The latest issue of Media, Culture, and Society features an open-access discussion section responding to SMC all-stars danah boyd and Kate Crawford‘s “Critical Questions for Big Data.” Though the article is only a few years old, it’s been very influential and a lot has happened since it came out, so editors Aswin Punathambekar and Anastasia Kavada commissioned a few responses from scholars to delve deeper into danah and Kate’s original provocations.
The section features pieces by Anita Chan on big data and inclusion, André Brock on “deeper data,” Jack Qiu on access and ethics, Zizi Papacharissi on digital orality, and one by me, Nick Seaver, on varying understandings of “context” among critics and practitioners of big data. All of those, plus an introduction from the editors, are open-access, so download away!
My piece, titled “The nice thing about context is that everyone has it,” draws on my research into the development of algorithmic music recommenders, which I’m building on during my time with the Social Media Collective this fall. Here’s the abstract:
In their ‘Critical Questions for Big Data’, danah boyd and Kate Crawford warn: ‘Taken out of context, Big Data loses its meaning’. In this short commentary, I contextualize this claim about context. The idea that context is crucial to meaning is shared across a wide range of disciplines, including the field of ‘context-aware’ recommender systems. These personalization systems attempt to take a user’s context into account in order to make better, more useful, more meaningful recommendations. How are we to square boyd and Crawford’s warning with the growth of big data applications that are centrally concerned with something they call ‘context’? I suggest that the importance of context is uncontroversial; the controversy lies in determining what context is. Drawing on the work of cultural and linguistic anthropologists, I argue that context is constructed by the methods used to apprehend it. For the developers of ‘context-aware’ recommender systems, context is typically operationalized as a set of sensor readings associated with a user’s activity. For critics like boyd and Crawford, context is that unquantified remainder that haunts mathematical models, making numbers that appear to be identical actually different from each other. These understandings of context seem to be incompatible, and their variability points to the importance of identifying and studying ‘context cultures’–ways of producing context that vary in goals and techniques, but which agree that context is key to data’s significance. To do otherwise would be to take these contextualizations out of context.