If you’ve read any of my previous posts you know that I am constantly experimenting with different ways to represent and explore social network data with R. For example, in previous posts I’ve written about sonification of tweet data, animation of dynamic twitter networks, and various ways to plot social networks (here and here). In each case the underlying idea is finding different ways to explore data under the assumption that sometimes just looking at something from a different point of view reveals something novel. In this post I will briefly discuss how to go from data to 3D model network, to 3D object using R most of the way.Read More
One difficulty with which social media researchers grapple is the separation of “noise” from “signal.” Noise is traditionally those data that don’t contain relevance to a given query – in this case, tweets about Occupy Wall Street, or more specifically, Occupy Oakland. Occupy Oakland took on the hashtag “#oo” fairly early into the occupations, which has served to create a headache for those of us at the SoMe Lab. Continuing my exploration into topic modeling and taking inspiration from digital humanists and practices of ‘pataphysics, I decided to explore the noise to see what it contains. After all, one person’s noise is another’s signal! Click through to read about #oo, emotions, tokenization, and linguistic difference and to see our first topic modeling visualization — and figure out what Ducktales has to do with #ows!Read More
In The Practice of Everyday Life, Certeau describes the process of “walking the city,” noting that the ways in which people experience the city are qualitatively different than what urban planners and sociologists are capable of measuring. I argue that this process of “walking a space” can be applied to the spaces of social media as well, particularly in regard to the spaces of discourse created by emergent hashtags. I’m also playing with MALLET, a tool for Latent Dirichlet Allocation (LDA) topic modeling for “big data” texts. I’m just getting started in the process of learning some of the computational tools needed for performing these “distant readings,” but already I’ve discovered ways in which “walking the data” might inform our practice as researchers. Click through to read an explanation of what I mean, an example or two of MALLET topic output, and how my own experience of “walking the data” as a lived event informs the analysis.Read More
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 More
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.Read More
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.Read More