- 🚨 Watch this video tutorial! (this is technical info needed for the examples). Of course if you alreaddy know this material, you can skip.
- 🔢 This is found in a group, maybe pick just one to check out!
- 🍿 Additional video if you have a particular interest and want to do a deeper dive.
- 📕 Required reading! Let's make sure we all have read this.
- 📚 Optional additional reading for a deeper dive.
- 💻 Code examples here!
- 🔗 Extra reference material / link
- 🚨 Eyeo 2014 Ignite - Sarah Groff-Palermo
- 🍿 The Secret Life of Pronouns: James Pennebaker at TEDxAustin
- 🍿 Overview of Word Counting + Text Analysis
- 🚨 Associative Arrays in JavaScript
- 🚨 Word Counting in JavaScript
- 💻 Additional p5.js word counting visualization
- 💻 p5.js word counting two documents visualization
- 🔗 SPEECH COMPARISON by Rune Madsen
- 🔗 Book-Book by Sarah Groff-Palermo
- 🔗 Word Tree by Martin Wattenberg and Fernanda Viegas
- 🔗 Entangled Word Bank by Stephanie Posavec
- 🔗 Annual Report 2013 by Nicholas Feltron
- 🔗 Partisan Thesauras by Melanie Hoff
- 🍿 TF-IDF Video Tutorial
- 🍿 Logarithmic scale | Logarithms by Khan Academy
- 🔗 TF-IDF Single Page Tutorial
- 📚 A Plan for Spam by Paul Graham
- 🍿 Explaining Bayesian Problems Using Visualizations by Luana Micallef
- 💻 Natural, a general natural language facility for nodejs with a built-in Bayes Classifier
- 💻 Sample start of Bayesian Classification Library
Part of the challenge of understanding algorithmic oppression is to understand that mathematical formulations to drive automated decisions are made by human beings. While we often think of terms such as “big data” and “algorithms” as being benign, neutral, or objective, they are anything but.
- 🚨 Algorithms of Oppression: How Search Engines Reinforce Racism, Chapter 1: A Society Searching, by Safiya Umoja Noble,
Choose a text or data source and count word frequencies following the examples above. Design your own creative output. This need not be visual (sonify word counts?) nor does it require code (knit your own word frequency scarf! Some things to consider:
- Use a language other than English!
- What happens if you compare different texts according to word frequency?
- Can you look at frequency of how words appear next to each other?
Reflect on your process of word counting and consider the following questions in a blog post:
- Did you discover anything new about the text by counting words?
- What is lost from word counting?
- Challenge the assumption that algorithms for analyzing text (such as word counting) are neutral.
(Please note you are welcome to post under a pseudonym and/or password protect your published assignment. Here is some helpful information on privacy options for an NYU blog. Finally, if you prefer not to post your assignment at all here, you may email the submission.)
- Julie Lizardo -- 2020 Presidential Debate Analysis
- KJ Ha -- Lyrics Visualizer
- Martin Martin -- The Syllabi and by – Fall 2020
- Beste Saylar -- Word Cloud
- Helen Zegarra --Falling words
- Tracey Shi -- winnie the pooh
- Minyoung Bang -- Rected Words
- DonNan Dai -- Word Explore
- Brandon Roots -- (Presidential)? Debate
- Tianxu Zhou -- Amazon Review
- Zeyao Li -- Love Words
- Youming Zhang -- Word Count App
- Lynne Yun -- Exploring Word Count
- Simone Salvo -- Lost in Transcription
- Jan Suphitcha -- Word Counting
- Elizabeth Pérez -- Why is word counting a thing
- Ahmad Arshad -- Arabic Word Counting