46th HICSS Workshop
Workshop–Hawaii
Hawaii.
The name itself evokes images of tropical sun, warm waters, surfing, and relaxation.
So what are the people in this image doing inside, intent on looking at computer screens? Instead of savoring the sunshine and walking on the sand, here they sit. Inside. Hunched over laptops. Interpreting a series of instructions to make sense of social media data. Listening to the SoMe Lab team explain what they are seeing. They are not behaving as you imagine Hawaiian visitors would behave.
These dedicated researchers are taking part in the workshop organized by the SoMe Lab team at HICSS46, held this past January in Wailea on Maui. As a part of the workshop, they were hearing from the SoMe team about lessons the team has learned in the past fifteen months.
Read MoreR/iGraph Holiday network animation and How-to tips
The animation embedded in this post was done using R and the iGraph package and was, frankly, a great deal more work than I thought it would be when I started. What kept me going was a desire to express a wish for the New Year while also experimenting with some functionality that might be useful in my future research. In the following post I will provide some example code that extends my previous attempts at network animation by: 1) using the iGraph plot parameter margin to zoom in and out of different parts of the graph; 2) use the neighborhood function to highlight an information spread; and, 3) moving nodes along a path where you know the first and last point and the number of steps you want to make between them. I can imagine using the first two in my research, and the third was, well, just fun. I’m a geek. I’ll end the post with a line or two more about my motivations for creating this particular animation.
How to: network animation with R and the iGraph package & Meaning in data viz
This article lists the steps I take to create a network animation in R, provides some example source code that you can copy and modify for your own work, and starts a discussion about programming and visualization as an interpretive approach in research. Before I start, take a look at this network animation created with R and the iGraph package. This animation is of a retweet network related to #BankTransferDay. Links (displayed as lines) are retweets, nodes (displayed as points) are user accounts. For each designated period of time (in this case, an hour), retweets are drawn and then fade out over 24 hours.
Network vizualization and meaning shifting due to algorithm settings
Partially processed network layout
Data visualizations are useful for exploratory work and as an aid in communicating findings. Data visualizations also seem to be in demand these days as a kind of eye candy for capturing attention. But when we look at one engaging enough to hold our attention, we want to know what it means. In other words, we want to interpret the image we see and try to extract meaning. The image on the right is the same OccupyOakland retweet network that I have used in other posts (and in the post below), but it looks different. Why?
Read MorePower and Networked Social Movements
I presented a paper with Manuel Castells in the 13th annual meeting of AOIR (Association of Internet Researchers) from a study we are conducting regarding networked social movements. In April we presented some of the findings at the USC Annenberg School for the ANN-SONIC Fourth International Seminar. You can watch the video by clicking here, or you can read below for further explanation about the work and the dynamics of power among stakeholders.
Read MoreVisualization of a Twitter retweet network: art or useful data visualization?
This is a Twitter retweet network. When people tweet, they may get retweeted by other people, repeating the message for their followers to view. Each retweet is a one-way flow of information that links the first person to each person who retweeted them (forwarded the original tweet into their own network). So, in this visualization we are looking at a network of people (white nodes) linked (orange lines) by information flows in the form of 140 character retweets. But is this kind of visualization helpful for analysis or just a kind of computer generated eye-candy?