  Text provides a compelling example of unstructured data that can be used to
motivate and explore classification problems. Challenges arise regarding the
representation of features of text and student linkage between text
representations as character strings and identification of features that embed
connections with underlying phenomena. In order to observe how students reason
with text data in scenarios designed to elicit certain aspects of the domain,
we employed a task-based interview method using a structured protocol with six
pairs of undergraduate students. Our goal was to shed light on students'
understanding of text as data using a motivating task to classify headlines as
"clickbait" or "news". Three types of features (function, content, and form)
surfaced, the majority from the first scenario. Our analysis of the interviews
indicates that this sequence of activities engaged the participants in thinking
at both the human-perception level and the computer-extraction level and
conceptualizing connections between them.
