An interesting new visualization for Twitter content was launched Wednesday evening. The Social Collider—a Google Chrome Experiment created by Karsten Schmidt and Sascha Pohflepp of the UK—reveals cross-connections between conversations on Twitter.
Social Collider, a Chrome Experiment in action
With the Internet’s promise of instant and absolute connectedness, two things appear to be curiously underrepresented: both temporal and lateral perspective of our data-trails. Yet, the amount of data we are constantly producing provides a whole world of contexts, many of which can reveal astonishing relationships if only looked at through time.
The solution is inspired by visualizations for particle colliders, drawing pictures of how the subatomic matter interacts. The tweets that resonated with others connect horizontally with those of other users writing about relevant topics. Sometimes the connections are direct and obvious, but this visualization also reveals more subtle relationships amongst all the Twitter activity.
To start the visualization, you define two options. First, the keyword search can be done on a username, a phrase, or the trends tracked by Twitter. Second, determine the duration of the data set, from one day up to one month. Once submitted, the querying begins to populate a graph with tweets. The most recent tweets are represented as dots at the top of the screen, and each user in the network appears as its own column of dots—identified by topic, link, or username at the bottom of the screen.
That’s when the magic happens.
Using connector cues, such as @username references and shared links, the visualizations starts tracing the connections made between tweets. Every colored track is a new related topic; You can trace the patterns of how the content and people are connected by following any line. A flash of retweets might be a tight circle contained near the top of the page, whereas more persistent topics run the height of the screen. Conversations spiral around between users, easily identified from the mass of dots.
The weaknesses of Social Collider are few but important. Each tweet can be revealed by mousing over any dot, but the target is so small that this becomes a difficult task, impeding exploration. At least on my MacBook, loading this tool results in that jet engine sound that accompanies a heavy processor load. I was unable to leave the visualization, too, without the browser asking me to manually stop the script from running.
I was impressed, however, with the patterns that did emerge. The user search appears to be the easiest to comprehend, showing how my most recent tweets on topics like Star Trek and AIG fit in with or sparked other posts. My new book, “Twitter API: Up and Running,” got a plug and a bunch of retweets a week ago. Those show up as a swirl and a red line crossing several users. My test of the Twitter trend “AIG” was surprisingly segregated, with not much activity running horizontally. I’m not certain what that means, but I do appreciate that the visual patterns are made easy to identify should I want to investigate.