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This study attempts to provide the basis for restructuring data-driven reporting among Nigerian journalists by examining its content and practice. Field Theory and Normalization Theory provide the theoretic framework, while focus group discussions by 30 respondents help generate the requisite data. Findings show that Nigerian journalists are not only knowledgeable about the concept of data-driven journalism, but they are also actively engaged as practitioners. Discussants attest to the factuality, verifiability, and reliability associated with data-driven journalism, the challenges notwithstanding. It turned out that its reshaping and sustainability would hinge on two factors. The first is to intensify data-driven journalism training and research. The second is for government to formulate policies or popularize the legislations that guarantee access to data and make data-driven journalism more professional.

Can journalists use data in order to produce better journalism? In this text, C.W. Anderson traces the genealogy of data journalism and its material and technological underpinnings, arguing that the use of data in news reporting is inevitably intertwined with national politics, the evolution of computable databases, and the history of professional scientific fields. This work shows how the changes in specifically journalistic understandings of evidence can help us think through the current 'digital data moment' in ways that go beyond simply journalism

A journalistic paradigm defines what journalism is, and what journalists should do, in relation to reporting news. Drawing from desk research and in-depth interviews with eighteen data journalists and experts in the United Kingdom, this article discusses the recent development and practices of data journalism in the United Kingdom and their implications for the journalistic paradigm, which involves the substantial use of interviewing. Embracing the opportunities provided by the datafication of society and the British government's open data initiative, UK news organizations have institutionalized and incorporated data journalism into their organizational structure. However, difficulties in practice have emerged, resulting from new, mostly ethically grounded issues surrounding data. Traditional journalistic practices, in particular interviewing and cross-checking, provide a useful, practical guide to solving related problems. The institutionalization of data journalism, as well as traditional journalistic methods and skills coming as a solution, assimilate data journalism into conventional journalistic practices, reinforcing – rather than undermining – the journalistic paradigm.

The prevalence of data journalism in recent years has challenged traditional journalistic norms as well as the relationship between journalism and other subjects, affecting journalism both internally and externally. While the practice of data journalism has become increasingly pervasive around the world, systematic research on data journalism is only just starting to receive scholarly attention. The multi-aspects of data journalism cause difficulties in attempts to define it critically and clearly, and the limited number of previous studies suffer from a lack of coherent connections bridging the field's academic and professional dimensions. Through a concept explication, this article generates theoretical and operational definitions of data journalism via empirical analysis and meaning analysis.

Extant research on data journalism in Africa has focused on newsroom factors and the predilections of individual journalists as determinants of the uptake of data journalism on the continent. This article diverts from this literature by examining the slow uptake of data journalism in sub- Saharan Africa through the prisms of non-newsroom factors. Drawing on in-depth interviews with prominent investigative journalists sampled from several African countries, we argue that to understand the slow uptake of data journalism on the continent; there is a need to critique the role of data politics, which encompasses state, market and existing media ecosystems across the continent. Therefore, it is necessary to move beyond newsroom-centric factors that have dominated the contemporary understanding of data journalism practices. A broader, non-newsroom conceptualisation beyond individual journalistic predilections and newsroom resources provides productive clarity on data journalism's slow uptake on the continent. These arguments are made through the conceptual prisms of materiality, performativity and reflexivity.

Data journalism has recently emerged as an important method of news gathering and editing in journalism. For data journalism -which includes the process of producing news from data- journalists need to have various skills and competencies. As a result of the literature review, it is observed that the required skills and competencies for data journalism have not been determined precisely, and in this regard, there is a lack of quantitative studies to measure these skills and competencies of data journalism. This study aims to develop a unique scale that will reveal the data journalism skills of news producers. For this purpose, a data journalism scale has been developed through an online survey applied to news producers registered with the Turkish Press Agency (BIK). As a result of the item pool, expert views, and the scale application stages, it is understood that data journalism skills can be measured with a 2-factor structure: "data skills and tools" and "journalism skills." The Cronbach Alpha reliability coefficient for the overall 21-item data journalism scale was calculated as 0.856, the data skills and tools factor as 0.883, and the journalism skill factor as 0.821. As a result of the research, it has been determined that while the journalism skills of the news producers are at a high level, their competence in data processing is low. In this sense -as a result of the study- a revision is recommended in both academic and professional training curriculum to include data collection, analysis and visualization skills that will improve data skills of news producers.

