Using Deep Learning Language Models as Scaffolding Tools in Interpretive Research

Abstract

Objective: the paper introduces a framework for conducting interpretive research using deep learning algorithms that blur the boundaries between qualitative and quantitative approaches. The work evidences how research might benefit from an integrated approach that uses computational tools to overcome traditional limitations. Proposal: the increased availability and diversity of data raises the utility of algorithms as research tools for social scientists. Furthermore, tuning and using such computational artifacts may benefit from interpretive procedures. Such circumstances turn the traditional debate between quantitative and qualitative research on its head: the research strategy that likely yields the most assertiveness and rigor is the one that may require vigorous hermeneutic effort. Along these lines, neural word embeddings can be instrumental in allowing researchers to read the data closely before and after interpretation. Conclusions: to take advantage of the opportunity generated by these new algorithms, researchers may broaden their previous conceptions and adopt a participative point of view. In the coming decades, the interweaving of computational and interpretive methods has the potential to integrate rigorous social science research.