Playful Visualizations at Work, Working Visualizations at Play

Posts tagged ‘Human vs. Machine’

To each his (or her) own.

In this post, I would like to bring up something that I know Liz, Meaghan, and I have talked about in person, but have yet to discuss in LuAn (you know, the “cool” way to refer to our blog, Ludic Analytics).  The theme of today’s post:  Subjectivity.

Many of the visualizations that we create are based on a series of rules; regulations that each person as a visualization creator must invent before approaching the source to collect data.  In life, I tend to be a rule follower. I’m good at standing in lines, maybe not so good at coloring inside the lines, but overall I like structure.  The problem, however, is the fact that when one is creating the rules it’s a) easier to both follow and (occasionally) break said self-created rules and b) one person’s rules will be different from another’s.

I’ll give an example to help elucidate the point.  Liz and I both have worked on network graphing the dialog in the novels that we are studying (I believe she will post some interesting graphics on her work soon).  The other day, she mentioned the problem of judging what is, and what is not, dialog in a novel.

With certain genres, such as plays, this is less of a problem.  That’s why, I believe, many network graphs of literature are often done on theatrical works, especially Shakespeare (like Moretti’s work on Hamlet).  However, with novels, there are different types of dialog, and sometimes it is not as easy to grasp the flow of conversation.

I know that when I approached this problem, I resorted to making a list of rules.  I needed some structure to validate what I was doing.  I think, in a way, I wanted to make it more “scientific.”  Here are a few examples from my recent dialog network project:

1) It counts as dialog even if the protagonist talks to himself, as long as the comment is made “outloud” (in La tumba, this type of dialog is marked by a “-“, so it’s easier to see compared to some other novels)

2) If however, the comment is not “outloud” it does not count

3) Implied dialog does not count (if there is mention of two characters talking, but the reader doesn’t know what was said)

4) If the speaker is talking to more than one person, each listener will be listed

5) The first person who speaks is the speaker, and the other is the listener (meaning that directionally, the arrow representing the edge between the nodes will travel from speaker to listener, even if both actively participate in the conversation).

The list goes on.  Despite the fact that I created the list, I still found myself in the midst of grey areas.  To attempt to find more black/white territory and avoid the penumbra, I would make another rule.

This might be an extreme example; I got a bit carried away with the rule making.  Yet, anyone who has approached a text for this type of data classifying knows that it can be challenging to decipher different aspects of a text or in this case (to continue with the example) every instance of dialog.

In fact, almost all of the work done for this visualization was by hand (excepting, of course, the actual visualization); which, incidentally brings up the other issue of human vs. machine readings.  Could I have saved myself the work of manually mining the data?  Perhaps.  I’m sure some sort of program could be written to do the reading for me.  But, would the computer “know” who is talking?  Can the computer understand the context enough to fill in the character’s name if it were not mentioned?  Yes, but only if I “taught” the computer to do that, and even then, it might not always be right.  Also, while I may teach students daily, my methods for teaching a computer (i.e. programming) are not at the level where I could teach it to recognize specific characters.

In my case, it was easier to go through the book myself.  The result?  It was not as exciting as I had hoped, but I don’t know what I expected.  After all, in a first person narrative, most of the dialog does indeed revolve around the protagonist, with very few instances (just one in this case) of outside conversations.  However, the only slightly surprising factor was how many conversations there are.  Twenty-two nodes!  I know the novel is known for its use of dialog, but I had not realized how many different people are a part of these conversations with the protagonist.

Dialog Network for La Tumba

As for the subjectivity aspect of this post, it would be interesting to see someone else’s dialog network visualization of the same work (and based on his or her set of rules).  Would this somehow change the appearance of the graph?  I assume it would, considering even the presentation aspect was up to me.  I picked the color, yellow (seemed like a good choice at the time) and then changed the layout to better see the edges, so it was more aesthetically pleasing (at least to my eyes).

The more and more that I work with visualizations, the more I realize how much they are an extension of me: from my methodology in collecting the data, to my interpretation of the data, and finally to my presentation of it.  However much I strive to make a logical and objective product, I can never seem to separate it from being a form of (personal) expression.  Yet, I continually ask myself, is that a bad thing?  I think/hope not.

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