The phrase cultural analytics generally refers to analyzing vast amounts of image, text, or other media through computational methods. Think of it as data science aimed towards arts and culture. But unlike data science, cultural analytics isn’t necessarily asking political-social-economic questions. Rather, cultural analytics seeks to help us see the world in a new way, generating more questions than answers.<\/p>\n
In this lab we’ll attempt a special kind of cultural analytics. Instead of looking at a vast number of texts (say, the way Ben Schmidt analyzes State of the Union addresses<\/a>, or how Lev Manovich analyzes Instagram selfies<\/a>), we’ll break apart a single text\u2014a film\u2014into a vast number of discrete parts, and analyze those parts in the aggregate. Some researchers call this technique “image summation.”<\/p>\n Elements of this procedure have been adapted from Dr. Brian Croxall’s similar exercise<\/a> at Brigham Young University. Thanks, Brian! We’ll also be using an online image analysis tool<\/a> developed by Dr. Zach Whalen at the University of Mary Washington. Thanks, Zach!<\/p>\n First, you’ll need to extract still images from the film that you’ve ripped or otherwise acquired.<\/p>\n You can do this with the open source media player VLC<\/a>. After you’ve installed VLC, you need to change its settings in order to extract frames. Make these changes before<\/strong> you open your video file in VLC.<\/p>\n This method extracts frames in real time, which means it will take several hours (as long as the film) to extract all the images. Obviously, we don’t have enough time in class to complete this process. You’ll work on your own film outside of class. For the purposes of class, I’ve extracted frames from three different works: “The Entire History of You”<\/a> from\u00a0Black Mirror, <\/em>The Fast and the Furious<\/em><\/a>, and the first episode<\/a> of\u00a0Game of Thrones<\/em>. You can select one of these three videos to use during class.<\/p>\n For analysis of our images, we’re going to use Imj, a web-based image analysis tool. As the tool’s creator, Zach Whalen says<\/a>, this technique isn’t all that powerful compared to other desktop-based tools, but it does “enable some low-level visualizations that might help researchers or students determine whether an investigation with more robust tools is warranted.”<\/p>\n In particular, Imj supports three types of visualizations:\u00a0 barcodes, montages, and scatterplots. Basically, you upload your folder of extracted frames (up to 9999 frames), and let Imj do the work.<\/p>\n Use Imj<\/a>! Subject your movie to all three visualization types. For details on how each visualization works, read Zach Whalen’s guide to Imj<\/a>.<\/p>\n For the purposes of writing your lab report, you’ll use Imj on a film of your own. Follow the instructions above for using VLC to extract frames. Then subject your video to all three visualization types. Download the results (the barcode, montage, and plot) in order to include these images in your lab report.<\/p>\n In a 300-500 word lab report, reflect on some of the following questions:<\/p>\n Share the report with masample@davidson.edu as a Google Doc by end of the day, Monday, November 20. (Remember there is no class on Monday, November 20).<\/p>\n <\/p>\n <\/p>\n","protected":false},"excerpt":{"rendered":" Overview The phrase cultural analytics generally refers to analyzing vast amounts of image, text, or other media through computational methods. Think of it as data science aimed towards arts and culture. But unlike data science, cultural analytics isn’t necessarily asking political-social-economic questions. Rather, cultural analytics seeks to help us see the world in a new … <\/p>\nProcedure<\/h1>\n
Extracting Stills<\/h2>\n
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Analysis<\/h2>\n
Lab Report<\/h1>\n
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