Beware of Personalised Search! Information Dystopia and Online ‘Filter Bubbles’

Last year Eli Pariser spoke at TED, covering a topic that we’re all becoming increasingly affected by while navigating the web. Eli introduced his theory on the online ‘filter bubble’, explaining that through the continuous quest to ‘personalise’ search results, our online environments are becoming less and less diverse.

Hi everyone, I am Ned and this is my first (proper) post on State of Search. I’ve become fairly annoyed lately with many of the websites I visit regularly and spend a lot of time on, like: Amazon, Facebook and Google (after all, I am an SEO), that are becoming incredibly ‘samey’ places to hang out and discover information online. So much of the content that I see every day is either something I’ve already seen or, worse, nothing to do with what I was looking for in the first place! This is one of the effects that Eli refers to when talking about online ‘filter bubbles’.

Filter bubble [Definition] – “The unique universe of information that you live in online”

The filter bubble is an invisible force and one that I feel, it is also a counter-intuitive force that goes against the very powerful nature of the Internet that has, up until now, enabled the borderless and free ability to create and share vast amounts of information. Instead, this invisible force filters the information at your disposal, ultimately limiting not what exists on the Internet as a whole, but what you have the ability to find on ‘your Internet’. Such that it creates a form of information dystopia.

[youtube=http://www.youtube.com/watch?v=B8ofWFx525s&w=600&h=340]

Information Filtering in practice

Facebook

One area where you may be most familiar with this happening is Facebook. Facebook’s ‘EdgeRank’ is an algorithm that determines what people see when they log in to Facebook. In layman’s terms, this algorithm determines what content to show you based on:

  • Who the individual is that shared the content;
  • How you are connected to that individual;
  • and the certain types of content that you interact with most (liking, sharing etc).
  • Essentially it assigns values based on 3 variables: Affinity, weight, and time decay.

Facebook Edgerank Algorithm

I won’t dwell on this too much in this post but Kelvin Newman covered the topic very thoroughly in his post on algorithm marketing.

Mark Zuckerberg Quote on Relevancy

The problem here is that Facebook is removing results from you, without your consent.

Amazon

Amazon were also one of the first adopters to personalising results based on users profiles, dedicating a large part of their homepage to product based on:

  • Recently viewed products
  • Recent purchases
  • Other similar users buying/shopping habits
Amazon Personalised Recommendations
My personalised homepage on Amazon.co.uk

This is an intelligent approach to personalisation and many believe (myself included) that this is one of the reasons for Amazon’s phenomenal success. However, it is not without it’s flaws, the amount of information I am potentially missing out on is vast. We must challenge this personalised approach and question whether or not it is really providing to best experience for the user.

Learn a little more about how Amazon’s recommendation system works by reading the top answer here on Quora or looking at the top answer over on StackOverflow.

Google

A large amount of Google’s recent algorithmic changes have been focused on creating far more personalised results for the user; this trend only looks to continue. As SEOs and online marketers, we must understand how Google will look to do this. In the talk, Eli says that a Google engineer confirmed there are 57 signals that Google considers to help serve you results specific to your query, (even when you’re logged out!). Anything from:

  • The browser that is being used
  • Where you are accessing the information
  • What computer/device is being used

Google Signals for Personalised Results

This means that the results shown for any two people searching individually for the same keyword/keyword term can be very different. Even if these people are searching for the exact same term, at the exact same time, in exactly the same city, their results will be influenced by a large variety of factors and information that Google know’s about you. This is a trend that’s been happening for a while and we’re going to see considerably more of in the future; there is no ‘standard’ Google anymore, search has become personalised.

Why we should all beware of the ‘filter bubble’

The likes of Facebook, Amazon, Google and Netflix aren’t the only sites that are doing this, in fact a large amount of sites have began personalising their results. However, despite their brilliance, technologies such as Amazon and Netflix’s vastly intelligent recommendation systems need to give us (as the recipient for this information) some control, we need to be able decide what is right and wrong for us. We don’t want to live in a ‘web of one’, but rather a ‘web of many’.

“The Internet is showing us what it thinks we want to see but not necessarily what we need to see”

The problem with Internet sites tailoring their results to our personal likes and dislikes based on algorithms, is there is a dangerous unintended consequence of us all getting trapped inside our own ‘filter bubbles’.

The Filter Bubble

The problem about this personalisation of search, as Eli points out is that “You don’t decide what gets in [to your filter bubble], and more importantly you also don’t actually see what gets edited out.”

The Effect of the Filter Bubble

Over-Personalisation: The Robots Need a Human Touch

Roger Mozbot

Recommendation engines are a staple for the Internet. Using algorithms, websites can accumulate vast amounts of user-specific data and tailor their offerings to your likes, what you’ve spent time looking at, what your friends like etc. But this can be a flawed. Eli discusses the shift from the classical editor type figure of the print revolution; these were largely removed when the Internet developed into mainstream use, allowing a raft of bloggers and amateurs being able to publish their content without the need of editorial approval. This process has caused a trend to replace these humans with robots. The content we are served when logging onto some sites like: Mashable, the Huffington Post, Yahoo News and Facebook has been selected by algorithms. But algorithms should not be curators of the almost infinite amount of information on the Internet. Robots don’t do ethics. They need a human touch.

Humans need to consume information that we don’t agree with to understand different points of view; we need to be challenged; and we need to be made uncomfortable. If we don’t have transparency or control over the filters that are restricting us from seeing this information, we remain trapped inside our ‘filter bubbles’, blind to the kaleidoscope of people; different ideas; new ideals and perspectives on the Internet, in a ‘web of many’. The increased adoption of filter bubbles means that, instead of being social and integrated we’re actually heading backwards, toward a closed off, limited point of view. We must watch this change carefully, to quote Darwin:

“It is not the strongest or the most intelligent who will survive but those who can best manage change.” [Charles Darwin]

Eli has written a book on The Filter Bubble if you’re keen on reading more, but I’d love to hear your thoughts on personalisation of search results?

  • Do you think personalised results are a good or bad thing?
  • What sites have you noticed personalise results particularly well? Which do you think don’t personalise results well?
  • Do you think personalised results have started to limit the information you’re seeing on the web?
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About Ned Poulter

Ned Poulter is CEO & Founder of Pole Star Digital, a Digital Marketing Consultancy he runs alongside a number of entrepreneurial ventures. He spends his time developing and overseeing digital strategy for a number of well reputed international clients, developing creative approaches to achieving organic search results, increasing inbound traffic, establishing and managing paid search campaigns and ultimately driving revenue.