Classifying Data Journalism: A content analysis of daily data-driven stories. (26th November 2018)

• Record Type: Journal Article
• Authors: Stalph, Florian
• Is Part Of: Journalism practice. Volume 12:Number 10(2018)
• Page Start: 1332
• Page End: 1350
• Publication Date: 2018-11-26
• Subjects: computational journalism -- data-driven journalism -- data journalism -- data visualisation -- digital journalism -- Europe; Journalism -- Periodicals; 070.405
• DOI: 10.1080/17512786.2017.1386583
• Languages: English
• ISSNs: 1751-2786

Abstract

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.