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A common task in computational text analyses is to quantify how two corporadiffer according to a measurement like word frequency, sentiment, orinformation content. However, collapsing the texts' rich stories into a singlenumber is often conceptually perilous, and it is difficult to confidentlyinterpret interesting or unexpected textual patterns without looming concernsabout data artifacts or measurement validity. To better capture fine-graineddifferences between texts, we introduce generalized word shift graphs,visualizations which yield a meaningful and interpretable summary of howindividual words contribute to the variation between two texts for any measurethat can be formulated as a weighted average. We show that this frameworknaturally encompasses many of the most commonly used approaches for comparingtexts, including relative frequencies, dictionary scores, and entropy-basedmeasures like the Kullback-Leibler and Jensen-Shannon divergences. Throughseveral case studies, we demonstrate how generalized word shift graphs can beflexibly applied across domains for diagnostic investigation, hypothesisgeneration, and substantive interpretation. By providing a detailed lens intotextual shifts between corpora, generalized word shift graphs helpcomputational social scientists, digital humanists, and other text analysispractitioners fashion more robust scientific narratives.

Generalized Word Shift Graphs: A Method for Visualizing and Explaining Pairwise Comparisons Between Texts