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This series of blog posts provides an extremely accessible and clear introduction to networks and graph theory. They are written by someone with a strong background in digital humanities. Start with the first post, and then check out the other 8 (or so posts) in the series here and here.

Topic Modeling

Each of the articles below provides an introduction to topic models from a somewhat different perspective and for a somewhat different audience. Don't feel obliged to read all of them in detail. Instead, take a brief look at each, and try to find one or two that seem especially accessible to you. Some will prefer a more technical explanation, others may find a more abstract explanation to be preferable.

David M. Blei. 2012. "Probabilistic Topic Models." Communications of the ACM 55(4):77-84.

Edwin Chen. "Introduction to Latent Dirichlet Allocation."

Ted Underwood. "Topic modeling made just simple enough."

Scott Weingart. "Topic modeling and network analysis."

Clay Templeton. "Topic modeling in the humanities: an overview."

Matthew Jockers. "The LDA buffet is now open; or, Latent Dirichlet Allocation for English majors."

Allen Beye Riddell. "A simply topic model (mixture of unigrams)." (this one is a bit more technical)