Abstract

Digital humanities research has developed haphazardly, with substantive contributions in some disciplines and only superficial uses in others. It has made almost no inroads in philosophy; for example, of the nearly two million articles, chapters, and books housed at philpapers.org, only sixteen pop up when one searches for ‘digital humanities’. In order to make progress in this field, we demonstrate that a hypothesis-driven method, applied by experts in data-collection, -aggregation, -analysis, and -visualization, yields philosophical fruits. “Call no one happy until they are dead.” “De mortuis nil nisi bonum.” These ancient norms still inform how we speak of the dead. From their beginnings in the nineteenth century, modern obituaries have been both practical and honorary. They are written with two aims: to pay respect to the deceased and to inspire the living to follow in their footsteps. The obituary of William Custis of Virginia exemplifies these aims, say that “it is due to his memory publicly to record his virtues,” and that “There is in the life a noble, independent and honest man, something so worthy of imitation, something that so strongly commends itself to the approbation of the virtuous mind, that his name should not be left to oblivion” (Daily National Intelligencer, 18 November 1838). Likewise, philosophers have emphasized that we can determine what counts as a virtue for a given type of person in a given community by analyzing what people say about the dead (Zagzebski, L. Virtues of the Mind. Cambridge University Press, 1996, p. 135). By adhering to norms of praise, admiration, and respect, obituaries reveal what counts as a virtue, value, or constituent of wellbeing (VVC) for a particular type of person in a particular community. One way to explore this insight would be to close-read a small number of obituaries of people from intersecting demographics, crossing dimensions such as gender, age, race, class, educational attainment, veteran status, religion, and location. Another, which we demonstrate in this chapter, is to harness digital humanities resources to distance-read a much larger number of obituaries, extracting both demographic meta-data and first-order content expressive of VVC. We then analyze and visualize this information to establish the structures of VVC associated with different types of people in different communities, arguing that data-science measures such as eigenvector centrality can be used to identify cardinal virtues. We also explain the rudiments of our text- and meta-data mining, our method of aggregating and comparing texts, and our visualization techniques. In so doing, we pave the way for researchers in philosophy and related fields to generate similar analyses and visualizations.