Over recent years, the stakes and complexity of online content moderation have been steadily raised, swelling from concerns about personal conflict in smaller communities to worries about effects on public life and democracy. Because of the massive growth in online expressions, automated tools based on machine learning are increasingly used to moderate speech. While ‘design-based governance’ through complex algorithmic techniques has come under intense scrutiny, critical research covering algorithmic content moderation is still rare. To add to our understanding of concrete (...) instances of machine moderation, this article examines Perspective API, a system for the automated detection of ‘toxicity’ developed and run by the Google unit Jigsaw that can be used by websites to help moderate their forums and comment sections. The article proceeds in four steps. First, we present our methodological strategy and the empirical materials we were able to draw on, including interviews, documentation, and GitHub repositories. We then summarize our findings along five axes to identify the various threads Perspective API brings together to deliver a working product. The third section discusses two conflicting organizational logics within the project, paying attention to both critique and what can be learned from the specific case at hand. We conclude by arguing that the opposition between ‘human’ and ‘machine’ in speech moderation obscures the many ways these two come together in concrete systems, and suggest that the way forward requires proactive engagement with the design of technologies as well as the institutions they are embedded in. (shrink)

Search engines have become an integral part of our Internet use. They shape the way we look at the world, they provide orientation where there is none; but the maps they draw are too often hijacked by commercial interest. Search engines are less black box than black foam; functional decoupling, parasite technologies, and the embedding in the greater context of culture and society render the search act subject to overdetermination. Control is thus diluted into a dense network of human and (...) non-human “actants” and the power of the search engine is located in a control zone rather than a control center. In order to shift power back to the public, this paper proposes the concept of “symmetry of confidence”, a new relationship between search engine companies and their users. (shrink)

This research commentary proposes a conceptual framework for studying big tech companies as “technical systems” that organize much of their operation around the mastery and operationalization of key technologies that facilitate and drive their continuous expansion. Drawing on the study of Large Technical Systems (LTS), on the work of historian Bertrand Gille, and on the economics of General Purpose Technologies (GPTs), it outlines a way to study the “tech” in “big tech” more attentively, looking for compatibilities, synergies, and dependencies between (...) the technologies created and deployed by these companies. Using Google as example, the paper shows how to interrogate software and hardware through the lens of transversal applicability, discusses software and hardware integration, and proposes the notion of “data amalgams” to contextualize and complicate the notion of data. The goal is to complement existing vectors of “big tech” critique with a perspective sensitive to the specific materialities of specific technologies and their possible consequences. (shrink)

This paper discusses the empirical, Application Programming Interface -based analysis of very large Facebook Pages. Looking in detail at the technical characteristics, conventions, and peculiarities of Facebook’s architecture and data interface, we argue that such technical fieldwork is essential to data-driven research, both as a crucial form of data critique and as a way to identify analytical opportunities. Using the “We are all Khaled Said” Facebook Page, which hosted the activities of nearly 1.9 million users during the Egyptian Revolution and (...) beyond, as empirical example, we show how Facebook’s API raises important questions about data detail, completeness, consistency over time, and architectural complexity. We then outline an exploratory approach and a number of analytical techniques that take the API and its idiosyncrasies as a starting point for the concrete investigation of a large dataset. Our goal is to close the gap between Big Data research and research about Big Data by showing that the critical investigation of technicity is essential for empirical research and that attention to the particularities of empirical work can provide a deeper understanding of the various issues Big Data research is entangled with. (shrink)