It is often claimed that the rise of so called ‘big data’ and computationally advanced methods may exacerbate tensions between disciplines like data science and anthropology. This paper is an attempt to reflect on these possible tensions and their resolution, empirically. It contributes to a growing body of literature which observes interdisciplinary collabrations around new methods and digital infrastructures in practice but argues that many existing arrangements for interdisciplinary collaboration enforce a separation between disciplines in which identities are not really put at risk. In order to disrupt these standard roles and routines we put on a series of workshops in which mainly self-identified qualitative or non-technical researchers were encouraged to use digital tools (scrapers, automated text analysis and data visualisations). The paper focuses on three empirical examples from the workshops in which tensions, both between disciplines and between methods, flared up and how they were ultimately managed or settled. In order to characterise both these tensions and negotiating strategies I draw on Woolgar and Stengers’ use of the concepts humour and irony to describe how disciplines relate to each others’ truth claims. I conclude that while there is great potential in more open-ended collaborative settings, qualitative social scientists may need to confront some of their own disciplinary baggage in order for better dialogue and more radical mixings between disciplines to occur.

The figure of the imposter can stir complicated emotions, from intrigue to suspicion and fear. But what insights can these troublesome figures provide into the social relations and cultural forms from which they emerge? Edited by leading scholars in the field, this volume explores the question through a diverse range of empirical cases, including magicians, spirit possession, fake Instagram followers, fake art and fraudulent scientists. Proposing ‘thinking with imposters’ as a valuable new tool of analysis in the social sciences and humanities, this revolutionary book shows how the figure of the imposter can help upend social theory.The figure of the imposter can stir complicated emotions, from intrigue to suspicion and fear. But what insights can these troublesome figures provide into the social relations and cultural forms from which they emerge? Edited by leading scholars in the field, this volume explores the question through a diverse range of empirical cases, including magicians, spirit possession, fake Instagram followers, fake art and fraudulent scientists. Proposing ‘thinking with imposters’ as a valuable new tool of analysis in the social sciences and humanities, this revolutionary book shows how the figure of the imposter can help upend social theory.

‘Our friends have been suggesting for quite a long time that we visit this wonderful city. [...] They have a famous cathedral there, Salisbury Cathedral. [...] It’s famous for its clock. It’s one of the oldest working clocks in the world.’ These words are from an interview with two Russian men on Russian state television news (Russia Today, RT) on 7 March 2018 (Figure 1.1).1?Their appearance followed an incident on 4 March 2018, when Salisbury resident Sergei Skripal and his daughter Yulia were rushed to hospital. The authorities found traces of Novichok A-234, a nerve agent, at the?scene. The two Russian men were subsequently named as suspects by British police and their faces splashed all over the news (Figure 1.2). The?UK government took the bold step of accusing the Russian government of attempted murder and expelling several Russian diplomats. Then?suddenly the two suspects appeared on TV. The interviewer asked them why they were in Salisbury and if they worked for the Russian?Intelligence Services to which their cryptic reply was “Do you?”. When pressed about their actual profession they offered, “If we tell you about?our business, this will affect the people we work with.”

Science and technology studies is famous for questioning conceptual and material boundaries by following controversies that cut across them. However, it has recently been argued that in research involving online platforms (Wikipedia, Facebook, Twitter, etc.), there are also more practical boundaries to negotiate that are created by the variable availability, visibility, and structuring of data. In this paper, I highlight a potential tension between our inclination toward following controversies and “following the medium” and suggest that sometimes following controversies might involve going “against platforms” as well as with them. I will illustrate this dilemma through an analysis of the controversy over the coverage of the Fukushima disaster on English language Wikipedia, which concerns boundaries between expert and lay knowledge but also the social and technical functioning of Wikipedia itself. For this reason, I show that following the controversy might mean making use of less formatted and less obvious data than Wikipedia normally provides. While this is not an argument against the use of automated digital research tools such as scrapers, I suggest that both quantitative and qualitative researchers need to be more willing to tweak their approaches based on the specificities of the case.

