Data storytelling is rapidly gaining prominence as a characteristic activity of digital journalism with significant adoption by small and large media houses. While a handful of previous studies have examined what characterises aspects of data storytelling like narratives and visualisation or analysis based on single cases, we are yet to see a systematic effort to harness these available resources to gain better insight into what characterises good data stories and how these are created. This study analysed 44 cases of outstanding data storytelling practices comprising winning entries of the Global Editors Network’s Data Journalism Award from 2013 to 2016 to bridge this knowledge gap. Based on a conceptual model we developed, we uniformly characterised each of the 44 cases and then proceeded to determine types of these stories and the nature of technologies employed in creating them. Our findings refine the traditional typology of data stories from the journalistic perspective and also identify core technologies and tools that appear central to good data journalism practice. We also discuss our findings in relations to the recently published 2017 winning entries. Our results have significant implications for the required competencies for data journalists in contemporary and future newsrooms.

Twitter has been widely adopted into journalistic workflows, as it provides instant and widespread access to a plethora of content about breaking news events, while also serving to disseminate reporting on those events. The content on Twitter, however, poses several challenges for journalists, as it arrives unfiltered, full of noise, and at an alarming velocity. Building on the results of the first national survey of social media use in Irish newsrooms, this paper investigates the adoption of social media into journalistic workflows, journalists’ attitudes towards various aspects of social media, and the content and perspectives generated by these online communities. It particularly investigates how Twitter shapes the processes of sourcing and verification in newsrooms, and assesses how notions of trust factor into the adoption of the Twitter platform and content into these processes. The paper further analyses relationships between journalist profile and adopted practices and attitudes, and seeks to understand how Twitter operates in the current journalistic landscape. While this paper draws its data from a survey of journalists in Ireland, the analysis of the relationship between trust, sourcing, and verification reveals broader patterns about journalistic values, and how these values and practices may operate in the field of journalism as a whole.

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.

Despite the growing interest in data journalism in newsrooms and its more recent emergence as an academic discipline, there is a need for systematic research on the state-of-the-art and current data journalism-related practices in newsrooms. The Global Data Journalism survey was an attempt to address this gap by studying the data journalism practices in newsrooms across the world. This study provides a descriptive view of the results of this study and discusses the findings on several aspects of data journalism practice, characteristics of data journalists and data teams, and their skills and educational requirements. We further provide insight into the values associated with journalistic work and analyse the ways in which the community believes data journalism has improved or undermined these values.

This study introduces a synthesised framework for the analysis of data visualisations in the news. Through a close examination of seminal content analyses, their methodologies and findings, this article proposes a framework that consolidates dimensional components of data visualisations previously scattered across this body of research. To transition from incidental and essentialist examinations of visual data artefacts towards a systematic and theory-informed exploration, we consider the diagrammatic dimensions of data visualisations. The offered synthesized framework can serve as a starting point for both theory-infused descriptive purposes as well as more theory-guided explorations. The framework is put to the test by analysing 185 visualisations drawn from award-winning data stories. Findings generated through the application of the framework highlight the varied composition of components of data visualisations, though certain combinations of components are prevalent, leading to static categorical comparisons or interactive spatial localization. After all, data artefacts can be understood as problem-posing elements that are the outcome of diagrammatic thinking that journalists employ to communicate claims.