Digital text has revolutionized how we consume and produce information, and also provides seemingly limitless sources of data from Twitter feeds to online historical archives. Such new data challenge traditional boundaries between quantitative and qualitative research, and exciting horizons have emerged. New analytic approaches are warranted, however, given the typically unstructured, respondent-generated format of such data. In this article, I examine how sociologists have handled text data prior to digitization. Building on recent advancements in computational linguistics and computational social science, I then offer a network-based model and approach for analyzing similarities and locating emergent, general themes in digitized text. I provide a case in point by analyzing the United States’ presidential inaugural addresses. This analysis illustrates how sociologists can take advantage of both the breadth of new digital sources of data and the richness that such qualitative material provides. Indeed, the digitization of texts represents a possible and stimulating sea change in how we tell socio-cultural and historical stories. The greatest potential in these regards rests at the nexus of new computational methods and in-depth, qualitative strategies.
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