Correlating Code & Community (part 3)
The first step in my attempt to combine DH and CCS approaches was to mine data from the ROMhacking website and paratexts distributed with the patches and to create a spreadsheet with relevant metadata. (All of this data is also included in the "Tools" rubric of this webtext.) On this basis, I began trying to analyze and visualize the data with the visualization software Tableau. But while this yielded some basic information that might be relevant for assessing the serial community (e.g. the number of mods produced each year, including upward and downward trends; a list of the top modders in the community; and a look at trends in the types of mods/hacks being produced), the visualizations themselves were not very interesting or informative on their own.
How could this high-level metadata be coordinated with and brought to bear on the code-level serialization processes that we saw in the hexcode? In looking for an answer, it became clear that I would have to find a way to collect some data about the code itself. The mods, themselves basically just "diff" files (i.e. files containing a record of the differences that are to be instantiated with respect to the original ROM file that is to be patched and "modded"), could be opened and compared with the "diff" function that powers some forms of DH-based textual analysis (for example, text comparisons conducted with a piece of software like juxta). But the hexadecimal code that we can access here — and the sheer amount of it in each modded game, which consists of over 42000 bytes — is not particularly conducive to analysis with such tools. 
Many existing hex editors also include a "diff" analysis, but it occurred to me that it would be more desirable to have a graphical display of differences between the files in order to see the changes at a glance. My thinking here was inspired by hexcompare, a Linux-based visual "diff" program for quickly visualizing the differences between two binary programs. However, the comparison here is restricted to local use on a Linux machine, and it only considers two files at a time. If this type of analysis was to be of any use for seriality studies, it would have to assess a much larger set of files and/or automate the comparison process.
This is where my colleagues from Duke University's Visualization & Interactive Services stepped in and helped me to develop an alternative approach. Eric Monson wrote a script in Python that analyzes the mod patch files and records the basic "diff" information they contain: the address or offset at which they instruct the computer to modify the game file, as well as the number of bytes that they instruct it to write. With this information, a much more useful and interactive visualization can be created with Tableau.
Following a suggestion from Angela Zoss, Gannt charts are used here to represent the size and location of changes that a given mod makes to the original Mario game; thus, it becomes possible to see a large number of these mods at a single glance, to filter them by year, by modder, by title, or even size, and in this way we can begin to see patterns emerging. (These interactive visualizations are included in the "Tools" section of this webtext.) In this way, we bring a sort of "distant reading" to the level of code, combining DH and CCS. (Contrast this approach with Marino's 2006 call to "make the code the text," which despite his broad understanding of code and acknowledgement that software/hardware and text/paratext distinctions are non-absolute, was still basically geared towards a conception of CCS that encouraged critical engagements of the "close-reading" type. As I have argued, however, researching seriality in particular requires that we oscillate between big-picture and micro-level analyses, between distant readings of larger trends and developments and detailed comparisons between individual elements or episodes in the serial chain.)
But to complete the approach, we still need to correlate this code-based data with the social level of online modding communities. For this purpose, I initially used Palladio (a tool explicitly designed for DH work by the Humanities + Design lab at Stanford) to graph networks on the basis of metadata contained in "Readme.txt" and other accompanying paratextual files. Here, for example, I have mapped the references (or "shout-outs") that modders made to one another in these paratexts, thus revealing a picture of digital seriality as a kind of "imagined community" (Anderson 1991) of modders:
Below, on the other hand, I have mapped paratextual references to various online communities that have come and gone over the years. We see early references to the now defunct TEKhacks website, by way of Zophar's Domain, Acmlm's and Insectduel's boards, with more recent references to Romhacking.net, the most recent community site and the one that I am studying here.
(Note that one of the limitations of Palladio is that it is not possible to save and embed these visualizations for online interactive use. Thus, in the "Tools" section, readers will find a variety of "Community Network" graphs rendered in Cytoscape, an open-source network visualization program that does not have these limitations, and that can output its graphs as interactive webpages. Beyond this it remains possible, however, for interested readers to copy the underlying data, also included in the "Tools" section, and to utilize Palladio for local browser-based filtering and analysis.)