Die Entstehung des Datenjournalismus kann als Antwort des Journalismus auf die "Datafizierung" der Gesellschaft verstanden werden: In der Auseinandersetzung mit dem Phänomen "Big Data" entwickelt der Journalismus neue Wege, Geschichten (in Daten) zu identifizieren und (mit Daten) zu erzählen. Von Journalismusforschern wie -praktikern wird das dabei entstehende Berichterstattungsmuster häufig als die Zukunft des Journalismus angesehen, vor allem aber als genuin für den Onlinejournalismus. Aus Sicht der Forschung ist jedoch noch nicht einmal der Status Quo des ohnehin sehr dynamischen Datenjournalismus geklärt: Noch wissen wir wenig insbesondere über das, was den Datenjournalismus als Berichterstattungsmuster ausmacht, nämlich die datenjournalistischen Beiträge sowie die spezifischen inhaltlichen und darstellerischen Elemente, aus denen sie bestehen und die sie von anderen Formen der Berichterstattung abgrenzen. Der Schwerpunkt dieser Studie liegt daher auf den Produkten des Datenjournalismus: Mithilfe einer standardisierten Inhaltsanalyse untersuchen wir datenjournalistische Projekte, die man als "Goldstandard" datengetriebener Berichterstattung betrachten kann - Projekte, die von 2013 bis 2015 (N = 179) für einen der Data Journalism Awards (DJA) nominiert wurden, einen jährlich vom Global Editors Network in unterschiedlichen Kategorien vergebenen Preis. Untersucht wurden unter anderem die genutzten Datenarten und -quellen, Visualisierungsstrategien, Interaktionsmöglichkeiten, die behandelten Themen sowie die Medienangebote, von denen die Beiträge stammen. Die Ergebnisse zeigen unter anderem, dass über 40 Prozent der datengetriebenen Arbeiten auf Webseiten von Tages- oder Wochenzeitungen veröffentlicht wurden und fast 20 Prozent von Non-Profit Organisationen für investigativen Journalismus wie Pro Publica stammen. Fast die Hälfte der Fälle behandelt ein politisches Thema. Auch Gesellschafts- und Wirtschafts- sowie Gesundheits- und Wissenschaftsthemen kommen vermehrt vor. Finanz- und Geodaten sind die meistgenutzten Datenarten, wobei sich der Großteil der Datensätze auf eine nationale Ebene bezieht. In mehr als zwei Drittel der Projekte werden die Daten einer offiziellen Quelle wie zum Beispiel Eurostat verwendet. In weiteren Analysen werden die Unterschiede zwischen den Jahren 2013, 2014 und 2015 herausgearbeitet und Visualisierungsstrategien sowie Interaktionsmöglichkeiten untersucht.

Die Entstehung des Datenjournalismus kann als Antwort des Journalismus auf die "Datafizierung" der Gesellschaft verstanden werden: In der Auseinandersetzung mit dem Phänomen "Big Data" entwickelt der Journalismus neue Wege, Geschichten (in Daten) zu identifizieren und (mit Daten) zu erzählen. Von Journalismusforschern wie -praktikern wird das dabei entstehende Berichterstattungsmuster häufig als die Zukunft des Journalismus angesehen, vor allem aber als genuin für den Onlinejournalismus. Aus Sicht der Forschung ist jedoch noch nicht einmal der Status Quo des ohnehin sehr dynamischen Datenjournalismus geklärt: Noch wissen wir wenig insbesondere über das, was den Datenjournalismus als Berichterstattungsmuster ausmacht, nämlich die datenjournalistischen Beiträge sowie die spezifischen inhaltlichen und darstellerischen Elemente, aus denen sie bestehen und die sie von anderen Formen der Berichterstattung abgrenzen. Der Schwerpunkt dieser Studie liegt daher auf den Produkten des Datenjournalismus: Mithilfe einer standardisierten Inhaltsanalyse untersuchen wir datenjournalistische Projekte, die man als "Goldstandard" datengetriebener Berichterstattung betrachten kann - Projekte, die von 2013 bis 2015 (N = 179) für einen der Data Journalism Awards (DJA) nominiert wurden, einen jährlich vom Global Editors Network in unterschiedlichen Kategorien vergebenen Preis. Untersucht wurden unter anderem die genutzten Datenarten und -quellen, Visualisierungsstrategien, Interaktionsmöglichkeiten, die behandelten Themen sowie die Medienangebote, von denen die Beiträge stammen. Die Ergebnisse zeigen unter anderem, dass über 40 Prozent der datengetriebenen Arbeiten auf Webseiten von Tages- oder Wochenzeitungen veröffentlicht wurden und fast 20 Prozent von Non-Profit Organisationen für investigativen Journalismus wie Pro Publica stammen. Fast die Hälfte der Fälle behandelt ein politisches Thema. Auch Gesellschafts- und Wirtschafts- sowie Gesundheits- und Wissenschaftsthemen kommen vermehrt vor. Finanz- und Geodaten sind die meistgenutzten Datenarten, wobei sich der Großteil der Datensätze auf eine nationale Ebene bezieht. In mehr als zwei Drittel der Projekte werden die Daten einer offiziellen Quelle wie zum Beispiel Eurostat verwendet. In weiteren Analysen werden die Unterschiede zwischen den Jahren 2013, 2014 und 2015 herausgearbeitet und Visualisierungsstrategien sowie Interaktionsmöglichkeiten untersucht.

