The project may feel like a creepfest--and to some extent, maybe it is. Because data visualization guru Santiago Ortiz mapped the relationships between every Twitter employee by mere eavesdropping, and he rendered the results in a stunningly detailed interactive graph.
But it’s notable that Ortiz was only working with public information. He had no inside information or access to special accounts. In fact, he worked without Twitter’s knowledge at all. Using Twitter’s API, Ortiz requested all the tweets authored by Twitter’s list of employees. Then he filtered that content, keeping only the tweets made between colleagues. With the rules in place, every step of the analysis is automated.
The result is a web of conversational relationships. The magenta lines are incoming tweets, while the cyan lines are outgoing. Individual accounts are sized based upon their tweet frequency. Click on any of those avatars, and you’re transported to a cluster of conversations--a staggeringly simple way to see exactly who someone has been talking to.
Better still, press play in any view, and you can see these conversations as animations. Individuals are even pulled around the simulation, attracted to one another like gravity as they speak. It’s as if you can see true human relationships--interpersonal closeness, and the balance between countless acquaintances, friends, and best friends--playing out in real time.
So why would Ortiz do this? It’s not a commission. He was just constructing his own network visualization platform for his freelancing business, and he wanted to see if he could actually map corporate hierarchy through Twitter conversations alone. (He figured there was no better case study than Twitter itself.)
“The question is if this network matches the company structure … I believe yes, at least to some extent,” Ortiz tells Co.Design. "For instance, you can see how people from U.K. tend to be clusterized, and the same happens with people from Japan. Also, it’s possible to identify clusters made from people of the same department.”
But to me, what would be particularly fascinating is if you could actually subtract that corporate hierarchy from what you see. So every conversation in which someone was tweeting to their boss (just because they’re their boss) is removed from the equation, and you’d be left with a strange snapshot of relationship outliers--a map of friendships or corporate romances that are bridging the gap of departmental groupings. And that, my friends, wouldn’t be just a little creepy. It would be exceptionally creepy.