ABSTRACT
This chapter provides analytical frameworks for digital case study analysis. It begins with concepts for beginning data analysis, such as atomization, structures, and attention, before moving on to workflow usage. After laying out workflow habits, it describes a variety of analytical techniques, including qualitative techniques such as interface analysis and the integration of texts, images, and emojis. It then discusses quantitative, computational techniques for generative thematizing, including topic modeling, named entity recognition, and sentiment analysis. It touches on generative machine learning and analysis as a type of ethics. This chapter aims to provide a non-exhaustive framework for digital case study analysis to help researchers make sense of their data and develop meaningful interpretations from messy data.
