  A common task in computational text analyses is to quantify how two corpora
differ according to a measurement like word frequency, sentiment, or
information content. However, collapsing the texts' rich stories into a single
number is often conceptually perilous, and it is difficult to confidently
interpret interesting or unexpected textual patterns without looming concerns
about data artifacts or measurement validity. To better capture fine-grained
differences between texts, we introduce generalized word shift graphs,
visualizations which yield a meaningful and interpretable summary of how
individual words contribute to the variation between two texts for any measure
that can be formulated as a weighted average. We show that this framework
naturally encompasses many of the most commonly used approaches for comparing
texts, including relative frequencies, dictionary scores, and entropy-based
measures like the Kullback-Leibler and Jensen-Shannon divergences. Through
several case studies, we demonstrate how generalized word shift graphs can be
flexibly applied across domains for diagnostic investigation, hypothesis
generation, and substantive interpretation. By providing a detailed lens into
textual shifts between corpora, generalized word shift graphs help
computational social scientists, digital humanists, and other text analysis
practitioners fashion more robust scientific narratives.
