Geocaching: tracing geotagged social media research using mixed methods

This thesis explores the development of academic research with geotagged social media data (geosocial research) - an emerging computational, digital social research field - using 19 semi-structured interviews with scholars from diverse disciplines, participant observation at a geosocial research summer school and scientometrics. It asks: 'how can we study the development of geosocial research approaches through combining STS and scientometrics?' for five main reasons: to explore the diversity of computational social research; reflect on the ESRC's (2013) call to 'close the gap' between quantitative and qualitative human geography; contribute to methodological discussions in academic literature which call for combining STS and scientometrics; co-compose knowledge with distinct ways of knowing through mixing methods; and inform research methods curriculum development in the social sciences. Using new forms of digital data (like social media posts) is core to contemporary social science. Scholars from diverse disciplines conduct geosocial research. It thus provides rich opportunities to study how diverse approaches to computational social research develop. I combine STS and diverse scientometric methods as part of a single case study iteratively to explore how they can co-compose knowledge. The thesis contributes to literature which explores the STS - scientometrics interface. Most existing studies either reflect on diverse mixed methods approaches from theoretical or methodological perspectives, or provide worked examples using specific mixed methods designs. Conceptually, this thesis contributes by highlighting the need to develop and evaluate the affordances of computational methods for STS in light of the interpretative context - including research questions, characteristics of the studied research practice, theories and prior findings. I developed computational methods iteratively, in light of my theoretical and empirical knowledge about geosocial research. Empirically, the thesis first contributes by showing how diverse combinations of STS and scientometrics – including statistical and visual network analyses as well as descriptive statistics - can inform a single case study. Second, it offers three ways STS and scientometrics can co-compose knowledge by aligning their units of analyses, reflecting on how calculation acts inform qualitative analysis even when analytical units are not aligned, and using each method inductively. I combined STS and scientometrics to study practices through which geosocial research approaches develop - including collaboration, developing (sub)-disciplinary communities and methods' mediation of geosocial research. I also identified geosocial research approaches and compared them using mixed methods. Finally, I combined insights from STS and scientometrics to highlight the construction of my own analyses. Using mixed methods, the thesis argues that geosocial research is a collection of approaches rather than a coordinated community. I highlight fourteen practices that enable scholars to develop their approaches, including interdisciplinary collaboration; setting up distinct geosocial laboratories to experiment with geosocial data; reflecting on the data analysis process; and using local knowledge about spaces. I differentiate `social', `technical' and 'geographic' approaches, which differ in terms of the methods they use and spatial units they study. Finally, I illustrate approaches' heterogeneity - including their diverse computational approaches - and similarities, such as their urban studies focus