William E Underwood

Abstract: This question of disciplinary meaning—which I ask from the viewpoint of the humanities generally—is larger than the question of disciplinary identity now preoccupying “DH” itself, as insiders call it. Having reached a critical mass of participants, publications, conferences, grant competitions, institutionalization (centers, programs, and advertised jobs), and general visibility, the field is vigorously forming an identity. Recent debates about whether the digital humanities are a “big tent” (Jockers and Worthey), “who's in and who's out?” (Ramsay), whether “you have to know how to code [or be a builder]” (Ramsay, “On Building”), the need for “more hack, less yack” (Cecire, “When Digital Humanities”; Koh), and “who you calling untheoretical?” (Bauer) witness a dialectics of inclusion and exclusion not unlike that of past emergent fields. An ethnographer of the field, indeed, might take a page from Claude Levi-Strauss and chart the current digital humanities as something like a grid of affiliations and differences between neighboring tribes. Exaggerating the differences somewhat, as when a tribe boasts its uniqueness, we can thus say that the digital humanities—much of which affiliates with older humanities disciplines such as literature, history, classics, and the languages; with the remediation of older media such as books and libraries; and ultimately with the value of the old itself (history, archives, the curatorial mission)—are not the tribe of “new media studies,” under the sway of the design, visual, and media arts; Continental theory; cultural criticism; and the avant-garde new. Similarly, despite significant trends toward networked and multimodal work spanning social, visual, aural, and haptic media, much of the digital humanities focuses on documents and texts in a way that distinguishes the field's work from digital research in media studies, communication studies, information studies, and sociology. And the digital humanities are exploring new repertoires of interpretive or expressive “algorithmic criticism” (the “second wave” of the digital humanities proclaimed in “The Digital Humanities Manifesto 2.0” [3]) in a way that makes the field not even its earlier self, “humanities computing,” alleged to have had narrower technical and service-oriented aims. Recently, the digital humanities' limited engagement with identity and social-justice issues has also been seen to be a differentiating trait—for example, by the vibrant #transformDH collective, which worries that the digital humanities (unlike some areas of new media studies) are dominantly not concerned with race, gender, alternative sexualities, or disability.

Abstract: Search in an environment with an uncertain distribution of resources involves a trade-off between exploitation of past discoveries and further exploration. This extends to information foraging, where a knowledge-seeker shifts between reading in depth and studying new domains. To study this decision-making process, we examine the reading choices made by one of the most celebrated scientists of the modern era: Charles Darwin. From the full-text of books listed in his chronologically-organized reading journals, we generate topic models to quantify his local (text-to-text) and global (text-to-past) reading decisions using Kullback-Liebler Divergence, a cognitively-validated, information-theoretic measure of relative surprise. Rather than a pattern of surprise-minimization, corresponding to a pure exploitation strategy, Darwin's behavior shifts from early exploitation to later exploration, seeking unusually high levels of cognitive surprise relative to previous eras. These shifts, detected by an unsupervised Bayesian model, correlate with major intellectual epochs of his career as identified both by qualitative scholarship and Darwin's own self-commentary. Our methods allow us to compare his consumption of texts with their publication order. We find Darwin's consumption more exploratory than the culture's production, suggesting that underneath gradual societal changes are the explorations of individual synthesis and discovery. Our quantitative methods advance the study of cognitive search through a framework for testing interactions between individual and collective behavior and between short- and long-term consumption choices. This novel application of topic modeling to characterize individual reading complements widespread studies of collective scientific behavior.

Abstract: In its first part, this article gives some illustrative insights into the spectrum of methods and model types from Computational Linguistics that one could in principle apply in the analysis of literary texts. The idea is to indicate the considerable potential that lies in a targeted refinement and extension of the analysis procedures, as they have been typically developed for newspaper texts and other everyday texts. The second part is a personal assessment of some key challenges for the integration of working practices from Computational Linguistics and Literary Studies, which ultimately leads to a plea for an approach that derives the validity of model-based empirical text analysis from the annotation of reference corpus data. This approach should make it possible, in perspective, to refine modeling techniques from Computational Linguistics in such a way that even complex hypotheses from Literary Theory can be addressed with differential, data-based experiments, which one should ideally be able to integrate into a hermeneutic argumentation.

Abstract: A history of literary prestige needs to study both works that achieved distinction and the mass of volumes from which they were distinguished. To understand how those patterns of preference changed across a century, we gathered two samples of English-language poetry from the period 1820–1919: one drawn from volumes reviewed in prominent periodicals and one selected at random from a large digital library (in which the majority of authors are relatively obscure). The stylistic differences associated with literary prominence turn out to be quite stable: a statistical model trained to distinguish reviewed from random volumes in any quarter of this century can make predictions almost as accurate about the rest of the period. The “poetic revolutions” described by many histories are not visible in this model; instead, there is a steady tendency for new volumes of poetry to change by slightly exaggerating certain features that defined prestige in the recent past.

Abstract: Scholars of Chinese literature note that China’s tumultuous literary history in the 20th century centered around the uncomfortable tensions between tradition and modernity. In this corpus study, we develop and automatically extract three features to show that the classical character of Chinese poetry decreased across the century. We also find that Taiwan poets constitute a surprising exception to the trend, demonstrating an unusually strong connection to classical diction in their work as late as the ’50s and ’60s.