Classifying Data Journalism

Abstract

The review of theoretical and empirical studies in data journalism has uncovered different conceptualisations of data journalistic artefacts. This quantitative content analysis of data-driven stories published by European quality news websites Zeit Online, Spiegel Online, The Guardian and Neue Zürcher Zeitung aims to outline universal characteristics of daily data-driven stories and to compare these findings with previous analyses of data stories and acclaimed data journalism projects. Results suggest that daily data journalism stories generally feature two visualisations that are likely to be bar charts. The majority of these visualisations are not interactive whereas maps turn out to be the most interactive type of visualisation. Data journalists rely predominantly on pre-processed data drawn from domestic governmental bodies. For the most part, data-driven stories are reports on political topics paralleling traditional news reporting. The sparsity of collaborative efforts and investigative approaches distinguishes daily data journalism from previous analyses of eclectic and elaborate data-driven projects.

KEYWORDS:

• computational journalism
• data-driven journalism
• data journalism
• data visualisation
• digital journalism
• Europe

ACKNOWLEDGEMENTS

I would like to thank the anonymous reviewers for their constructive and detailed comments.

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the author.

Notes

1 The Global Editors Network Data Journalism Award, the Marketwired Data Journalism Award and the investigative data journalism award of the Online News Association.

2 Some of these landing pages might have changed. Reorganisations within the news outlets over the last year have led to new teams and new output channels.

3 Level I = not interactive; level II = interactive; levels III, IV, V and VI = highly interactive.

4 They found 48.6 per cent political topics, 34.6 per cent societal topics and 23.5 per cent business topics.