  Qualitative thematic exploration of data by hand does not scale and
researchers create and update a personalized point of view as they explore
data. As a result, machine learning (ML) approaches that might help with
exploration are challenging to apply. We developed Teleoscope, a web-based
system that supports interactive exploration of large corpora (100K-1M) of
short documents (1-3 paragraphs). Teleoscope provides visual programming
workflows that have semantic and computational meaning; helping researchers to
retrace, share, and recompute their sense-making process. Attempting to create
qualitative "themes" rather than "topics," our NLP approach tunes an ML model
to "think like you" without significant retraining. Here, we present our
two-year design process and validation of Teleoscope, including a multi-week
study with qualitative researchers (N = 5), a six-month field deployment with a
qualitative research group, and an on-going public release.
