David Moats is a Lecturer in Digital Methods at the Department of Digital Humanities. Specialising in Science and Technology Studies (STS), his research is mainly about digitization and the role of machine learning and artificial intelligence in transforming various industries including media, healthcare, politics and academia.

He is also interested in the methodological implications of these new sources of digital data for the humanities and social sciences. He regularly works with computer scientists and designers to develop new research tools and has written about the role of interdisciplinarity in digital research.

David completed his PhD at Goldsmiths, University of London, where he studied how science topics (specifically nuclear power) were debated on various online platforms including Wikipedia, Facebook and Twitter, which were seen to upset the relationship between experts and lay-publics. As part of this work, he developed methodologies for integrating qualitative methods with data visualisations and computational techniques.

David has held posts at Linköping University, Sweden and the University of Helsinki, Finalnd. In 2021 David edited a book about Imposters with Steve Woolgar, Else Vogel and CF Helgesson. He is currently working on a European project about the relationship between public values and algorithmic systems.

Research interests and PhD supervision 

• Social impacts of new (or old) technology
• Social studies of algorithms and AI
• Digital Social Research Methods (especially data visualizations)
• Public Engagement in Science
• Digital Democracy

Selected publications

• Woolgar, S; Vogel, E; Moats, D and Helgesson, CF (eds) 2021 The Imposter as Social Theory: Thinking with Gatecrashers, Cheats and Charletons Bristol: Bristol University Press
• Moats, D and Tseng, YS 2023 ‘Sorting a public? Using quali-quantitative methods to interrogate the role of algorithms in digital democracy platforms’ Information Communication & Society. DOI: 10.1080/1369118X.2023.2230286
• Moats, D 2020 'Rethinking the Great Divide: Approaching Interdisciplinary Collaborations around Digital Data with Humour and Irony' Science & Technology Studies 34(1) pp19-42
• Moats, D and Seaver, N 2019 ‘‘‘You Social Scientists Love Mind Games’’: Experimenting in the ‘‘divide’’ between data science and critical algorithm studies’ Big Data & Society 6 (1) https://doi.org/10.1177/2053951719833404
• Moats, D and Borra, E 2018 ’Quali-quantitative methods beyond networks: Studying information diffusion on Twitter with the Modulation Sequencer’ Big Data & Society 5 (1). https://doi.org/10.1177/2053951718772137