New methods to analyse social media data provide a powerful way to know publics and capture what they say and do. At the same time, access to these methods is uneven, with corporations and governments tending to have best access to relevant data and analytics tools. Critics raise a number of concerns about the implications dominant uses of data mining and analytics may have for the public: they result in less privacy, more surveillance and social discrimination, and they provide new (...) ways of controlling how publics come to be represented and so understood. In this paper, we consider if a different relationship between the public and data mining might be established, one in which publics might be said to have greater agency and reflexivity vis-à-vis data power. Drawing on growing calls for alternative data regimes and practices, we argue that to enable this different relationship, data mining and analytics need to be democratised in three ways: they should be subject to greater public supervision and regulation, available and accessible to all, and used to create not simply known but reflexive, active and knowing publics. We therefore imagine conditions in which data mining is not just used as a way to know publics, but can become a means for publics to know themselves. (shrink)

During the course of this article, we examine the use of membership categorisation practices by a high-profile celebrity public social media account that has been understood to generate interest, attention and controversy across the UK media ecology. We utilise a data set of harvested tweets gathered from a high-profile public ‘celebrity antagonist’ in order to systematically identify types of antagonistic formulation that have generated different levels of interest within the social media community and beyond. Drawing from classic ethnomethodological studies of (...) banner headlines and other means of generating public interest and ‘making sense’, we respecify high-profile antagonistic tweets as category formulations that exhibit particular and regular membership category features that are reflexively bound to potential antagonistic readings, interest and controversy. In conclusion, we consider how such formulations may be understood to represent resources that constitute ignition points within antagonistic flows of communication and information that can be metaphorically understood as ‘digital wildfires’. (shrink)

This paper proposes an account of scientific data that makes sense of recent debates on data-driven and ‘big data’ research, while also building on the history of data production and use particularly within biology. In this view, ‘data’ is a relational category applied to research outputs that are taken, at specific moments of inquiry, to provide evidence for knowledge claims of interest to the researchers involved. They do not have truth-value in and of themselves, nor can they be seen as (...) straightforward representations of given phenomena. Rather, they are fungible objects defined by their portability and prospective usefulness as evidence. (shrink)

In the age of the digital generation, written public data is ubiquitous and acts as an outlet for today's society. Platforms like Facebook, Twitter, Google+ and LinkedIn have profoundly changed how we communicate and interact. They have enabled the establishment of and participation in digital communities as well as the representation, documentation and exploration of social behaviours, and had a disruptive effect on how we use the Internet. Such digital communications present scholars with a novel way to detect, observe, analyse (...) and understand online communities over time. This article presents the formalization of a Social Observatory: a low latency method for the observation and measurement of social indicators within an online community. Our framework facilitates interdisciplinary research methodologies via tools for data acquisition and analysis in inductive and deductive settings. By focusing our Social Observatory on the public Facebook profiles of 187 federal German politicians we illustrate how we can analyse and measure sentiment, public opinion, and information discourse in advance of the federal elections. To this extent, we analysed 54,665 posts and 231,147 comments, creating a composite index of overall public sentiment and the underlying conceptual discussion themes. Our case study demonstrates the observation of communities at various resolutions: “zooming” in on specific subsets or communities as a whole. The results of the case study illustrate the ability to observe published sentiment and public dialogue as well as the difficulties associated with established methods within the field of sentiment analysis within short informal text. (shrink)

During the course of this short article, I respond and connect with insights and issues raised by the Conversational Rollercoaster and its relationship with conversation analysis and the public science of talk. I constructively engage with the innovative thinking and practice associated with this initiative. Finally, I consider how this might enhance a potential drive to connect ethnomethodology and CA to additional public and collaborative practices and interventions in the digital age.

How a social science of big data would look like? In this article, we exemplify such a social science through a number of cases. We start our discussion with the epistemic qualities of big data. We point out to the fact that contrary to the big data champions, big data is neither new nor a miracle without any error nor reliable and rigorous as assumed by its cheer leaders. Secondly, we identify three types of big data: natural big data, artificial (...) big data and human big data. We present and discuss in what ways they are similar and in what other ways they differ. The assumption of a homogenous big data in fact misleads the relevant discussions. Thirdly, we extended 3 Vs of the big data and add veracity with reference to other researchers and violability which is the current author’s proposal. We explain why the trinity of Vs is insufficient to characterize big data. Instead, a quintinity is proposed. Fourthly, we develop an economic analogy to discuss the notions of data production, data consumption, data colonialism, data activism, data revolution, etc. In this context, undertaking a Marxist approach, we explain what we mean by data fetishism. Fifthly, we reflect on the implications of growing up with big data, offering a new research area which is called as developmental psychology of big data. Finally, we sketch data resistance and the newly proposed notion of omniresistance, i.e. resisting anywhere at any occasion against the big brother watching us anywhere and everywhere. (shrink)

This commentary elaborates on the ideas and projects outlined in this special issue, from a specifically sociological perspective. Much recent work in sociology proposes ‘methods mashups’ of ethnography and digital data/computational tools in different and diverse ways. However, typically, these have taken the form of applying the principles of ethnography to new domains and data types, as if ethnography itself is stable and immutable; that it has a universal set of methodological principles that unify ethnographic practice. Returning to anthropology is, (...) therefore, a useful way to extend our methodological thinking to consider what ethnography is and how it operates, and from there think more clearly about how it may be effectively combined with digital data/computational tools in an emerging ‘Computational Anthropology’. (shrink)