An Analytical person at the Nutcracker (or Research Methodology, Nutcracker Style)

Last night we attended a Russian Ballet performance of the Nutcracker. It was a great performance, and fun was had by all.

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Early in the performance I realized that although I have developed some understanding of the ballet, I hadn’t shared any of that knowledge with my kids. At this point, I started whispering to them quietly to explain what they were seeing. I whispered quick, helpful comments, such as “those are toys dancing” and “the kids have gone to sleep now, so this is just the adults dancing.” It wasn’t long into the performance that this dynamic began to change. I realized that their insights were much funnier than mine “wow, that guy should go on ‘So You Think You Can Dance!’ or ‘The Voice’ or something! “and that my comments were starting to be pretty off-base. My comments evolved into a mash-up of “The kids have gone to sleep now” “No, I guess the kids haven’t gone to sleep yet” “I really can’t tell if the kids are still up or not!” and “Those are the sugarplum fairies” “Wait, no, maybe these are the sugar plum fairies?” and “I don’t know, sweetie, just watch them dance!” By the end of the show I had no idea what was going on or why the Chuck.E.Cheese king was dancing around on stage (although one of the girls suspected this particular king was actually a bear?). The mom next to me told me she didn’t know what was going on either “and,” she added, “I go to the Nutcracker every year! Maybe that was what made it a Russian Nutcracker?” …And here I thought the Russian influences were the Matryoshka dolls and the Chinese dancers clothed in yellow (despite the awkward English conversation that the costumes prompted).

At the beginning of the show I was nervous to whisper with my kids, but I soon realized that there was a low hum all around me and throughout the concert hall of people whispering with their kids. This, I think, is what remix research methods should be all about- recording and interviewing many audience members to gain a picture of the many perspectives in their interpretations of the show. Here is a challenge question to my readers who are hipper to qualitative research methods: what research strategy could best capture many different interpretations of the same event?

Earlier this week I spoke with a qualitative researcher about the value of an outsider perspective when approaching a qualitative research project. Here is a good example of this dynamic at play: people clapped at various parts of the performance. I recognized that people were clapping at the end of solo or duo performances (like jazz). If I were to describe these dances, I would use the claps as a natural demarcation, but I probably would not think to make any note of the clapping itself. However, the kids in my crew hadn’t encountered clapping during a show before and assumed that clapping marked “something awesome or special.” Being preteens, the kids wanted to prove that they could clap before everyone else, and then revel in the wave of clapping that they seemingly started. At one point this went awry, and the preteens were the only audience members clapping. This awkward moment may have annoyed some of the people around us, but it really made the little sister’s day! From a research perspective, these kids would be more likely to thoroughly document and describe the clapping than I would, which would make for a much more thorough report. Similarly, from a kids-going-to-a-show perspective this was the first story they told to their Dad when they got home- and one that kicked off the rest of our report with uncontrollable laughter and tears.

As the show went on and appeared not to follow any of the recognizable plot points that I had expected (I expected a progressive journey through worlds experienced from the vantage of a sleigh but instead saw all of the worlds dancing together with some unrecognizable kids variously appearing on a sleigh and the main characters sometimes dancing in the mix or on their own), I began to search for other ways to make sense of the spectacle. I thought of a gymnast friend of mine and our dramatically different interpretations of gymnastics events (me: “Wow! Look what she did!” her: “Eh, she scratched the landing. There will be points off for that.” Which parts of the dancing should I be focusing on? I told my little one “Pay attention, so we can try these moves at home.” Barring any understanding of the technical competencies involved (but sure that laying your body at some of these amazing angles, or somehow spinning on one foot, or lifting another person into the air require tons of training, skills and knowledge) or any understanding of the plot as it was unfolding in front of me, I was left simply to marvel at it all. This is why research is an iterative process. In research, we may begin by marveling, but then we observe, note, and observe again. And who knows what amazing insights we will have developed once the process has run its course enough times for events to start making sense!

To be a researcher is not to understand, but rather to have the potential to understand- if you do the research.

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Reflections on Social Network Analysis & Social Media Research from #SMSociety13

A dispatch from a quantitative side of social media research!

Here are a few of my reflections from the Social Media & Society conference in Halifax and my Social Network Analysis class.

I should first mention that I was lucky in two ways.

  1. I finished the James Bond movie ‘Skyfall’ as my last Air Canada flight was landing. (Ok, I didn’t have to mention that)
  2. I finished my online course on Social Network Analysis  hours before leaving for a conference that kicked off with an excellent  talk about Networks and diffusion. And then on the second day of the conference I was able to manipulate a network visualization with my hands using a 96 inch touchscreen at the Dalhousie University Social Media Lab  (Great lab, by the way, with some very interesting and freely available tools)

 

This picture doesn't do this screen justice. This is *data heaven*

This picture doesn’t do this screen justice. This is *data heaven*

Social networks are networks built to describe human action in social media environments. They contain nodes (dots), which could represent people, usernames, objects, etc. and edges, lines joining nodes that represent some kind of relationship (friend, follower, contact, or a host of other quantitative measures). The course was a particularly great introduction to Social Network Analysis, because it included a book that was clear and interesting, a set of youtube videos and a website, all of which were built to work together. The instructor (Dr Jen Golbeck, also the author of the book and materials) has a very unique interest in SNA which gives the class an important added dimension. Her focus is on operational definitions and quantitative measures of trust, and because of this we were taught to carefully consider the role of the edges and edge weights in our networks.

