Short abstract:

To analyze digital practices, we suggest a hybrid research methodology that merges computational data harvesting and filtering with case study analysis. This approach enables the use of the extensive online data while facilitating in-depth examination of the gathered information.

Long abstract:

The rise of digital technology has opened new avenues for research, particularly by harnessing the extensive power of computational methods to analyze large datasets. However, a purely computational research method raises various issues. Millerand, Myles, and Proulx (2020, note, for instance, that works relying on computational methods tend to adopt a positivist view, asserting that the analysis of massive data would allow for a more objective analysis and representation of reality. On the other hand, these authors also note that the use of traditional and more qualitative methods to study digital practices encounters limitations when faced with large data corpora. They thus suggest the relevance of combining elements from several categories of research methods.

Following these reflections, we present a “mixed methodology” that combines computational practices, such as data harvesting and filtering, with more traditional research practices, namely case studies. We start by summarizing the work of Millette et al. (2020), following Marres (2012), who divide four categories of methodologies to study digital practices. We then present the mixed methodology we developed for a study of digital practices in support of the Black Lives Matter movement. Finally, we discuss the advantages and issues that such a mixed methodology may raise for subsequent research works. We argue in this presentation that implementing such a methodology for analysis allows, on one hand, access to the vast quantity of digital data thanks to computational practices, while maintaining the scope of analyses associated with qualitative data analysis.