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.

中文翻译：

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垃圾邮件的四种方式：理解文本数据

世界充满了文本数据，但文本分析传统上并没有在统计教育中发挥重要作用。我们考虑了四种不同的方式，让学生有机会探索电子邮件是否是不需要的信件（垃圾邮件）。主题行中的文本用于识别可用于分类的特征。这些方法包括使用模型引发活动、使用 CODAP 进行探索、使用专门设计的 Shiny 应用程序建模以及使用 R 编写更复杂的分析。这些方法在技术和代码的使用方面各不相同，但都有一个共同的目标，即使用数据做出更好的决策并评估这些决策的准确性。