This study reveals that the entry into World War I in 1917 indexed the decisive transition to the modern period in American political consciousness, ushering in new objects of political discourse, a more rapid pace of change of those objects, and a fundamental reframing of the main tasks of governance. We develop a strategy for identifying meaningful categories in textual corpora that span long historic durées, where terms, concepts, and language use changes. Our approach is able to account for the fluidity of discursive categories over time, and to analyze their continuity by identifying the discursive stream as the object of interest.State of the Union | text analysis | networks | natural language processing |

Early in 2008 – before the economy started acting up—the New York Times published a special issue of its Sunday magazine—the Money Issue, though it might equally well have been called the "Good" Issue. Its cover package touted various strategies for transforming money into social good—and in some serendipitous cases, back into money! The pièce de résistance was an article by Jon Gertner—"For Good Measure"—surveying contemporary practices of philanthropic foundations.

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We introduce a novel task, that of associating relative time with cities in text. We show that the task can be performed using NLP tools and techniques. The task is deployed on a large corpus of data to study a specific phenomenon, namely the temporal dimension of contemporary arts globalization over the first decade of the 21 st century.