There is a long history in science and technology studies (STS) of tracking problematic objects, such as controversies, matters of concern, and issues, using various digital tools. But what happens when public problems do not play out in these familiar ways? In this paper, we will think through the methodological implications of studying “problems” in relation to recent events surrounding the sharing of patient data in the National Health Service in the United Kingdom. When a data sharing agreement called care.data was announced in 2013, nearly 1.5 million citizens chose to opt out. Yet, in subsequent years, there has been little evidence of a robust public mobilising around data sharing. We will attempt to track this elusive ‘non problem’ using some digital tools developed in STS for the purpose of mapping issues and problem definitions within science. Although we find these digital tools are unable to capture the “problem,” the process of searching helps us map the terrain of the case and forces us to consider wider definitions.

In recent years, many qualitative sociologists, anthropologists, and social theorists have critiqued the use of algorithms and other automated processes involved in data science on both epistemological and political grounds. Yet, it has proven difficult to bring these important insights into the practice of data science itself. We suggest that part of this problem has to do with under-examined or unacknowledged assumptions about the relationship between the two fields—ideas about how data science and its critics can and should relate. Inspired by recent work in Science and Technology Studies on interventions, we attempted to stage an encounter in which practicing data scientists were asked to analyze a corpus of critical social science literature about their work, using tools of textual analysis such as co-word and topic modelling. The idea was to provoke discussion both about the content of these texts and the possible limits of such analyses. In this commentary, we reflect on the planning stages of the experiment and how responses to the exercise, from both data scientists and qualitative social scientists, revealed some of the tensions and interactions between the normative positions of the different fields. We argue for further studies which can help us understand what these interdisciplinary tensions turn on—which do not paper over them but also do not take them as given.

Although the rapid growth of digital data and computationally advanced methods in the social sciences has in many ways exacerbated tensions between the so-called ‘quantitative’ and ‘qualitative’ approaches, it has also been provocatively argued that the ubiquity of digital data, particularly online data, finally allows for the reconciliation of these two opposing research traditions. Indeed, a growing number of ‘qualitatively’ inclined researchers are beginning to use computational techniques in more critical, reflexive and hermeneutic ways. However, many of these claims for ‘quali-quantitative’ methods hinge on a single technique: the network graph. Networks are relational, allow for the questioning of rigid categories and zooming from individual cases to patterns at the aggregate. While not refuting the use of networks in these studies, this paper argues that there must be other ways of doing quali-quantitative methods. We first consider a phenomenon which falls between quantitative and qualitative traditions but remains elusive to network graphs: the spread of information on Twitter. Through a case study of debates about nuclear power on Twitter, we develop a novel data visualisation called the modulation sequencer which depicts the spread of URLs over time and retains many of the key features of networks identified above. Finally, we reflect on the role of such tools for the project of quali-quantitative methods.

What are the ethics of a network graph? Data visualizations like the above present problems for qualitative researchers because they involve data about more users than can feasibly give consent, and they also involve giving over more control in the research process to tools, APIs, and algorithms, while ethical frameworks often assume a heavily orchestrated research process, with the researcher at the helm. This chapter addresses some of the ethical implications of visualizations utilizing data from social media platforms, drawing on material from two ongoing studies (one which follows nuclear controversies on e-mail lists and Facebook, and another about diet-related hashtags on Instagram). While we cannot hope to offer any programmatic or definitive statements on the matter, we will draw on some insights from Science and Technology Studies (STS) to highlight how ethical issues are distributed between different aspects of the research process and between different types of actors, including non-human algorithms and tools.

This article assesses the usefulness for social media research of controversy analysis, an approach developed in Science and Technology Studies (STS) and related fields. We propose that this approach can help to address an important methodological problem in social media research, namely, the tension between social media as resource for social research and as an empirical object in its own right. Initially developed for analyzing interactions between science, technology, and society, controversy analysis has in recent decades been implemented digitally to study public debates and issues dynamics online. A key feature of controversy analysis as a digital method, we argue, is that it enables a symmetrical approach to the study of media-technological dynamics and issue dynamics. It allows us to pay equal attention to the ways in which a digital platform like Twitter mediates public issues, and to how controversies mediate “social media” as an object of public attention. To sketch the contours of such a symmetrical approach, the article discusses examples from a recent social media research project in which we mapped issues of “privacy” and “surveillance” in the wake of the National Security Agency (NSA) data leak by Edward Snowden in June 2013. Through a discussion of social media research practice, we then outline a symmetrical approach to analyzing controversy with social media. We conclude that the digital implementation of such an approach requires further exchanges between social media researchers and controversy analysts.