Despite growing interest in the emergence of technologies in journalistic practices, especially from the production perspective, there is still very little research on organizational structures and professional culture in relation to the deployment of these technologies. Drawing on six interviews and observation in staff meetings, this study aims to explore the nuances behind the professional roles of data journalists and how these relate to structural aspects of news organizations. The study focuses on the case of ProPublica, a news organization internationally renowned for its global excellence in data stories. This work considers boundary-making in the context of journalism and focuses on new professional roles in the news industry to produce a hybrid ethnography study based on qualitative data collected immediately before the Covid-19 pandemic hit the United States. The findings reveal the importance of hybrid profiles at ProPublica. While some journalists have had to expand their knowledge to learn more about new areas, such as coding and design, some non-journalistic professionals have had to develop writing skills, and this blurring of traditional boundaries forms an important aspect of ProPublica's professional culture. The structure of the organization, divided into two teams engaged in cross-sector activities, helps to promote data skills and collaboration with other journalists, which also serves to mitigate any individual lack of experience on certain topics. The article concludes by suggesting that the growing importance of these new professional roles has broader implications for the development of data skills in the newsroom, and also discusses the limitations that can arise from the increasing overlap between journalistic and non-journalistic roles.

Aleszu Bajak, Senior Data Reporter at USA Today, discusses his reporting on social media and politics using computational methods. We talk about the types of data that data journalists are working with, how they acquire it (e.g., Freedom of Information Requests), and how they approach reporting results in a way that tells an engaging...
The post #151: Data Journalism to Report Social Media and Politics, with Aleszu Bajak appeared first on Social Media and Politics.

Ancien collaborateur d'Owni, laboratoire de journalisme numérique sans but lucratif 1 , Sylvain Lapoix est l'un des journalistes français qui pratiquent le « data journalisme » ou journalisme de données 2 . C'est dans le cadre de cette nouvelle pratique qu'il a notamment couvert les luttes entourant l'émergence des gaz de schiste. Plus récemment, durant la campagne présidentielle française de 2012, il cofonde le « Véritomètre, » projet de factchecking des candidats à l'élection 3 . Pour comprendre les enjeux de ce qui est régulièrement présenté comme l'avenir du journalisme d'enquête, voire une véritable « révolution » pour le journalisme et une opportunité démocratique pour l'espace public, Mouvements a voulu en savoir plus, avec lui, sur ce qu'était véritablement le data journalisme, ses spécificités pratiques et politiques mais aussi mieux comprendre les liens entre data journalisme, data visualisation, factchecking voire aussi open data, toutes pratiques qui semblent faire de la « donnée » la pierre de Rosette d'un espace public critique.

Data journalism has become a stable trend in the development of the global media system. The article is devoted to the key competencies that a data journalist should possess. Examples of the foreign and Russian publications prepared on the basis of data analysis are considered. Special attention is paid to the training of data journalists. The system of journalistic education is not ready to fully meet the challenges of the media market: we can safely say that there are very few educational programs on data journalism in Russia.

Although data journalism is practiced globally, data journalism research has traditionally focused on a limited set of countries, primarily within the liberal and democratic corporatist media systems. Many recently published studies illustrate a growing scholarly interest in data journalism in other parts of the world, but these studies are still limited in number. This special issue brings together five new empirical studies of data journalism around the world, as well as two commentaries and two book reviews on the topic with the aim of broadening the theoretical, empirical, and geographic perspectives on data journalism. The core of the special issue consists of five national and comparative case studies studying data journalism in Africa, the Arab world, Italy, the UK, and Argentina. Combined, these articles and the other publications in this special issue point to three important contextual factors that shape data journalism worldwide: journalistic cultures, media markets, and the political environment. After a discussion of each of these three factors, areas for future research are proposed.

Abstract: The COVID-19 health crisis has been heavily reported on an international scale for several years. This has pushed news journalism in a datafied direction: reporters have learnt how to analyse and visualise the statistical effects of COVID-19 on various sectors of society. As a result, in 2021, the international Sigma Awards competition for data journalism highlighted coverage of the pandemic. Using content analysis with qualitative elements, this paper analyses the shortlisted works covering COVID-19 from the competition (n=73). It focuses on the data references made by the teams – sources, type of both reference and data used – showing statistics from official institutions to be the most used type of data. It also lists the main problems journalists had to face while working on their projects. Most often these problems fell into two categories: specific characteristics of the project, mostly 'time consuming', and issues with data.