As the focus for this week’s episode [airs live Tues 21/6/11 at 19.32 UK time, or catch it via the podcast] in the OU co-produced season of programmes on openness with Click (radio), the BBC World Service radio programme formerly known as Digital Planet, we’re looking at one or two notions of diversity…
If you’re a follower of pop technology, over the last week or two you will probably have already come across Eli Pariser’s new book, The Filter Bubble: What The Internet Is Hiding From You, or his TED Talk on the subject:
Eli Pariser, :The Filter Bubble”, TED Talks
It could be argued that this is the Filter Bubble in action… how likely is it, for example, that a randomly selected person on the street would have heard of this book?
To support the programme, presenter Gareth Mitchell has been running an informal experiment on the programmes Facebook page: Help us with our web personalisation experiment!! The idea? To see what effect changing personalisation settings on Google has on a Google search for the word “Platform”. (You can see results of the experiment from Click listeners around the world on the Facebook group wall… Maybe you’d like to contribute too?)
It might surprise you to learn that Google results pages – even for the same search word – do not necessarily always give the same results, something I’ve jokingly referred to previously as “the end of Google Ground Truth”, but is there maybe a benefit to having very specifically focussed web searches (that is, very specific filter bubbles)? I think in certain circumstances there may well be…
Take education, or research, for example. Sometimes, we want to get the right answer to a particular question. In times gone by, we might have asked a librarian for help, if not to such a particular book or reference source, at least to help us find one that might be appropriate for our needs. Nowadays, it’s often easier to turn to a web search engine than it is to find a librarian, but there are risks in doing that: after all, no-one really knows what secret sauce is used in the Google search ranking algorithm that determines which results get placed where in response to a particular search request. The results we get may be diverse in the sense that they are ranked in part by the behaviour of millions of other search engine users, but from that diversity do we just get – noise?
As part of the web personalisation/search experiment, we found that for many people, the effects of changing personalisation settings had no noticeable effect on the first page of results returned for a search on the word “platform”. But for some people, there were differences… From my own experience of making dozens of technology (and Formula One!) related searches a day, the results I get back for those topics hen I’m logged in to Google are very different to when I have disabled the personalised reslults. As far as my job goes, I have a supercharged version of Google that is tuned to return particular sorts of results – code snippets, results from sources I trust, and so on. In certain respects, the filter bubble is akin to my own personal librarian. In this particular case, the filter bubble (I believe), works to my benefit.
Indeed, I’ve even wondered before whether a “trained” Google account might actually be a valuable commodity: Could Librarians Be Influential Friends? And Who Owns Your Search Persona?. Being able to be an effective searcher requires several skills, including the phrasing of the search query itself, the ability to skim results and look for signals that suggest a result is reliable, and the ability to refine queries. (For a quick – and free – mini-course on how to improve your searching, check out the OU Library’s Safari course.) But I think it will increasingly rely on personalisation features…which means you need to have some idea about how the personalisation works in order to make the most of its benefits and mitigate the risks.
To take a silly example: if Google search results are in part influenced by the links you or your friends share on Twitter, and you follow hundreds of spam accounts, you might rightly expect your Google results to be filled with spam (because your friends have recommended them, and you trust your friends, right? That’s one of the key principles of why social search is deemed to be attractive.)
As well as the content we discover through search engines, content discovered through social networks is becoming of increasing importance. Something I’ve been looking at for some time is the structure of social networks on Twitter, in part as a “self-reflection” tool to help us see where we might be situated in a professional social sense based on the people we follow and who follow us. Of course, this can sometimes lead to incestuous behaviour, where the only people talking about a subject are people who know each other.
For example, when I looked at the connection of people chatting on twitter about Adam Curtis’ All Watched Over By Machines of Loving Grace documentary, I was surpised to see it defined a large part of the UK’s “technology scene” that I am familiar with from my own echochamber…
So what do I mean by echochamber? In the case of Twitter, I take it to refer to a group of people chatting around a topic (as for example, identified by a hashtag) who are tightly connected in a social sense because they all follow one another anyway… (To see an example of this, for a previous OU/Click episode, I posted a simple application (it’s still there), to show the extent to which people who had recently used the #bbcClickRadio hashtag on Twitter were connected.)
As far as diversity goes, if you follow people who only follow each other, then it might be that the only ideas you come across are ideas that keep getting recycled by the same few people… Or it might be the case that a highly connected group of people shows a well defined special interest group on a particular topic….
To get a feel for what we can learn about our own filter bubbles in Twitterspace, I had a quick look at Gareth Mitchell’s context (@garethm on Twitter). One of the dangers of using public apps is that anyone can do this sort of analysis of course, but the ethics around my using Gareth as a guinea pig in this example is maybe the topic of another programme…!
So, to start with, let’s see how tightly connected Gareth’s Twitter friends are (that is, to what extent do the people Gareth follows on Twitter follow each other?):
The social graph showing how @garethm’s friends follow each other
The nodes represent people Gareth follows, and they have been organised into coloured groups based on a social network analysis measure that tries to identify groups of tightly interconnected individuals. The nodes are sized according to a metric known as “Authority”, which reflects the extent to which people are followed by other members of the network.
A crude first glance at the graph suggests a technology (purple) and science (fluorine-y yellowy green) cluster to me, but Gareth might be able to label those groups differently.
Something else I’ve started to explore is the extent to which other people might see us on Twitter. One way of doing this is to look at who follows you; another is to have a peek at what lists you’ve been included on, along with who else is on those lists. Here’s a snapshot of some of the lists (that actually have subscribers!) that Gareth is listed on:
The flowers are separate lists. People who are on several lists are caught on the spiderweb threads connecting the list flowers… In a sense, the lists are filter bubbles defined by other people into which Gareth has been placed. To the left in the image above, we see there are a few lists that appear to share quite a few members: convergent filters?!
In order to try to looking outside these filter bubbles, we can get an overview of the people that Gareth’s friends follow that Gareth doesn’t follow (these are the people Gareth is likely to encounter via retweets from his friends):
Who @garethm’s friends follow that @garethm doesn’t follow…
My original inspiration for this was to see whether or not this group of people would make sense as recommendations for who to follow, but if we look at the most highly followed people, we see this may not actually make sense (unless you want to follow celebrities!;-)
Popular friends of Gareth’s that he doesn’t follow…
By way of a passing observation, it’s also worth noting that the approach I have taken to constructing the “my friends friends who aren’t my friends” graph tends to place “me” at the centre of the universe, surrounded by folk who are a just a a friend of a friend away…
For extended interviews and additional material relating to the OU/Click series on openness, make sure you visit Click (#bbcClickRadio) on OpenLearn.
4 thoughts on “Filter Bubbles, Google Ground Truth and Twitter EchoChambers”
Did we miss a trick at dev8d? Should we revive the filter bubble experiment or has that ship sailed?
@martin When we discussed the expt idea last week, I was in the middle of a w/s, considered the bookmarklet, and discounted it because it would have had to have been built, tested and instructions written in under 4 hours… But yes, I think it mat be a good idea; could certainly make mileage of the current hype ;-)
@tony at the time when @GarethM put it out on the Facebook page I thought it made sense to do it that way as while bookmarklets are clever I still find installation isn’t as straightforward as it could be. How far did you get with the scrapping code
PS apologies if I screw up your comment structure (not sure if this will reply to your reply)
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