[Air-L] Journalism in an Era of Big Data - CfP for special issue

[Seth Lewis]

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-special-issue/

I'm happy to field questions leading up to the July 1 deadline for extended
abstracts: sclewis at 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 at 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 at 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 at 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>