Big data gets big help: Law and policy literacies for text data mining

A wealth of digital texts and the proliferation of automated research methodologies enable researchers to analyze large sets of data at a speed that would be impossible to achieve through manual review. When researchers use these automated techniques and methods for identifying, extracting, and analyzing patterns, trends, and relation- ships across large volumes of un- or thinly structured digital content, they are applying a methodology called text data mining or TDM. TDM is also referred to, with slightly different emphases, as “computational text analysis” or “content mining.” The “distant reading” that TDM makes possible supports the discovery of scientific and social insights, such as how gender is depicted in fiction over time or evidence of racial disparity in police camera footage. Libraries are eager to provide and expand institutional access to data sets so that scholars can continue exploring unknown connections, yet both scholars and professional staff who support TDM research often run into roadblocks. Law and policy questions are paramount and shape not only how TDM scholarship is disseminated, but also the very questions being asked. If researchers are limited to corpora unencumbered by legal restrictions, they risk perpetuating bias in the scholarly record. With a basic set of law and policy literacies in hand, libraries can help scholars navigate these issues so that they can confidently use, create, and share a far wider set of corpora and research results