Statistical topic modeling is an increasingly popular approach to text
analysis. Many existing visualization tools focus on analyzing the model
itself, distinct from the documents upon which it was trained. In contrast,
we seek to treat the model as a lens through which to view the original
documents. This would enable the reader to observe trends and build
hypotheses at multiple scales--ranging from across a corpus to within a
single text--and find both algorithmic data and textual examples to defend
these hypotheses. Supporting this workflow requires a multi-tiered framework
that affords comparisons at three levels: the entire corpus, small sets of
documents, and a single document. This framework is embodied in Serendip, a
web-application that combines view-coordinated reorderable matrices, small
multiples displays, and tagged text in order to allow readers to develop
insight at and across multiple levels.