While often commonly positioned at the intersection of computer science and digital humanities, computational humanities engages with other fields including data science, (computational) linguistics, and statistics.Such a transdisciplinary approach creates "a digital ecology of data, algorithms, metadata, analytical and visualization tools, and new forms of scholarly expression that result from this research," as Christa Williford and Charles Henry, of the Council on Library and Information Resources, write. Text analysis, particularly the method of topic modeling, has enjoyed broad exposure within computational humanities. Given the scale of the corpus, computational methods were used to identify reprinted texts in 41,829 issues.The goal of the project "is not to construct a definitive, empirical solution to the problem of nineteenth-century newspaper reprinting," Cordell writes, "but to facilitate an iterative conversation between the large-scale, quantitative output generated by a corpus analysis algorithm and qualitative, literary-historical readings of the surprising texts that algorithm brings into focus. Suggesting a shift from distant reading, the project is part of a growing chorus of such work that argues that DH needs to expand beyond text to other forms such as photography and moving images, a shift that American studies has also called for. DV, therefore, focuses on how a critical use of computer vision can be used to analyze moving image culture. Since the majority of the algorithms are trained on twenty-first-century data held by companies like Google and platforms like Flickr, we need to question and adapt these algorithms using machine learning informed by our areas of inquiry.

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Copyright © 2018 Johns Hopkins University Press. Article first published online: September 2018.

DOI: 10.1353/aq.2018.0046

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Tilton, Lauren. "American Studies + Computational Humanities." American Quarterly 70, no. 3 (September 2018): 633-639.