Spam four ways: Making sense of text data

Nicholas J. Horton; Jie Chao; William Finzer; Phebe Palmer

doi:10.1080/09332480.2022.2066414

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arXiv · 2202.07389

Spam four ways: Making sense of text data

Nicholas J. Horton·Jie Chao·William Finzer·Phebe Palmer

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Abstract

The world is full of text data, yet text analytics has not traditionally played a large part in statistics education. We consider four different ways to provide students with opportunities to explore whether email messages are unwanted correspondence (spam). Text from subject lines are used to identify features that can be used in classification. The approaches include use of a Model Eliciting Activity, exploration with CODAP, modeling with a specially designed Shiny app, and coding more sophisticated analyses using R. The approaches vary in their use of technology and code but all share the common goal of using data to make better decisions and assessment of the accuracy of those decisions.

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Nicholas J. Horton, Jie Chao, William Finzer, Phebe Palmer. 2022-02-11. Spam four ways: Making sense of text data. https://doi.org/10.1080/09332480.2022.2066414

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