Critical Data Studies explore the unique cultural, ethical, and critical challenges posed by Big Data. Rather than treat Big Data as only scientifically empirical and therefore largely neutral phenomena, CDS advocates the view that Big Data should be seen as always-already constituted within wider data assemblages. Assemblages is a concept that helps capture the multitude of ways that already-composed data structures inflect and interact with society, its organization and functioning, and the resulting impact on individuals’ daily lives. CDS questions the (...) many assumptions about Big Data that permeate contemporary literature on information and society by locating instances where Big Data may be naively taken to denote objective and transparent informational entities. In this introduction to the Big Data & Society CDS special theme, we briefly describe CDS work, its orientations, and principles. (shrink)

Recent work on Big Data and analytics reveals a tension between analyzing the role of emerging objects and processes in existing systems and using those same objects and processes to create new and purposeful forms of action. While the field of science and technology studies has had considerable success in pursuing the former goal, as Halford and Savage argue, there is an ongoing need to discover or invent ways to “do Big Data analytics differently.” In this commentary, I suggest that (...) attempts to produce new ways of working with Big Data and analytics might be hindered by how science and technology studies-influenced scholars have conceptualized assemblages. While these scholars have foregrounded objects’ relations within existing assemblages, new materialist philosophers draw attention to properties of objects that transcend those relations and might indicate opportunities for more creative or generative uses of Big Data and analytics. (shrink)

Within some online communities, discussion often centers on issues on which writers take sides, and within some subset of those debate-prone communities, we find over time that particular sets of writers almost always end up on the same side of an issue. These sets we call factions. In this paper, we describe a tool to perform what we call faction discovery on online communities. Generalizing methods developed in the bibliometrics and information retrieval literature, we define a network determined by similarities (...) of content in a community of users and add in direct evidence of online ties between users. We then perform community detection on the network to find factions. Using a set of data collected from science and fantasy blogs, we show that the discovered factions accurately reflect an active conflict in the community leading to significant, politically related social fracture. (shrink)