Since last Friday, when we first published Breaking Bin Laden: Visualizing the Power of a Single Tweet, our analysis and data visualization of the way news filtered out around the Bin Laden raid via Twitter, we’ve been overwhelmed by the response. Thousands of Tweets, many in Spanish, French, German and Japanese.
There have been quite a few interesting articles written about our post as well. The Guardian asked important questions about how journalists can respond to the tremendous velocity of the real-time web. Over at Fast Company, Brian Solis used our visualization as a jumping off point for a discussion of who matters in “the information economy.”
There have been plenty of inquiries about the graph itself, so we wanted to provide you with the opportunity to explore it in greater depth. Click on the image below or download it, and zoom in to get a closer look at all of the intersecting forces that propelled a single tweet to its eventual astonishing spread.
Each node represents a twitter user that mentioned @KeithUrbahn within 1 hour and 15 minutes of his infamous tweet referencing a phonecall with a trustworthy information source revealing that Osama Bin Laden was killed. Each edge (arch between two nodes) represents a mention or retweet that took place between two users. The larger the node, the more mentions and retweets it generated. Same goes for the width of all edges.
One important attribute we added to the graph is a representation of time. The lighter color the nodes and edges have, the earlier the user participated in passing on Keith Urbahn’s message. Both @keithurbahn and @brianstelter have a lighter shade, as Brian’s tweet came within a minute of Keith’s initial post. To provide some perspective, if you browse over to the right side of the graph, you’ll see @ObamaNews and the splurge of retweets it generated. @ObamaNews posted its response to @keithurbahn within 6 minutes of Keith’s initial tweet. This point in time is also whats called the 25th percentile, meaning, by the time that @ObamaNews reposted @KeithUrbahn’s tweet, 25% percent of total reactions have already been published.
For network graph analysis and visualization we used an open source tool called Gephi and for number crunching and plots we used the R statistical language, both staples of social data analysis. Below are a six screenshots, each zooming into a different section of the graph. Feel free to download and use them. All we ask for is SocialFlow attribution and some link love in return.
Download a high resolution SVG version of the graph here.
Other available images:
[
[
[
[
[
[As always, would love to hear comments, thought and questions.
The SocialFlow R&D team: @gilgul | @dgaff | @cherplunk
