This project explores the potential epistemic effects of ‘big data’ in the social sciences in two ways. First, through a case study that explores how scholars from diverse disciplines use geotagged social media data - such as geotagged tweets, Instagram posts and Foursquare content – in scientific research. Second, ‘in action’, through combining ethnographic fieldwork and interviews with digital, computational data analysis. This project addresses two gaps in the Science and Technology Studies (STS). First, it can help better understand ‘digital’ social scientific scholarship enabled by ‘big data’ analysis [cf. 1]. Relatively few STS studies explore the use of digital data in scientific research  and most existing studies focus on the natural sciences (cf. ; e.g. [4, 5]) with a few exceptions, such as  and . This project’s focus on geotagged social media data practices can provide an insight into a nascent research speciality within ‘computational social science’  or ‘digital social science’ . Second, in line with recent calls, it explores the consequences of embedding digital, scientometric data analysis in STS scholarship [2, 10].
Research Question 1: How do scholars from diverse disciplines work with geotagged social media data? Can we identify similarities and differences?
Research Question 2: How can we combine scientometric methods with concepts from STS?
Research Question 3: How are places made and unmade through geotagged social media research?
 Woolgar, S., & Coopmans, C. (2006). Virtual Witnessing in a Virtual Age: A Prospectus for Social Studies of E-Science. In C. Hine (Ed.), New Infrastructures for Knowledge Production: Understanding E-Science. Information Science Publishing.
 Cambrosio, A., Bourret, P., Rabeharisoa, V., & Callon, M. (2014). Big Data and the Collective Turn in Biomedicine How Should We Analyze Post-genomic Practices? TECNOSCIENZA Italian Journal of Science & Technology Studies, 5(1), 11–42.
 Fry, J. (2006). Coordination and Control of Research Practice across Scientific Fields: Implications for a Differentiated E-Science. In C. Hine (Ed.), New Infrastructures for Knowledge Production: Understanding E-Science (p. 167-). Information Science Publishing.
 Leonelli, S. (2016). Data-Centric Biology: A Philosophical Study. University of Chicago Press.
 Edwards, P. N. (2010). A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming. MIT Press.
 Wouters, P., Beaulieu, A., Scharnhorst, A., & Wyatt, S. (Eds.). (2012). Virtual Knowledge: Experimenting in the Humanities and the Social Sciences. MIT Press.
 Plantin, J.-C., & Russo, F. (2016). D’abord les données, ensuite la méthode? Big data et déterminisme en sciences sociales. Socio, 97–115.
 Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., … Alstyne, M. Van. (2009). Computational Social Science. Science, 323(5915), 721–723. http://doi.org/10.1126/science.1167742  Marres, N. (2017). Digital Sociology: The Reinvention of Social Research. Polity Press.
 Wyatt, S., Milojević, S., Park, H. W., & Leydesdorff, L. (2017). Intellectual and Practical Contributions of Scientometrics to STS. In U. Felt, R. Fouché, C. A. Miller, & L. Smith-Doerr (Eds.), The Handbook of Science and Technology Studies (Fourth Edi, pp. 87–112). MIT Press Cambridge, Massachusetts London, England.
This author is supported by the Horizon Centre for Doctoral Training at the University of Nottingham (RCUK Grant No. EP/L015463/1) and Ordnance Survey.