The more we contribute, communicate share and talk online the more we leave a trail of personal data in our tracks. This may be data about what we say to whom on Twitter, when we are most active or the photos we take. Or it may be data that we have captured from a specific activity – data on every run I have done in the last two years is stored by Runkeeper, for example.
The quantity and depth of data that we are structuring about our lives even on one network comes as a surprise to many people. Taking Facebook as an example – the data we create about ourselves and our networks is vast, and often hidden from the consumer – you just can’t imagine what it might be. The first step to help you understand the amount of data you have stored and how it might be useful is to visualise it – and search engine Wolfram Alpha have now produced a report that takes this information and presents it back to you.
For any user what you uncover about yourself, what Facebook knows about you, is interesting. For example, the word I have used most frequently on Facebook is ‘run’ (perhaps reflecting the training I have spent two years doing and how I use social to motivate my training). The peak time for me to upload photos is apparently 9pm on a Saturday. And the most common first name and surname among my friends is ‘James’.
But what is more interesting to start to explore is how this Facebook data is able to understand data better than we might be able to. Take how it clusters my friends. Just looking at connections (and their connections) you can start to map out how my friends group themselves and really start to understand something about me.
You can see three clear groups:
- A tight cluster of yellow connections – people who are all interconnected and clearly all know each other. These are people I’ve been friends with since University.
- A relatively tight cluster of blue connections – less interconnected but the groups of people I’ve made friends with in 10 years in London.
- A more spread our cluster of green connections – a loosely connected set of people that I have worked with.
There are also the odd random connection that I have seemed to pick up along the way.
So Facebook can accurately and clearly summarise my friendships and how they interact. And you could probably make inferences from that about how likely I am to mix people across these groups – only a small number of people connect between the clusters, suggesting I am more likely to socialise in these groups separately (which to be honest I am).
There a lot of data out there, data that we are leaving in our wake with every social interaction. Currently this data is being used by the platforms and by brands, but the exciting opportunity is to see how individuals can take more ownership of their own data and get more value from it. The first step is to start to understand what data there is out there and how it is structured. The Wolfram Alpha Facebook reports make an important first step to revealing this.
Other posts you might like to read:
- Formula One team social media rankings
- Developing a great social media channel strategy
- Who are the most engaging world leaders on Twitter?
- The social media landscape in 2012 – infographic
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