slides, as the PDF file didn’t show how the builds happened, so I may add a video slideshow version at some point too.

The audience was primarily first and second year students at the beginning of their media studies degrees.

[If a transcript ever becomes available, this is where it will appear.]

What was left out at the end

I didn’t keep a close enough eye on the time, which is most unprofessional of me, so I had to drop a couple of things at the end of the lecture. So what did we miss?

My planned closing was to speculate a little more about the implications of all this technology — essentially the material covered in references 26 through 30 below.

When advertisers and newsmakers know all about you, including where you are and what you’re interested in, and when robots become so good that they’re able to tailor news and advertising precisely for your interests and current state of mind — what does that mean for political persuasion, and other kinds of persuasion?

Watch the videos of the robots from the US Naval Research Laboratory responding to everyday human speech. Consider Apple founder Steve Job’s comment that the iTunes Store gives you “freedom from pornography”. Consider than in a world of filter bubbles, some news outlets with a political agenda might want to give you “freedom from confusing thoughts”. After all, Apple has already blocked from their App Store an app that provided information on US military drone strikes.

Just where might this go? As I told the media students at the start of the lecture, they are the ones who will be creating this future for themselves and their descendants, not those of us in the second half of our lives.

Licensing and Re-Use

This work is made available under a Creative Commons BY-NC-SA license. This presentation may be re-used for non-commercial purposes within the terms of the Creative Commons license. The non-commercial and share-alike conditions are required to adhere to the licensing of the imagery used. Please contact me if you require an alternative version. As a minimum, attribution should read: “Source: Stilgherrian.” Online versions must link the word Stilgherrian to the website at


  1. A presentation I did in 2012, Consilium: Social media is destroying society? Good!, which sets out my evolving thoughts on the internet revolution changing power relationships at every level of society.
  2. Bitdefender’s Clueful app for uncovering the privacy risks of the apps running in your smartphone. Available for Android and Apple’s iOS.
  3. TrustGo’s Ad Detector app for Android, which reveals which advertising networks your smartphone’s apps are sending data to.
  4. The Collusion plugin for the Firefox web browser, which maps the relationships between the third-party tracking code you encounter in your web browsing.
  5. Cookies, the fundamental building-block of web tracking technology.
  6. Recent research by Finnish information security company F-Secure which shows that More than half of the top 100 URLs are tracking you.
  7. Some of my recent articles about data mining and re-identification for supposedly anonymous data.
  8. The ProPublica article, Everything We Know About What Data Brokers Know About You, from March 2013.
  9. How Companies Learn Your Secrets, by New York Times journalist Charles Duhigg, 16 February 2012. This includes the example of Target being able to determine when a woman has become pregnant from her shopping list.
  10. Gaydar: Facebook friendships expose sexual orientation, by Carter Jernigan and Behram F T Mistree, First Monday, 5 October 2009.
  11. How well does music predict your politics? by Brian Whitman, 11 July 2012.
  12. Facebook’s Gen Y Nightmare, the Monday Note essay by Frédéric Filloux which included the speculative scenario if a woman being turned down for a job because data mining predicted some likely health problems and perhaps even a good chance of her becoming pregnant.
  13. Official information about Australia’s new privacy laws, which came into force on 12 March 2014.
  14. Earthquake: 3.2 quake strikes near San Simeon, a computer-generated story from the LA Times, 1 February 2013.
  15. Stats Monkey, the 2009 project in robot-written sports news stories from the Intelligent Information Laboratory at Northwestern University. That project page contains links to media coverage of the project.
  16. Narrative Science’s ‘Robot Journalists’ Now Tackling Real Estate, Media Bistro, 12 September 2011.
  17. Narrative Science, one of the leaders in computer-generated news.
  18. Automated Insights, another such leader.
  19. My Crikey article Twitter mapping and how we choose our own adventure from 23 May 2012, linking to the work of the ARC Centre of Excellence for Creative Industries and Innovation at the Queensland University of Technology.
  20. Mapping Online Publics, the ongoing blog of that project.
  21. The Quartz article, Turns out Twitter is even more politically polarized than you thought, which is based on…
  22. A study by the Pew Research Center’s Internet & American Life Project, Mapping Twitter Topic Networks: From Polarized Crowds to Community Clusters.
  23. Robots: their rights and legal status, an episode of ABC Radio National’s Future Tense from 11 August 2013.
  24. The Only Thing Weirder Than a Telemarketing Robot, wrote Alexis Madrigal of The Atlantic on 13 December 2013, is the possibility that there is a human in the call centre listening to you, yes, but he or she isn’t speaking to you, but rather playing back pre-recorded scripted soundbites.
  25. Research from 2009 by British-American information security company Sophos, Sophos Australia Facebook ID probe 2009, which showed that — at least at that time — almost half of Facebook users would accept a random friend request from a fake profile with almost no hard information.
  26. My article Prepare for the attack of the politiclones! on fake Twitter followers and Australian politics, 15 August 2013.
  27. Videos from the US Naval Research Laboratory’s Cognitive Robots project. Of particular interest is Robotic Secrets Revealed, Episode 002 (Hiatt, Harrison, Lawson, Martinson, & Trafton, 2011).
  28. Steve Jobs Offers World ‘Freedom From Porn’, Gawker, 15 May 2010.
  29. Apple Rejects App That Tracks US Drone Strikes, Wired, 30 August 2012.
  30. My article The Facebook experiment, 23 March 2012.