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at the Harvard Metalab: metalab.harvard.edu/2012/07/paper-machines/
"Below is a screenshot from the “topic” view of our application, showing the output of a topic model analysis. Such an analysis assumes that texts are comprised of various collections of words (“topics”) according to a probability distribution; the algorithm infers which words make up which topics and which topics make up which documents based solely on word co-occurrence, without prior semantic knowledge. These topics are machine-generated, but often reveal a surprising coherence; for example, the “estate, tenant, law” topic above also includes “property, right, rent, lease, heir, inclosure” and so on. The topics below give a sense of some of the changing concerns in two subsets of the corpus:"
"My own research as a Ph.D student at Brown focuses on the intertwining of technology with race, gender, and sexuality in contemporary African-American music and online discourse on sites like Twitter, Tumblr, Okayplayer and Rapgenius. While my subject is superficially quite different from Jo’s, I believe this tool will help me to condense the massive corpora of these online fora into more manageable forms. I am excited to see what other uses may emerge from this project once it is released to the broader community."
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