Data Journalism: Definition, Skills, Difficulties, and Perspectives

Data Journalism: Definition, Skills, Difficulties, and Perspectives

Andreas Veglis (Aristotle University of Thessaloniki, Greece) and Charalampos P. Bratsas (Aristotle University of Thessaloniki, Greece)
Copyright: © 2021 |Pages: 12
DOI: 10.4018/978-1-7998-3479-3.ch079
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Abstract

In recent years, data journalism has drawn significant attention in academic literature as well as in the area of new developments in digital news production. It is a journalistic specialty reflecting the increased role that numerical data has in the production and distribution of information in the digital era. This chapter attempts to describe the state of data journalism today. Specifically, the chapter provides the historic evolution and definition of data journalism and it discusses the stages of data journalism in relation with the necessary corresponding skills of the data journalist. Also, the various difficulties that the evolution of data journalism is facing are presented. Finally, recommendations and future research directions are briefly discussed.
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Introduction

The introduction of ICTs (Information Communication Technologies) at the last decade of the 20th century had a profound impact on almost every aspect of human activities. Journalism was one of the areas where ICTs had a significant impact, since it transformed the profession through the digitalization of the work process as well as the introduction of internet services (Veglis 2009). Today, the journalist is expected to have the ability to firstly employ many tools and services in order to be instantly informed about breaking news as well as current events, and secondly, use a variety of tools and applications in order to prepare and disseminate news articles (Veglis & Bratsas, 2017a). Many new types of journalism have emerged (algorithmic journalism, drone journalism, multimedia journalism, etc. (Diakopoulos, 2019; Ntalakas, Dimoulas, & Veglis & Bratsas, 2017a; 2017b; Tu, 2015)) along with data journalism (Gray, Chambers, & Bounegru, 2012), which require journalists to have special skills.

In recent years, data journalism has drawn significant attention in academic literature as well as in the area of new developments in digital news production (Hermida, & Young, 2017; Loosen, Reimer, & De Silva-Schmidt, 2017; Weber, Engebretsen, & Kennedy, 2018). Data journalism is now considered to be an established form of journalism. It has appeared gradually in the dawn of the new century. Many factors have contributed to the introduction of data journalism, but one of the most prominent is believed to be the availability of data in digital form. Another factor that contributed to its introduction was the availability of visualization and data management tools (Veglis & Bratsas, 2017a). Data Journalism is a journalistic specialty reflecting the increased role that numerical data has in the production and distribution of information in the digital era. Data can be the source of data journalism, and/or it can be the tool with which the story is told (Gray, Chambers, & Bounegru, 2012).

This chapter attempts to describe the state of data journalism today. The background section provides the historic evolution and definitions of data journalism. Next, the stages of data journalism are presented in detail, along with the necessary corresponding skills of the data journalist. Also, the various difficulties that the evolution of data journalism is facing are presented. Finally, recommendations and future research directions are briefly discussed.

Key Terms in this Chapter

Data Cleaning or Data Scrubbing: The process of detecting and correcting corrupted or incorrect records from a dataset.

Data Visualization: The graphical display of abstract information for data analysis and communication purposes.

Data Journalism: The process of extracting useful information from data, writing articles based on the information and embedding visualizations in the articles that help readers understand the significant of the story or allow them to pinpoint data that relate to them.

Open Data: Is data that can be freely used, re-used and redistributed by anyone – subject only, at most, to the requirement to attribute and sharealike.

Dataset: Is a collection of data that contains individual data units organized in a specific way and accessed by a specific access method that is based on the dataset organization.

Data Scraping: The process in which a software tool extracts data from human-readable output that originates from other software.

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