The nice thing about context is that everyone has it. (October 2015)

• Record Type: Journal Article
• Authors: Seaver, Nick
• Is Part Of: Media, culture, and society. Volume 37:Number 7(2015:Oct.)
• Page Start: 1101
• Page End: 1109
• Publication Date: 2015-10
• Subjects: context -- cultural anthropology -- algorithms -- big data -- recommender systems; Mass media and culture -- Periodicals; Mass media -- Social aspects -- Periodicals; Wireless communication systems -- Social aspects -- Periodicals; 302.2305
• DOI: 10.1177/0163443715594102
• Languages: English
• ISSNs: 0163-4437

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.