Data Mining Research with In-copyright and Use-limited Text Datasets: Preliminary Findings from a Systematic Literature Review and Stakeholder Interviews

Abstract

Text data mining and analysis has emerged as a viable research method for scholars, following the growth of mass digitization, digital publishing, and scholarly interest in data re-use. Yet the texts that comprise datasets for analysis are frequently protected by copyright or other intellectual property rights that limit their access and use. This article discusses the role of libraries at the intersection of data mining and intellectual property, asserting that academic libraries are vital partners in enabling scholars to effectively incorporate text data mining into their research. We report on activities leading up to an IMLS-funded National Forum of stakeholders and discuss preliminary findings from a systematic literature review, as well as initial results of interviews with forum stakeholders. Emerging themes suggest the need for a multi-pronged distributed approach that includes a public campaign for building awareness and advocacy, development of best practice guides for library support services and training, and international efforts toward data standardization and copyright harmonization.

Whose text, whose mining, and to whose benefit?

“…Robots may or may not be allowed to search open access databases. Scholars and libraries are pressing for greater mining privileges of journals, books, and other intellectual resources (Lammey, 2014;Senseney, Dickson, et al, 2018;Van de Sompel, 2013;Van de Sompel, Rosenthal, & Nelson, 2016;Williams, Fox, et al, 2014).…”

“…Publishers, in turn, often claim that their contracts cover only "consumptive use" by human readers and that universities should pay additional fees for mining access. Complicating matters further, large text corpora may contain both public domain and copyrighted materials that are indistinguishable for mining purposes (Baldwin, 2014;Elkin-Koren, 2004;Elkin-Koren & Fischman-Afori, 2017;Levine, 2014;Senseney et al, 2018;Wilkin, 2017).…”

“…User interfaces to bibliographic databases provide minimal mining capabilities and may limit the number of records that can be downloaded. Researchers report missing records and a general lack of transparency in search results when they attempt to download files for TDM (Dickson, Senseney, et al, 2018;Senseney et al, 2018).…”

Prototype to Investigate the Extent to Which Words with Specific Attributes Can Be Retrieved Using Granular Metadata

“…The availability of resources in digital form, together with the developments in technology, has opened up new possibilities for researchers. New methods and techniques are employed to explore texts and text collections, for example text and data mining, distant reading and visualisation (e.g., Michel et al 2010;Nguyen et al 2020;Senseney et al 2021;Viiri 2014). Interesting studies using new techniques and methods have been done, for example, the investigation of cultural trends (Michel et al 2010), the mapping of emotions in London (Heuser, Moretti, and Steiner 2016) or investigating gender in English fiction (Underwood, Bamman, and Lee 2018).…”

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

“…If researchers are limited to corpora unencumbered by legal restrictions, they risk perpetuating bias in the scholarly record. 4 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. 5…”