Unleashing Data Journalism's Potential: COVID-19 as Catalyst for Newsroom Transformation
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| author | Witzenberger, Benedict Pfeffer, Jürgen |
| author_facet | Witzenberger, Benedict Pfeffer, Jürgen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_14816 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Unleashing Data Journalism's Potential: COVID-19 as Catalyst for Newsroom Transformation Witzenberger, Benedict Pfeffer, Jürgen Social and Information Networks Applications J.5; H.3.3; H.5.4 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. |
| title | Unleashing Data Journalism's Potential: COVID-19 as Catalyst for Newsroom Transformation |
| topic | Social and Information Networks Applications J.5; H.3.3; H.5.4 |
| url | https://arxiv.org/abs/2401.14816 |