A literature review of "lawful" text and data mining.

Abstract

Text and data mining (TDM) is a process, typically automated, that looks for patterns in data that may otherwise remain unnoticed. In a world where data driven solutions play a progressively more important role, TDM has become a vital tool in sectors ranging from medicine, to commerce, gaining widespread attraction. Nevertheless, a variety of regulatory frameworks not always specifically attuned towards regulating TDM continue to apply concurrently. The literature within the context of regulatory frameworks governing TDM is a fragmented piecemeal of valuable insights into what "lawful" TDM resembles. This literature review adopts a grounded theory approach analysing 88 pieces of literature, collating views regarding "lawful" TDM, ultimately providing a holistic assessment of academics' and practitioners' views and opinions regarding the regulatory framework which governs TDM. A total of 7 categories were identified and each of these are analysed. Tables are provided in the Appendix (accessible here: https://doi.org/10.5281/zenodo.12654691)highlighting which scholarly works were used for each section of the literature review, but also how those works were used. It is ultimately concluded that the regulatory frameworks that apply to users conducting TDM are multifaceted, and ever-changing on a case-by-case basis. There is an ever-growing need for a holistic interpretation of the regulatory frameworks which apply, creating a map which would allow for users conducting TDM to navigate this complex web of legal rules.