Classifying Data Journalism

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.     

3Ws of Data Journalism Education

This paper explores data journalism education, with a particular focus on formal training in the higher education sector globally. The study draws on data from: (1) the 2017 Global Data Journalism Survey, to study the state of data journalism education and the requirements in terms of training and (2) a dataset of 219 unique modules or programmes on data journalism or related fields that were curated and examined in order to understand the nature of data journalism education in universities across the world. The results show that while journalists interested in data are highly educated in journalism or closely related fields, they do not have a strong level of education in the more technical areas of data journalism, such as data analysis, coding and data visualisation. The study further reveals that a high proportion of data journalism courses are concentrated in the United States, with a growing number of courses developing across the world, and particularly in Europe. Despite this, education in the field does not have a strong academic underpinning, and while many courses are emerging in this area, there are not enough academically trained instructors to lead and/or teach such interdisciplinary programmes in the higher education sector.     

Beyond “Woodstein”: Narratives of Investigative Journalism

ABSTRACT

Using a methodology inspired by structural narratology and by James Hamilton’s [2016. Democracy’s Detectives: The Economics of Investigative Journalism. Cambridge: Harvard] economic analysis of investigative journalism, this paper identifies a set of 14 recurring structural and formal elements (plot events, character types and functions, visual iconography) that constitute a fable about investigative journalism. The fable structure is applied to analyze six diverse films about investigative journalism produced in the US in the last 40 years. The films include two instantiations of successful investigative journalism (All the President’s Men, Spotlight), two cases where conflict between journalists and corporate managers diminished the impact of the investigation (Good Night and Good Luck, The Insider), and two instances of a counter-fable of failed investigative journalism (Truth, Kill the Messenger). The paper argues that the films’ representation of investigative journalism influences public perceptions of investigative journalism. It also speculates about the factors that will influence investigative journalism and its representations in the current political context in the US.     

Automated SKOS Vocabulary Design for the Biopharmaceutical Industry

Ensuring quick and consistent access to large collections of unstructured documents is one of the biggest challenges facing knowledge-intensive organizations. Designing specific vocabularies to index and retrieve documents is often deemed too expensive, full-text search being preferred despite its known limitations. However, the process of creating controlled vocabularies can be partly automated thanks to natural language processing and machine learning techniques. With a case study from the biopharmaceutical industry, we demonstrate how small organizations can use an automated workflow in order to create a controlled vocabulary to index unstructured documents in a semantically meaningful way.