Johanna Blakley

Media | Entertainment | Fashion

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Deflating the Filter Bubble

filterbubble

I was asked recently to speak at a symposium on Media Choices at Drexel University. The event drew a fascinating array of scholars who were studying things like Internet addiction, online dating, and political polarization in media consumption.

When someone mentions “media choice” to me, I automatically start thinking about the algorithms that have been developed to help shape that choice.

I have followed avidly the growing use of recommendation systems that you see on sites like Amazon, Netflix, YouTube and Pandora. I saw these mechanisms as a significant move away from demographic marketing (which I find deeply flawed) to marketing based on customer taste.

I did have my reservations though. I was very moved by Eli Pariser’s TED talk about the danger of “filter bubbles,” which effectively insulate us from opinions and content that we don’t understand or like. His talk really resonated with me because of the deeply divided ideological and taste communities that I found in a major survey research project I conducted on the correlation between entertainment preferences and political ideology (spoiler: they are even more deeply connected than you might think.)

But, when I conducted further research about collaborative filtering systems, I made some rather counter-intuitive discoveries. YouTube, for instance, found that “suggesting the videos most closely related to the one a person is already watching actually drives them away.”

Of course YouTube’s goal is to get you to sit and watch YouTube like you watch TV: to lean back and watch a half hour to an hour of programming, rather than watching for two minutes, getting frustrated trying to find something else worth watching and then going elsewhere. So, in short, it’s in YouTube’s best interest to introduce some calculated serendipity into their recommendations. Read the rest of this entry »

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