Data Journalism

Data Journalism

Andreas A. Veglis (Aristotle University of Thessaloniki, Greece) and Charalampos P. Bratsas (Aristotle University of Thessaloniki, Greece)
Copyright: © 2018 |Pages: 10
DOI: 10.4018/978-1-5225-2255-3.ch103
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Abstract

The introduction of Information Communication Technologies (ICTs) has transformed journalism profession through the digitalization of the work process as well as the introduction of the internet along with its services. Many new types of journalism have emerged, among which data journalism, which require journalists to have special ICT skills. Data journalism is a new form of journalism which has appeared gradually during the previous years, driven by the availability of data in digital form. This article studies the issue of data journalism. Specifically, the article will include a definition of data journalism as well as a discussion on the necessary ICT skills that journalists should have in order to cope with this new type of journalism. These skills are closely associated with the stages of the development of a data journalism project. Also, the relation between data journalism and open data will be presented due the importance of the later in the development of data journalism.
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Introduction

The introduction of ICTs (Information Communication Technologies) had a profound impact on every aspect of human activities. In the case of journalism, the utilization of ICTS has transformed the profession through the digitalization of the work process as well as the introduction of the internet along with its 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, 2017). Many new types of journalism have emerged, among which, data journalism (Gray, Chambers, & Bounegru, 2012), which requires journalists to have special ICT skills.

In the recent years, data journalism has drawn significant attention in the academic literature as well as in the area of new developments in digital news production (Appelgrena & Nygren, 2014; Fink & Anderson, 2015; Mair & Keeble, 2014). Data journalism is considered to be a new 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 (Veglis & Bratsas, 2017). Data Journalism is a journalistic specialty reflecting the increased role of the 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 examines current trends and future perspectives of data journalism. The background section provides historic evolution and definitions of data journalism. Next, the stages of data journalism are presented in detail. Also, the relation between data journalism and open data is discussed due to the importance of the later in the development of data journalism. Finally, recommendations and future research direction are briefly discussed.

Key Terms in this Chapter

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

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.

Dataset: 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 data set organization.

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

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

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

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