Digital Studies 101

A common resource for Digital Studies at UMW.

Tag: methodology

Networks

As per Introduction to Digital Humanities – Networks:

The concept of a network has become ubiquitous in current culture. Almost any connection to anything else can be called a network, but properly speaking, a network has to be a system of elements or entities that are connected by explicit relations. Unlike other data structures we have looked at–data bases, mark-up systems, classification systems, and so on—networks are defined by the specific relations among elements in the system rather than by the content types or components. The term network is frequently used to describe the infrastructure that connects computers to each other and to peripherals, devices, or systems in a linked environment. But the networks we are concerned with in digital humanities are created by relationships among different elements in a model of content.

Goals:

  • Understand how networks and relationships within a text work and the subsequent impact
  • Create and analyze a network visualization

Get Resources:

Tools:

Image Visualization

Why does Image Visualization Matter?

This topic may seem redundant, since images and movies are already visual, but what we’re talking about is analyzing images in a way or at a scale that, through visual means, reveal more about their composition, structure, origin, or meaning.

For example, what do you learn about a film by viewing it as a barcode-style image? Or as a single composite frame? What can you learn from thousands of selfies?

Learn:

  • Think about artifacts of visual culture from a different perspective
  • Learn to analyze individual artifacts within a stylistic context
  • Compare different software and different techniques for macroanalysis of visual artifacts

Build:

  • Create the same style of visualization for a number of works by the same author, director, studio, or artist.
  • Create and compare several different visualizations of the same work.
  • After making several visualizations, see if you can correctly identify them with their source material.

Resources and Examples:

FFMPEG:

Whatever approach you take, FFMPEG will likely make your work easier and faster. It’s a command-line tool that can do many, many things, including extracting frames from video.

To install it on a Mac, use the terminal:

  1. First install “Homebrew”:
    /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
  2. Then use Homebrew to install ffmpeg with this command:
    brew install ffmpeg

It also works on Windows, but there are a few more steps.

Either way, assume you have a movie saved as “the_godfather.mp4” in a folder by itself with a subfolder called “frames”. To extract 6 frames per second, the command would be:

ffmpeg -i the_godfather.mp4 -r 6 frames/img-%05d.jpg

This will generate thousands of images, so make sure your hard drive can handle it. Once you’ve got all these, you can try dumping them into various tools below.

Tools and Datasets:

 

Mapping

From UPenn Libraries:

By mapping real and imagined places, scholars can better argue and represent the significance of space and place in human experience and social structures.

But, maps themselves carry certain assumptions and connotations. From Introduction to Digital Humanities:

Maps are highly conventionalized representations, distortions, but they do not come with instruction books or warnings about how to read their encoding. In learning how to use GIS (Geo-Spatial Information Systems) built in digital environments, we can also learn to expose the assumptions encoded in maps of all kinds, and to ask how the digitization process reinforces certain kinds of attitudes towards knowledge in its own formats.

Goals:

  • Think critically about space and spacial representations
  • Create a map and interpret the results of the project

Resources:

Tools:

 

 

 

Text Analysis

Why Software-Driven Text Analysis Matters

Computers can’t think, at least not yet, but they assist human thinking every day whether we our phone’s calculator or answer a random question with a Wikipedia search. What about more complex forms of thought like literary analysis? As it turns out, there are some kinds of software-driven analyses that make useful tools to the human analyst.

Given, say, a novel, what are the most frequently used adjectives? Can you recognize the novel from a wordcloud? What words are likely to appear near each other? What is the text “about” on a semantic level? Does the author have certain habits that reveal themselves numerically? How does this version of a text differ from a previous edition or draft? What do those changes suggest about the writing process or the text’s position in culture?

These are the sorts of questions that computers can help us answer. They can also raise other questions like, “Why have the Yankees been so popular for so long?” Or “Why does ‘Mr’ appear more frequently than ‘Mrs’ in Jane Austen’s novels?”.

Learn:

  • Learn how computers can assist in analyzing literary texts
  • Practice using different tools for text analysis
  • Learn how to compare and distinguish between different types of software-driven analysis
  • Practice applying insights gained through software-driven analysis within a critical interpretation or analysis of a text

Build:

  • Use Voyant Tools to generate visualizations of a novel you know well, then use it on a novel you haven’t read. Compare your understanding of the results.
  • Use Voyant to develop a new argument about a work you know well.
  • Use topic modeling to find themes across a number of works by your favorite author.

Resources:

Tools and Data Sets:

 

Digital Journalism

It’s a complex world and we need complex tools to make sense of it. In this module, your team will identify a topic or pose a question, investigate that question, and tell the story you discover using a confluence of modalities and digital tools.

A recent piece breaks down new digital journalism formats into 12 categories:

  1. vertical video; often with captions, pioneered by AJ+ and NowThis
  2. Horizontal *Stories; swipeable cards like Snapchat Stories and its clones
  3. Longform scrollytelling; evolved from the original NY Times Snowfall
  4. Structured news; like the original Circa or the reusable cards at Vox.com
  5. Live blogs; frequently used for big events
  6. Listicles; like Buzzfeed
  7. Newsletters and briefings; which seem to be on trend right now
  8. Timelines; which I expected to be more common
  9. Bots and chat; from the chat-styled Qz app to the many attempts to deliver news within chat apps
  10. Personalised; which typically is used to filter the choice of stories, rather than the story itself
  11. Data visualisation; from graphs to interactives
  12. VR and AR

Goals

  • Learn about the historical relationship between journalism and media technologies (print, radio, TV)
  • Learn how the Internet has made new modalities of communication possible
  • Learn how digital tools make new kinds of research possible.

Resources

Suggested Tasks

  • Investigate some topic or question using a digital tool like OpenRefine, Kumu or even one of the APIs at the Huffington Post.
  • Learn about the history of converging media and modalities within journalism, starting with different technologies for including images alongside print (lithography, color photography, etc.)
  • Create your own multimedia, multi-modal journalistic piece telling the story of the question or topic you investigated

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