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For the past few months I have been working on Dragon Age: By The Numbers. I became interested in doing a data-driven analysis of fandom trends by way of my interest in the debates that float around fandom, as I tend to be very opinionated in these debates myself. There are all sorts of fascinating questions which simply beg for answers -- is slash really fetishized? Is it that everybody hates Anders or is it that everybody loves Anders? Is it fair to call the Dragon Age fandom sexist? Why doesn't femslash get more love?
These are not questions that can be answered solely through data, but I think that data can provide useful insight to their investigation. It can at least provide some substantiation of the claims that we make while arguing with one another. And, if done right, it may well bring up new questions.
To these ends, I have set about gathering, interpreting, and visualizing data about the fan works produced by the Dragon Age fandom. This is an ongoing process, and I am not nearly done yet. I doubt I ever will be. What I can do is post my results as I go along, and to make my methods as transparent and open to collaboration as possible.
My data source for this project is the “Dragon Age - All Media Types” tag on Archive of Our Own. I gathered this data using an app called kimono, with which I was able to obtain a dataset thousands of rows long containing all sorts of information about each story found in the Dragon Age tag. This dataset is up to date as of April 15, 2014.
It is important to note that this dataset is not perfect. Ao3 is likely not representative of the Dragon Age fandom; I chose it as a source because of its seeming popularity among tumblr users (who I see as my primary audience) and because it is the website that is most easily compatible with kimono. I imagine that gathering data from Fanfiction.net or the Dragon Age Kink Meme would yield different and interesting results, but gathering that data would be a trying process (perhaps one day I will atempt it anyway). Additionally, collecting data with kimono is a process that inevitably creates some inaccuracies. I don’t feel that these inaccuracies are severe enough to invalidate my conclusions, but you can check out a full explanation of my process in Chapter 2 and decide that for yourself.
As I do work on this project, I will continue to post my results as new chapters in this work, and I will continue to update the Table of Contents in this introductory chapter as the work evolves. If you want to follow the progress of this project, the easiest way is probably to subscribe to this work. You could also follow the blog I help run on tumblr, or you could track the "dragon age by the numbers" tag on tumblr.
If you would like to see/play with the raw data yourself, please leave me a comment or drop me an ask on tumblr and I’ll be glad to share it with you. Also definitely hit me up if you know anything about data analysis/coding, because I have been teaching myself all this stuff myself and would definitely welcome assistance!
Many thanks to my dear firstblush, who taught me how to use Excel, merged many a csv file for me, and provided assistance in doing calculations. Many thanks also to my kind teachers, who advised me on how to best collect and present the data, and helped me immensely with cleaning it.
And now that that talking's all over with, here is my table of contents, in which you will find a brief summary of every chapter in addition to its title, so that you can easily find what interests you the most. Happy reading, and may you find this analysis intriguing.
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TABLE OF CONTENTS
Chapter 1: Introduction - In which I tell you what this project is all about, provide brief justification for its existence, and furnish readers with a table of contents. (Hint: You're here right now!)
Chapter 2: Methodology for collecting, cleaning, and formatting the data - In which I explain more thoroughly exactly how I obtained this data and how I finagled it into a useable state. This chapter exists for transparency's sake and is not super interesting in and of itself. Skip it unless you find methodology super interesting, you want to copy my process to round up some of your own data, or you are skeptical about the validity of my data/conclusions and want to take a look at exactly how the sausage got made.
Chapter 3: What does DA fandom like to write? - In which I investigate which fan works are most popular in terms of number of works written and number of words written. Basically, I seek to answer the question of what kinds of works are most commonly uploaded. The analysis in this chapter is conducted mostly through the lens of gendered relationship categories (F/F, F/M, M/M, Gen, Multi, Other, No Category).
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