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This article explores the existing research literature on data journalism. Over the past years, this emerging journalistic practice has attracted significant attention from researchers in different fields and produced an increasing number of publications across a variety of channels. To better understand its current state, we surveyed the published academic literature between 1996 and 2015 and selected a corpus of 40 scholarly works that studied data journalism and related practices empirically. Analyzing this corpus with both quantitative and qualitative techniques allowed us to clarify the development of the literature, influential publications, and possible gaps in the research caused by the recurring use of particular theoretical frameworks and research designs. This article closes with proposals for future research in the field of data-intensive newswork.

Computational methods offer a new perspective on the evolving agendas of right-wing movements and parties online. This article showcases computational approaches to text analysis (specifically so-called topic models) to diachronically investigate nativist right-wing issues in social media by comparing comments posted on the Facebook page of the Pegida movement to those of the Alternative for Germany. After describing topic modelling as an increasingly popular method and drawing on the literature on right-wing nativism online, we investigate a set of shared issues relevant to the mobilization of commentators, including opposition to Islam, migration, the government and the media. We furthermore show contrastively how issue prevalence differs between the two groups, and how issue shares change over time, in some instances converging on a shared nativist core. We close with a series of suggestions on the utility of computation content analysis for the study of rapidly evolving political agendas.

Social media (SoMe) platforms provide potentially important information for news journalists during everyday work and in crisis-related contexts. The aims of this study were (a) to map central journalistic challenges and emerging practices related to using SoMe for collecting and validating newsworthy content; and (b) to investigate how practices may contribute to a user-friendly design of a web-based SoMe content validation toolset. Interviews were carried out with 22 journalists from three European countries. Information about journalistic work tasks was also collected during a crisis training scenario (<em>N </em>= 5). Results showed that participants experienced challenges with filtering and estimating trustworthiness of SoMe content. These challenges were especially due to the vast overall amount of information, and the need to monitor several platforms simultaneously. To support improved situational awareness in journalistic work during crises, a user-friendly tool should provide content search results representing several media formats and gathered from a diversity of platforms, presented in easy-to-approach visualizations. The final decision-making about content and source trustworthiness should, however, remain as a manual journalistic task, as the sample would not trust an automated estimation based on tool algorithms.