Sharad Goel’s plenary at #SMSociety13 was a very different look at networks. He questioned the common notion of viral diffusion online by looking at millions of cases of diffusion. He discovered that very few diffusions actual resemble any kind of viral model. Instead, most diffusion happens on a very small scale. He used Justin Bieber as an example of diffusion. Bieber has the largest number of followers on Twitter, so when it he posts something it has a very wide reach (“the Bieber effect”). However, people don’t share content as often as we imagine. In fact, only a very small proportion of his followers share it, and only a small proportion of their followers share it. Overall, the path is wide and shallow, with less vertical layers than we had previously envisioned.

Goel’s research is an example of Big Data in action. He said that Big Data methods are important when the phenomenon you want to study happens very infrequently (e.g. one in a million), as is the case for actual instances of viral diffusion.

His conclusions were big, and this line of research is very informative and useful for anyone trying to communicate on a large scale.

Sidenote: the term ‘ego network’ came up quite a few times during the conference, but not everyone knew what an ego network is. An ego network begins with a single node and is measured by degrees. A one degree social network looks a bit like an asterisk- it simply shows all of the nodes that are directly connected to the original node. A 1.5 degree network would include the first degree connections as well as the connections between them. A two degree network contains all of the first degree connections to these nodes that were in the one degree network. And so on.

One common research strategy is to compare across ego networks.

My next post will move on from SNA to more qualitative aspects of the conference

Source: https://twitter.com/JeffreyKeefer/status/378921564281921537/photo/1 This was the backdrop for a qualitive panel

Source: https://twitter.com/JeffreyKeefer/status/378921564281921537/photo/1
This was the backdrop for a qualitative panel. It says “Every time you say ‘data driven decision’ a fairy dies.

Digital Democracy Remixed

I recently transitioned from my study of the many reasons why the voice of DC taxi drivers is largely absent from online discussions into a study of the powerful voice of the Kenyan people in shaping their political narrative using social media. I discovered a few interesting things about digital democracy and social media research along the way, and the contrast between the groups was particularly useful.

Here are some key points:

  • The methods of sensemaking that journalists use in social media is similar to other methods of social media research, except for a few key factors, the most important of which is that the bar for verification is higher
  • The search for identifiable news sources is important to journalists and stands in contrast with research methods that are built on anonymity. This means that the input that journalists will ultimately use will be on a smaller scale than the automated analyses of large datasets widely used in social media research.
  • The ultimate information sources for journalists will be small, but the phenomena that will capture their attention will likely be big. Although journalists need to dig deep into information, something in the large expanse of social media conversation must capture or flag their initial attention
  • It takes some social media savvy to catch the attention of journalists. This social media savvy outweighs linguistic correctness in the ultimate process of getting noticed. Journalists act as intermediaries between social media participants and a larger public audience, and part of the intermediary process is language correcting.
  • Social media savvy is not just about being online. It is about participating in social media platforms in a publicly accessible way in regards to publicly relevant topics and using the patterned dialogic conventions of the platform on a scale that can ultimately draw attention. Many people and publics go online but do not do this.

The analysis of social media data for this project was particularly interesting. My data source was the comments following this posting on the Al Jazeera English Facebook feed.

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It evolved quite organically. After a number of rounds of coding I noticed that I kept drawing diagrams in the margins of some of the comments. I combined the diagrams into this framework:

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Once this framework was built, I looked closely at the ways in which participants used this framework. Sometimes participants made distinct discursive moves between these levels. But when I tried to map the participants’ movements on their individual diagrams, I noticed that my depictions of their movements rarely matched when I returned to a diagram. Although my coding of the framework was very reliable, my coding of the movements was not at all. This led me to notice that oftentimes the frames were being used more indexically. Participants were indexing levels of the frame, and this indexical process created powerful frame shifts. So, on the level of Kenyan politics exclusively, Uhuru’s crimes had one meaning. But juxtaposed against the crimes of other national leaders’ Uhuru’s crimes had a dramatically different meaning. Similarly, when the legitimacy of the ICC was questioned, the charges took on a dramatically different meaning. When Uhuru’s crimes were embedded in the postcolonial East vs West dynamic, they shrunk to the degree that the indictments seemed petty and hypocritical. And, ultimately, when religion was invoked the persecution of one man seemed wholly irrelevant and sacrilegious.

These powerful frame shifts enable the Kenyan public to have a powerful, narrative changing voice in social media. And their social media savvy enables them to gain the attention of media sources that amplify their voices and thus redefine their public narrative.

readyforcnn

Funny Focus Group moment on Oscars

It’s not often that aspects of survey research make it into the public sphere. Last night’s Oscars included some “recovered focus group footage” from the Wizard of Oz. It’s hilarious, and there’s a good reason why it is. Humor often happens when occurrences don’t match expectations. We tend to expect every member of the focus group to be reasonable and representative, but the reality of that just isn’t true.

 

Anyway, enjoy!