I'm guest editing a special issue of Digital Journalism on the subject of "journalism in an era of big data." Please see the CfP below or via http://sethlewis.org/call-for-papers-journalism-in-an-era-of-big-data-specia... I'm happy to field questions leading up to the July 1 deadline for extended abstracts: sclewis@umn.edu. Thanks! - Seth Lewis, U of Minnesota - - - - *Journalism in an Era of Big Data* Call for papers for a special issue of *Digital Journalism* (Routledge, Taylor & Francis; http://www.tandfonline.com/toc/rdij20/current) *Submission deadline:* July 1, 2013 (abstracts) January 1, 2014 (full papers for peer review) June 1, 2014 (revised full papers due) *Guest Editor:* Seth C. Lewis of the University of Minnesota, USA (*Digital Journalism* Editor: Bob Franklin) * * The term “Big Data” is often invoked to describe the overwhelming volume of information produced by and about human activity, made possible by the growing ubiquity of mobile devices, tracking tools, always-on sensors, and cheap computing storage. In combination with technological advances that facilitate the easy organizing, analyzing, and visualizing of such data streams, Big Data represents a social, cultural, and technological phenomenon with potentially major import for public knowledge and news information. How is journalism, like other social institutions, responding to this data abundance? What are the implications of Big Data for journalism’s norms, routines, and ethics? For its modes of production, distribution, and audience reception? For its business models and organizational arrangements? And for the overall sociology and epistemology of news in democratic society? This special issue of the international journal *Digital Journalism*(Routledge, Taylor & Francis) brings together scholarly work that critically examines the evolving nature of journalism in an era of Big Data. This issue aims to explore a range of phenomena at the junction between journalism and the social, computer, and information sciences—including the contexts and practices around news-related algorithms, applications, sophisticated mapping, real-time analytics, automated information services, dynamic visualizations, and other computational approaches that rely on massive data sets and their maintenance. This special issue seeks not simply to describe these tools and their application in journalism, but rather to develop what Anderson (2012) calls a “sociological approach to computational journalism”—a frame of reference that acknowledges the trade-offs, embedded values, and power dynamics associated with technological change. This special issue thus encourages a range of critical engagements with the problems as well as opportunities associated with data and journalism. The special issue welcomes articles drawing on a variety of theoretical and methodological approaches, with a preference for empirically driven or conceptually rich accounts. These papers might touch on a range of themes, including but not limited to the following: · The history (or histories) of computational forms of journalism; · The epistemological ramifications of “data” in contemporary newswork; · Norms, routines, and values associated with emerging forms of data-driven journalism, such as data visualizations, news applications, interactives, and alternative forms of storytelling; · The sociology of new actors connected to computational forms of journalism, within and beyond newsrooms (e.g., news application teams, programmer-journalists, tech entrepreneurs, web developers, and hackers); · The social, cultural, and technological roles of algorithms, automation, real-time analytics, and other forms of mechanization in contemporary newswork, and the implications of such for journalistic roles and routines; · The ethics of journalism in the context of Big Data; · The business, managerial, economic, and other labor-related issues associated with data-centric forms of newswork; · Approaches for conceptualizing the distinct nature of emerging journalisms (e.g., computational journalism, data journalism, algorithmic journalism, and programmer journalism); · The blurring boundaries between “news” and other types of information, and the role of Big Data and its related implications in that process Articles should be no more than 8,000 words in length, including references, etc. Please submit an abstract of 600-800 words that clearly spells out the theoretical construct, research questions, and methods that will be used. Also include the names, titles, and contact information for 2-3 suggested reviewers. Abstracts are due by July 1, 2013, to sclewis@umn.edu (with “DJ special issue” in the subject line). Providing the abstract meets the criteria for the call, full manuscripts are due by January 1, 2014 (also to sclewis@umn.edu), at which point they will be peer-reviewed and considered for acceptance. The proposed date of publication is 2015. Please contact guest editor Seth C. Lewis with questions: sclewis@umn.edu. Manuscripts should conform to the guidelines for Digital Journalism<http://www.tandfonline.com/action/authorSubmission?journalCode=rdij20&page=instructions> . -- ** Seth C. Lewis, Ph.D. Assistant Professor School of Journalism & Mass Communication University of Minnesota–Twin Cities http://sethlewis.org Guest editor, *Digital Journalism*: "Journalism in an Era of Big Data<http://sethlewis.org/call-for-papers-journalism-in-an-era-of-big-data-special-issue/> " New in 2013: Content analysis and Big Data<http://www.tandfonline.com/doi/abs/10.1080/08838151.2012.761702> ; Open innovation in digital journalism<http://nms.sagepub.com/content/15/2/314.abstract>; Audience clicks and news placement<http://crx.sagepub.com/content/early/2012/11/19/0093650212467031.abstract>