One of the oft-discussed challenges of working with social media data, especially Twitter, is the ephemeral nature of the data. If we do not collect the data in real-time, it disappears. While it is possible could purchase historical data from GNIP or DataSift; the cost is out of reach of most researchers. It’s easy to think that we’ve dodged that bullet of ephemerality once we’ve built an archive of tweets and other social media data about a specific event or topic. But have we?
What about the URLs in these tweets?
Twitter is a microblogging service in which the text of each message or tweet is up to 140 characters long. The tweet text can contain various elements including mentions of other users, hashtags, emoji, and URLs. What happens when a URL is included in the text of the tweet? Do the contents of the URL become part of the tweet? Can you understand the content and context of the tweet if you only read the text alone? Does the URL, in a way, extend the text of the tweet beyond 140 characters?Read More
Metaphors are magical. Unlike similes or analogs, which express a one-to-one mapping of concepts, metaphors are equivocal; they imply and suggest qualities that are not immediately apparent. Presented with a simile, we can accept or reject it. Offered a metaphor, we’re likely to respond with a “yes, but…” or with a quizzical “in what way do you mean this?” The mystery of the metaphor charms us into examining more closely what it is and how it fits into the context. A metaphor engages us; it invites us to think about how different aspects of the metaphor can create new meaning and understanding of the situation.
Metaphors are ambiguous. Through this ambiguity, they can provoke reflection, initiate discussions, and even provide a bridge to the unknown from something known. At their best, metaphors help us make sense of new situations and gain insight about the nature of disruptive events and evolving situations.
But at their worst, metaphors can constrain our thinking and limit our imagination. Instead of magic and engagement, a metaphor can be a sleight of hand that focuses our attention away from the critical actions and significant issues. This misdirection can happen implicitly without our awareness, as many metaphors have become so embedded in our discourse that we forget they are metaphors. References to space and vision are particularly evident in our everyday research discussions: I see what you mean [do we acknowledge that seeing is not comprehending?]; my area of research [deliberately bounding our interest and limiting our range of attention]; the information superhighway [directing us to consider that information and knowledge, as if they were in containers, move along predetermined paths]; etc.Read More
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
In the case of social media researchers, the situation today is that much of our research is like the flight data recorder: we collect, store, and report data and analyses, but we follow the dictum on the outside and “do not open” the box.
We’re discovering this is a mistake.
By keeping the black box closed, we can create a misleading impression when we report our research results. We inhibit others from replicating our findings or testing the limits of our results if we do not fully disclose the details of our processes. We may also miss the chance to ask research questions if we ignore the opportunities to explore the data by testing the sensitivity of our findings to changes in our research procedures. There are some things we can do from the outside—approaches borrowed from systems theory and systems analysis approaches—but all of us will improve our research as we make our methods more visible…as we open up the black box.
Let’s look at some examples. In conducting research with social media data, it’s helpful to think about the sequential ELT steps in data warehousing systems. In following these steps, we: Extract (data from streams or sources), Transform (the data by parsing it and including metadata that enable us to address our research questions), and Load (the transformed data into an accessible dataset). And these are just the first steps—before we begin our analysis. At each step, small variations in the procedures or rules we use can result in significant shifts to our later findings, to the questions we are capable of answering, and even to questions we can imagine asking. For example, suppose we want to do an analysis of Twitter messages. In extracting Twitter data, do we use the Twitter API? If so, do we collect the data in real time (streaming API) or do we employ queries (search API), getting some retrospective tweets? If we opt not to use the API, we could use one of several developer-based or commercial services (e.g., Gnip) to get our data, but can we afford it? Each may have advantages, but the samples that result from each may be different. If the samples differ, can we be confident in our research results in each case?Read More
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 More
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 More