The Special Issue of Digital Journalism welcomes submissions, which broaden the theoretical, empirical and geographic perspective on data journalism as one particular form of digital journalism. Theoretically, since data journalism practices involve a convergence of disciplines, cross-disciplinary approaches can fruitfully combine insight from journalism theory with insight from fields such as data science, interaction design, or cognition studies. Empirically, there is a need for more reception studies; content analyses of different data journalism genres; news production studies focusing on smaller news organizations or cross-national collaborations, and case studies of criticized data journalism projects. Geographically, there is a lack of systematic comparative research and of studies mapping how data journalism is practiced in regions like Africa, Asia, and Russia where data journalists might face data scarcity or state control over information.
This special issue welcomes computational, qualitative and quantitative research on data journalism as well as conceptual papers from all theoretical perspectives.
Topics may include, but are not limited to research on:
- the way audiences use, evaluate and learn from different forms of data journalism, including data visualization and interactive data journalism,
- the impact of computational logics, artificial intelligence, intelligence augmentation and automation on data journalism practices,
- novel forms of finding, combining, creating and evaluating data for journalistic purposes,
- the sustainability of data journalism beyond legacy organizations, such as hyperlocal data journalism or data journalism involving other institutions, for example in terms of access to open data and the use of freedom of information legislation,
- the history and development of data journalism from a comparative perspective and beyond the Western world.
Deadline for 500-750-word abstracts is 6 April 2018.
Read the full call for papers.