New digital tools and methods are changing the way scholars in the humanities read, research, and interpret text-based communicative forms. Digitization and optical character recognition (OCR) have made a preponderance of print materials web accessible, while new platforms have created a flood of text-based, born digital materials. This course engages critically with the epistemological implications of digital humanities text analysis, especially the impact of distant reading on contemporary literary studies. Topics for discussion will include how quantitative research structures (prejudices?) research questions, the interpretive limits of quantitative text analysis, the ethics of algorithmic inquiry, and how the emerging “cultural analytics” subfield engages power relations and historical and social constructions of identity.
In particular, the course will critically examine the social construction of women and gender, as well as the cultural assumptions about these identities that are intertwined with computational text analysis. Susan Brown and Laura Mandell, writing for the Journal of Cultural Analytics, recently identified gender as a crucial topic for the field, in part because, “Almost all of the work in quantitative computational approaches to identity in relation to literature has focused on gender.”  Further, “debate amongst feminist scholars in the digital humanities … about the gendered discourse of text mining and its value as a mode of inquiry” has rendered conceptions of gender “inextricable from an evaluation of cultural analytics as a field.”
The course will also attempt to prepare students to use digital humanities methods to pursue their own research questions by anchoring theoretical and methodological readings with a capstone, computational project that encourages students to bring their own disciplines’ text-based materials into their work for the course. To this end, we will work closely with the Python programming language to better understand the iterative processes and scholarly decisions one must make when writing code in the humanities. Students will not be evaluated by their programming skill; however, willingness to engage in hands-on work is essential. Scholarly readings will draw from multiple disciplines to backstop theoretical, methodological, and hands-on concerns.
 Brown, Susan, and Laura Mandell. “The Identity Issue,” Journal of Cultural Analytics (2 February 2018): http://culturalanalytics.org