The Politics of Algorithms and Digital Data

We are currently drowning in digital data and struggling to make sense of it with traditional techniques. My research is about how we can understand these new data sources using data visualisations which are more exploratory, reflexive and better capture the complexities of social life.

Visualizations as Exploratory MapsThe contents of a Facebook page. The middle of the graph represent all of the posts in time order, the left side is a network of users who interact with them and the right side is a network of words that appear in them.
As people in different industries frantically amass digital data such as patient records, social media posts, images, feedback, online transactions, or motion tracking data there is an urgent need to rethink how data is processed and analysed. Much of this data is made sense of using automated computational processes like algorithms, machine learning and statistical procedures in which human experts only supervise or ‘train’ the scripts to find patterns in the data. This results in very opaque systems which squeeze people into categories, or give them uneven access to services (health care, loans, insurance policies or jobs) while the inner workings of these systems remain unaccountable and often incomprehensible to their users. Popular commentator Cathy O’Neil has even dubbed these “weapons of math destruction”.
Qualitative researchers, anthropologists and philosophers have regularly complained that these methods of data analysis are reductive, and fail to capture the complexities of lived experience. They have shown how people frequently fall between the cracks of algorithmically assigned categories or try to game their metrics. They have argued that these types of analyses contain all sorts of assumptions and judgements which are concealed underneath a language of objectivity, yet have wide reaching political and ethical consequences. However, these researchers have rarely proposed alternative ways of analysing data at scale. 

Visualizations as Exploratory Maps

New techniques for visualizing data offer the possibility to represent complex information without unnecessary reduction or aggregation, which means that digital data could be analysed in more interpretive and exploratory ways. These visualisations could be read like maps charting an uncertain terrain – like a doctor looking at a MRI scan of a brain – instead of being used to convince us of correlations and quantities or explain the relationship between narrowly defined variables. Such techniques could allow practitioners to ask broader questions of their data, problematise their data sources and measurement systems and allow more fruitful collaborations with researchers from other disciplines. However, these possibilities for research are often complicated by the fact that they fall between different disciplines and traditions of so called “quantitative” and “qualitative research”.

An area graph of references.An area graph of references used in a Wikipedia article, coloured by type.

Collaborations in varoius fields

I am currently undertaking a series of collaborations with data scientists and programmers in various fields in order to learn about how these disciplinary and methodological tensions play out in practice and how an alternative mode of data analysis might be possible. I am currently working with practitioners in epidemiology, sports data and government statistics and experimenting with these new forms of visualisation and analysis. I am also conducting related research on the impact of data analytics on political campaigns and polling and the increasing drive to measure and rank academics and institutions, also through digital data.

Research ProjectShow/Hide content

Recent publicationsShow/Hide content

2020 'Rethinking the “Great Divide”: Approaching interdiciplinary collaborations around digital data with humour and irony’ Science & Technology Studies.

2019 “You Social Scientists Love Mind Games”: Experimenting in the “divide” between data science and critical algorithm studies, David Moats, Nick Seaver,

2018 ‘Following the Fukushima Disaster on (and against) Wikipedia: A Methodological Note about STS Research and Online Platforms’ Science, Technology & Human Values

2018 ‘In Search of a Problem: Mapping Controversies over NHS (England) Patient Data with Digital Tools’ Science, Technology & Human Values (with Liz McFall) https://doi.orgsci/10.1177/0162243918796274

2018 ’Quali-quantitative methods beyond networks: Studying information diffusion on Twitter with the Modulation Sequencer’ Big Data & Society 5 (1) (with Erik Borra)

2017 ‘From media technologies to mediated events: a different settlement between media studies and science and technology studies’ Information, Communication & Society

2017 ‘How Does it Feel to be Visualized: Redistributing Ethics’ in Internet Research Ethics for the Social Age: New Cases and Challenges. Zimmer, M and Kinder-Kurlanda, K eds, New York: Peter Lang (with Jessamy Perriam)

2016 ‘Of Stories and Numbers: Rethinking the Settlement between Anthropology and Metrics in Global Health’ Science as Culture 25 (4) 594 - 599

2015 ‘Mapping Controversies with Social Media: The Case for Symmetry’ Social Media + Society 1(2) 1-17 with Noortje Marres


CVShow/Hide content


  • 2011-2015
    PhD Sociology, Goldsmiths, University of London
  • 2009-2010
    MSc Sociology, Culture and Society, London School of Economics, Distinction   
  • 2004-2007
    BA History of Art, University College London, 1st Class Honours


  • Are We Data? (AWED): University of Edinburgh (UK) 2018-19
  • Intervening with Data, Linköping University (SE) 2017
  • Consultant, ARITHMUS: Goldsmiths (UK) 2016-2017
  • Consultant, Insuring Health in a Digital World: Open University (UK) 2015-16

Awards and Funding

  • RJ Research Initiation Fund 200,000 SEK June 2017
  • ESRC Multidisciplinary Fund (£2500) – Goldsmiths 2014
  • ESRC +3 PhD Funding with Advanced Quantitative Methods (AQM) — Goldsmiths 2011
  • Hobhouse Memorial Prize for Academic Excellence – LSE 2010
  • Zilkha Prize (highest mark in class) – UCL 2007


OrganisationShow/Hide content