Last week, Stephen Wolfram released a long and interesting analysis of aggregated and anonymized Facebook user data from his Data Donor program. He offers some observations about how Facebook behaviors illustrate the trajectories of people’s lives — how many people they friend, where they settle, and how clusters of friends reflect communities (school, friend, neighborhood).
In September I tried Wolfram Alpha to examine my Facebook use, and not much about the broad strokes observations changed when I re-ran it recently. I still use words more than pictures, and have roughly the same number of male and female friends. Geography is still fairly widely dispersed. This time, I took a closer look at the network graph.
The colors indicate a typology defined in the web app. In brief:
- Social insiders (purple) share the most connections with you. These include many colleagues in interactive, and my son.
- Social outsiders (grey) share at most one friend with you. These include people I’ve worked with briefly during consulting gigs, or met traveling somewhere far away on vacation. I see far more of these than I would have predicted.
- Social connectors (green) connect groups otherwise disconnected. In my network, this includes a friend who I went to elementary school with who also worked with me at the same software company in our twenties.
- Social neighbors (orange) have few friends you don’t already know. In my graph, this includes late adopters of social networks, and skews older.
- Social gateways (red) have a great many friends who you don’t know. If I were being more strategic about growing my social network, this is where I would focus, thinking that the strength of weak ties would provide more opportunities for connection that could be helpful for everything from a great restaurant in Montreal to job candidate referral.
You can graph your own life and social network courtesy of Stephen Wolfram right here.
MOMA has a terrific visualization as part of a show on Inventing Abstraction that opened back in December 2012. Visualization projects that map interconnections become complex quickly in a number of ways:
- Content for each subject: How much should you display? This seems like the right amount, although there’s something hilarious about seeing Picasso’s interests reduced to an all-caps summary: GUITARS, MODELS, CUBISM, SUMMERS IN CATALONIA
- Content that informs the connections: What’s the data source for these? Who relates to whom? How closely? How do you display relative strength of relationships, if at all?
- Overall user experience: How will users know what to do? Where to start? Is the story that is emerging the one you started out telling?
- Movement: What’s too sensitive? What’s not sensitive enough?
- Technology: How can this work everywhere you need it to? This is mostly a solved technical problem, but not trivial in a world of proliferating devices. Will this ever be projected? What’s the level of accessibility required?
- Flexibility: Depending on the life of your product, how do you handle new data about relationships? What’s the governance process for change post launch?
Information aesthetics also points to a great three-minute movie made about the mapping process which gets to the complexity under the hood here.
Reviews of the show overall can be found in The New Yorker and The New York Times, but only the latter of these mentions what struck me immediately in the visualization — the unusually large number of women represented as creators and not only subjects of an artistic movement.
I follow Arsenal because I am a committed glutton for punishment. Agency Signal | Noise built this handy visualization to help you follow the rate and flow of players and money in European football. It’s helpful to see who’s accelerating the pace of cash out for talent — and vice versa.
h/t Information Aesthetics
Excellent visualization of Game of Thrones characters and their events trajectory (spoiler alert: lots of killing) over time by Jerome Cukier (h/t Nathan Yau).
Love the proliferating use of data visualization to understand complex character structures and plot lines in novels; see also Infinite Atlas and this selection from brain pickings.
It takes a curious mixture of narcissism, introspection, and discipline to engage in personal analytics on any level, much less dialed up to Feltronesque quantified self. This quick download of my Facebook activity since September 2010 confirms:
- I use words (189) more than pictures (47), and neglect video (1) almost entirely
- My friends are a bit more female (53%) than male (47%), hail from 24 countries, and include 1 fervent monarchist
- Inexplicably, I post most often at 9pm on a Tuesday night
Aside from the vague shock of realizing where one’s time goes (I recommend Rescue Time for a sobering application analyzing web use), the possibilities for personal analytics are enormous. Nike+ FuelBand is a great example of a personal analytics service that’s addictive and competitive, and effectively connects long term fitness goals to short term behavior.
What are the effects of aggregating personal behaviors at this level — not even explicit consumer tastes, just daily habits? We live our lives in public as never before, and what may seem mundane — the precise time we’re gazing into the iPhone’s glowing screen on a Tuesday evening — could lead to useful personal insights, relevant commercial applications, and of course privacy concerns.