Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Unleashing Data Journalism's Potential: COVID-19 as Catalyst for Newsroom Transformation

Benedict Witzenberger, Jürgen Pfeffer

TL;DR

This study investigates how the COVID-19 pandemic acted as a catalyst for data journalism in German newsrooms. Using byline data and a combination of chi-square tests, negative binomial regression, and Relational hyperevent models within a Communities of Practice framework, it quantifies changes in outputs and collaboration networks. It finds a substantial rise in data-journalistic articles—especially within science departments—and a shift toward greater participation of science journalists in data reporting, alongside evolving co-authorship patterns. These results illuminate the transformational role of data journalism during crisis reporting and provide a quantitative lens for understanding newsroom collaboration in computational communication science.

Abstract

In the context of journalism, the COVID-19 pandemic brought unprecedented challenges, necessitating rapid adaptations in newsrooms. Data journalism emerged as a pivotal approach for effectively conveying complex information to the public. Here, we show the profound impact of COVID-19 on data journalism, revealing a surge in data-driven publications and heightened collaboration between data and science journalists. Employing a quantitative methodology, including negative binomial regression and Relational hyperevent models (RHEM), on byline data of articles co-authored by data journalists, we comprehensively analyze data journalism outputs, authorship trends, and collaboration networks to address five key research questions. The findings reveal a significant increase in data journalistic pieces during and after the pandemic, in particular with a rise in publications within scientific departments. Collaborative efforts among data and science journalists intensified, evident through increased authorship and co-authorship trends. Prior common authorship experiences somewhat influenced the likelihood of future co-authorships, underscoring the importance of building collaborative communities of practice. These quantitative insights provide an understanding of the transformational role of data journalism during COVID-19, contributing to the growing body of literature in computational communication science and journalism practice.

Unleashing Data Journalism's Potential: COVID-19 as Catalyst for Newsroom Transformation

TL;DR

This study investigates how the COVID-19 pandemic acted as a catalyst for data journalism in German newsrooms. Using byline data and a combination of chi-square tests, negative binomial regression, and Relational hyperevent models within a Communities of Practice framework, it quantifies changes in outputs and collaboration networks. It finds a substantial rise in data-journalistic articles—especially within science departments—and a shift toward greater participation of science journalists in data reporting, alongside evolving co-authorship patterns. These results illuminate the transformational role of data journalism during crisis reporting and provide a quantitative lens for understanding newsroom collaboration in computational communication science.

Abstract

In the context of journalism, the COVID-19 pandemic brought unprecedented challenges, necessitating rapid adaptations in newsrooms. Data journalism emerged as a pivotal approach for effectively conveying complex information to the public. Here, we show the profound impact of COVID-19 on data journalism, revealing a surge in data-driven publications and heightened collaboration between data and science journalists. Employing a quantitative methodology, including negative binomial regression and Relational hyperevent models (RHEM), on byline data of articles co-authored by data journalists, we comprehensively analyze data journalism outputs, authorship trends, and collaboration networks to address five key research questions. The findings reveal a significant increase in data journalistic pieces during and after the pandemic, in particular with a rise in publications within scientific departments. Collaborative efforts among data and science journalists intensified, evident through increased authorship and co-authorship trends. Prior common authorship experiences somewhat influenced the likelihood of future co-authorships, underscoring the importance of building collaborative communities of practice. These quantitative insights provide an understanding of the transformational role of data journalism during COVID-19, contributing to the growing body of literature in computational communication science and journalism practice.
Paper Structure (15 sections, 3 figures, 3 tables)

This paper contains 15 sections, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. COVID-19 and Data Journalism
  3. Communities of Practice
  4. Using Bylines to Study Journalism
  5. Research Questions
  6. Materials and Methods
  7. Data
  8. Methods
  9. Results
  10. Some increase in articles, but not in authors
  11. Science department becomes data journalistic
  12. Authorship collaborations change
  13. Discussion
  14. Limitations
  15. Conclusion

Figures (3)

  • Figure 1: Data model of network analysis showing an exemplary two-mode network between one or more authors connected to one article.
  • Figure 2: Comparing monthly number of publications (Fig. \ref{['fig:monthly_publications_lockdowns']}) and authorships (Fig. \ref{['fig:monthly_authorships_lockdowns']}).
  • Figure 3: Absolute counts of data-journalistic articles per media pre- and post-COVID-19.