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 mediatechnological 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.

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 mediatechnological 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.

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.

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.