Ethnography and Machine Learning: Synergies and New Directions

TL;DR

This paper explores how ethnography and machine learning can be integrated to enhance large-scale social science research, discussing benefits, challenges, methodologies, and future directions for their combined use.

Contribution

It provides a comprehensive analysis of integrating ethnography with machine learning, including recent methodological trends, practical workflow examples, and a roadmap for future collaboration.

Findings

Combining ethnography and machine learning offers new insights for large comparative studies.

Methodological trends support the coevolution of qualitative and computational methods.

Practical workflows demonstrate effective integration in real projects.

Abstract

Ethnography (social scientific methods that illuminate how people understand, navigate and shape the real world contexts in which they live their lives) and machine learning (computational techniques that use big data and statistical learning models to perform quantifiable tasks) are each core to contemporary social science. Yet these tools have remained largely separate in practice. This chapter draws on a growing body of scholarship that argues that ethnography and machine learning can be usefully combined, particularly for large comparative studies. Specifically, this paper (a) explains the value (and challenges) of using machine learning alongside qualitative field research for certain types of projects, (b) discusses recent methodological trends to this effect, (c) provides examples that illustrate workflow drawn from several large projects, and (d) concludes with a roadmap for enabling productive coevolution of field methods and machine learning.