The phenomena of “big data” and an increasingly data-driven society are doubly relevant for journalism. Firstly, it is a topic that needs to be covered so that the related developments and their consequences are made understandable for the public and publicly discussed. Secondly, the datafication has already begun to affect news production and is giving rise to novel ways of identifying stories of public interest and new forms of telling them. What we witness is the emergence of a new journalistic sub-field most often called “computational journalism”, “data(-driven) journalism”, or “DDJ” for short.
Research Questions
The project addressed a blind spot with the research of data(-driven) journalism. Existing studies can be divided into three categories:
1. interviews with DDJ’s actors in different countries about their self-image (as journalists) and their integration in the production processes of established newsrooms;
2. various attempts to define DDJ as a distinguishable reporting style that revolves around some apparent core characteristics, which often contradict each other in their different restrictions and prioritisations;
3. case studies that empirically analyze the DDJ’s output – data-driven articles and larger publications –, but are restricted in their scope regarding time or geography.
What research lacks is a longitudinal analysis that helps us better understand how DDJ as a new form of reporting is evolving over time, that is, whether the list of key characteristics is changing or these characteristics are simply appearing in different forms. Journalists and researchers agree that data(-driven) journalism is still in its infancy and in continuous transition. Moreover, it is repeatedly seen as the future of journalism. However, we don’t even know much about how it is currently developing. Against this backdrop, our content analysis seeked to give answers to the following research questions:
RQ 1: What structural elements and forms of presentation are data-driven pieces composed of and how is this composition evolving?
RQ 2: How are the topics covered in data-driven projects changing over time?
RQ 3: How is the field of actors who produce data journalism (media organisations, external partners etc.) developing over time?
Sample
The selection of data-driven pieces for the analysis followed an inductive and pragmatic approach: our sample consists of projects nominated for the Data Journalism Awards (DJA), a prize issued annually by the Global Editors Network. So, instead of starting with a predetermined definition of what a “data-journalistic article” is, and thus creating a definition that could be too narrow or too broad, we used what is already identified as data journalism within the field itself. However, this advantage lead to a doubly biased sample. First, the analysed pieces were based on self-selection; as any data journalist must submit her/his work herself/himself to be considered for nomination by the organising committee. Second, nominees for a data journalism award do not represent ‘everyday’ data journalism. The field has already diversified and our sample very likely consists of what Borges-Rey (2016: 9) calls ‘an extensive, thoroughly researched, investigative form of data journalism’ rather than of the ‘daily, quick turnaround, generally visualised, brief form of data journalism’. At the same time, the sample had two particular advantages. First, systematic differences between “only nominated” and awarded projects could be identified. Second, we could assume that the analysed cases, being nominees for a DJA, fulfil a certain quality threshold and are considered best practice examples in the field itself.
Codebook
Our codebook for the content analysis comprised, amongst others, the (presumed) key characteristics of DDJ that have been identified by scholars. These included a (usually larger) data set as a basis of the project, the visualisation of the data in diagrams, charts etc., and interactive elements. Most variables and their values, that is the different forms a variable can take, were developed inductively based on an explorative analysis of a subsample from 2013 (see Table 1).
Table 1: Dimensions and variables of the codebook
| Dimension |
Variables |
| authorship |
medium; type of medium; external partners; number of people involved mentioned by name |
| story properties |
headline; topic; reference to a specific eventI; question(s) posed to data; number of related articlesII; length of article; language; winner of the DJA (or not) |
| data |
data source(s); type(s) of data source(s); access to data; kind of data; additional information on dataI; geographical reference; changeability of datasetIII; time period covered; unit of analysis |
| analysis and journalistic editing of content |
personalised case exampleIV; call for public intervention or criticismII; purpose of data analysisV; visualisation |
| interactive features |
interactive functions; online access to the databaseII; opportunities of communication (for users) |
I Suggested by data journalist Lorenz Matzat.
II Adopted from Parasie and Dagiral (2013: 5–14).
III Suggested by (data) journalism researcher Julian Ausserhofer.
IV Inspired by Holtermann (2011)
V Inspired by Gray et al. (2012: n.p.)
Sources
Borges-Rey, Eddy (2016): Unravelling data journalism. A study of data journalism practice in British newsrooms. In: Journalism Practice, 10(7), 833–843. Available online at: http://dx.doi.org/10.1080/17512786.2016.1159921.
Gray, Jonathan; Bounegru, Liliana; Chambers, Lucy (Hg.) (2012): The data journalism handbook. How journalists can use data to improve the news. (Early release). Sebastopol: O’Reilly. Available online at: http://datajournalismhandbook.org/.
Holtermann, Hannes (2011): Datenjournalismus: eine neue Form der journalistischen Wertschöpfung aus Daten. Unpublished: Master’s Thesis. Hamburg.
Lombard, Matthew; Snyder-Duch, Jennifer; Bracken, Cheryl Campanella (2002): Content analysis in mass communication. Assessment and reporting of intercoder reliability. In: Human Communication Research, 28(4), 587–604. Available online at: http://dx.doi.org/10.1111/j.1468-2958.2002.tb00826.x.
Parasie, Sylvain; Dagiral, Eric (2013): Data-driven journalism and the public good. “Computer-assisted-reporters” and “programmer-journalists” in Chicago. In: New Media & Society, 15(6), 853–871. Available online at: http://dx.doi.org/ 10.1177/1461444812463345.
Further Information
A more detailed description of the method, a short overview of research on DDJ in general and an analysis of the data from 2013 to 2015 has been published as a working paper of the Hans Bredow Institute (Arbeitspapiere des HBI) and made available as a download.
(Missing) Funding
The project was funded by Seed-Money of the HIIG and the HBI in the years 2013/2014.
Cooperation Partners
The project was supported externally by Fenja de Silva-Schmidt, a former student worker at the HBI and now research associate at the Institute of Journalism and Communication Studies (IJK) at the University of Hamburg.
