Using R to visually compare the volume of different information sources
A couple of weeks ago Bob wrote about a post about a research note that was recently accepted to the iConference. In it we outline the beginnings of a research project where we look at the interaction of different media platforms (Twitter and Blogs) with more traditional sources. In this post I go through the R code we used to plot, and visually compare, the volume of different information sources.
The data for this example is randomly drawn along a Pareto distribution so anyone should be able to just open the file, run it and have plots. Like I did in the last R example, I have used comments in the code to explain what I’m doing in the creation of these plots. After the code I give a brief introduction on the tool I use to select colors.
Read MoreHi, HICSS46 Social Media Research Workshop participants!
Thanks for visiting our workshop at HICSS46, or just being curious after spotting a tweet!
We recently held a workshop that dealt with our lessons learned in working with our corpus of data collected in reference to the Occupy Wall Street movement. We took folks through a mock research project and one approach to how researchers might “do” social media research, with hands on examples. You’ll find our “Working Document” that details these learned lessons, along with the slides presented at the workshop. You can find them by clicking “read more” below!
Read MoreHockey, Basketball,…and Research?
Recognizing patterns and rhythms in social media data
Wayne Gretsky is quoted as saying that a great hockey player plays where “the puck is going to be,” not where it is. Gretsky, like the great NBA point guards (think Magic Johnson or Mark Price), was quick to detect emerging patterns in movement and flows–then take advantage of what was about to happen. In our research efforts, we often try to detect patterns in order to explore what these patterns may tell us about underlying processes.
The SoMe Lab is examining patterns in the movement and flows of information between and among social media platforms. We observe that traditional media news may inform or trigger information exchanges in the blogosphere or Twitter; and vice versa. We want to look closely at these patterns to gain insights into phenomena such as virality, the birth and life cycle of interest networks, and the dynamics of a fluid cast of gatekeepers. The accompanying image illustrates the patterns that distinguish the volume of tweets, blog posts, and traditional news items following the pepper spraying incident at UC-Davis November 18, 2011.
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R/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 MoreGeographic Imaginaries and Locational Hashtags
One of the things I’ve grappled with in my studies of social media is how the nodes of networked dyads related to one another spatially. As a geographer, I’m well familiar with Tobler’s first law of geography: “Everything is related to everything else, but near things are more related than distant things.” I wanted to see if this held true for networked geographic imaginaries within our Occupy Twitter data set: do the ways in which users co-locate #occupy<city> hashtags within their tweets relate at all to the distance between the two cities mentioned?
Spoiler alert: no. But we learned some stuff along the way.
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