For Lab 6, I decided to take a dialog heavy show, The West Wing, and map out the interactions within a brief segment. Within the first eight pages of the pilot episode, thirteen characters are introduced. Though this may seem like a large number, this particular segment is not as indicative of the show as I had assumed. Usually, characters pop in and out of any given on screen interaction, but in the case of the pilot’s introduction, the characters mostly interact one-on-one. If we look at Table 1, we can see that the characters outnumber the interactions. Also, the first two seasons of The West Wing focuses primarily on one character, Sam Seaborn, but the pilot seems to revolve around Leo McGary, the Chief of Staff.
Graph 1 below shows the characters, their interactions, and to whom they report (Staff Head in Table 1). I had initially set the attribute (Staff Head in this case) to political affiliation, but I ended up listing Democrat over and over. Staff head made for a much more interesting visualization. I did not intend to weight the interactions, but since the characters never interacted more than once with one another, the weights are all equal.
This process made me analyze shows in a rather interesting way. It made me question my assumptions of the greater picture and look further into integral parts. The narrative or the narrative’s interactive balance can change focus dependent on the particular need of the story. Typically, The West Wing maintains a relatively equal focus on all its characters (even through the early focus on Sam Seaborn), but in this segment, we can clearly see that Leo McGary is involved in eight out of eleven interactions. From this bit of script, we might assume that he is the main character. This is perhaps a larger problem of scope. While I can analyze this part and come to specific conclusions, I highly doubt they would stand for the entire show’s run or even for this episode.
I found that picking a segment and sticking with it was the most difficult part of this Lab. I wanted to fit the data I gathered to a particular assumption I already held. While this bias had me increasing the scope a bit to try to find what I was looking for, I realized that this defeated part of the process. True, we can use network analysis to prove our hypotheses, but what good does that do if we do not take the pieces that prove the null and use that to our advantage. For The West Wing, I can now see that the writers, Aaron Sorkin in particular, shift the focus in subtle ways that I did not initially see. While this does not change my view of the show, it does illustrate the hidden nuances that literature can have.
In a more general sense, network analysis allows us to make note of particular themes in literature. The interactions seems the be the main focus of the analysis, but I believe that the attribute data set can be used to great effect. We can use it to track changes in characters over time and account for writing trends. This means taking network analysis into a much larger scope than we did for this lab, but with that data, we can visualize how peoples are represented. The ability to change the scope of network analysis is perhaps one of the most powerful aspects of this research. I can use it for a single segment of a show and learn something that I can compare to other equally sized segments, or I can apply it to an entire genre and use it just as effectively.