This article explores the use of computational methods to study stylistic and content shifts of the nineteenth-century short story. It is generally assumed that the modern American short story somehow represents the democratic discourse of the United States. This paper argues that an explorative computational approach can help us to reconsider the connections between the textual patterns of the short story and U.S.-American modernity. We offer a critical, digital perspective on the distribution of certain indicative linguistic features across 123 short stories from 1840 to 1916. We used methods from computational linguistics to automatically annotate the texts with various linguistic properties (named entities and direct speech, for instance). The quantitative results of automated text processing are presented against the backdrop of the major social, economic, and cultural developments of the time. Our findings provide further insights on the tensions between processes of individualization and economic dependencies during the nineteenth century, especially with respect to the publishing industry. In addition, we propose that the more experimental nature of a macro-analytical perspective can direct our attention to texts or groups of texts that remain underestimated as to their literary value or exemplary nature for a certain topical, structural, or linguistic pattern. In this vein, the article offers a close reading of Thomas Bailey Aldrich’s short story “Mademoiselle Olympe Zabriski,” which features an above-average number of proper names within the corpus. When re-read in the light of quantitative results, we see the text comment humorously on 1870s class issues in New England. Thus, the article offers an example of how humanities research can benefit from quantitative approaches. To conclude, the article promotes the gains of digital literary analysis for future research in American Studies.