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Journal

Central European Journal of Communication

2022 | 15 | 3(32) | 379-395

Article title

Fighting COVID-19 with data: An analysis of data journalism projects submitted to Sigma Awards 2021

Authors

Liis Auväärt

Content

Full texts:
articles-2176108.pdf.pdf

Title variants

Languages of publication

Abstracts

EN
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.

Keywords

EN

Publisher

Polskie Towarzystwo Komunikacji Społecznej

Journal

Central European Journal of Communication

Year

2022

Volume

15

Issue

3(32)

Pages

379-395

Physical description

Dates

published
2022

Contributors

author
Liis Auväärt
  • University of Tartu

References

  • Beiler, M., Irmer, F., & Breda, A. (2020). Data Journalism at German Newspapers and Public Broadcasters: A Quantitative Survey of Structures, Contents and Perceptions. Journalism Studies, 21, 1–19. https://doi.org/10.1080/1461670X.2020.1772855
  • Borges-Rey, E. (2020). Towards an epistemology of data journalism in the devolved nations of the United Kingdom: Changes and continuities in materiality, performativity and reflexivity. Journalism, 21(7), 915–932. https://doi.org/10.1177/1464884917693864
  • Casero-Ripolles, A. (2020). Impact of Covid-19 on the media system. Communicative and democratic consequences of news consumption during the outbreak. El Profesional de La Información, 29(2). https://doi.org/10.3145/epi.2020.mar.23
  • Catalan-Matamoros, D., & Elías, C. (2020). Vaccine Hesitancy in the Age of Coronavirus and Fake News: Analysis of Journalistic Sources in the Spanish Quality Press. International Journal of Environmental Research and Public Health, 17(21), 8136. https://doi.org/10.3390/ijerph17218136
  • Chaparro-Domínguez, M.-Á., & Díaz-Campo, J. (2021). Data Journalism and Ethics: Best Practices in the Winning Projects (DJA, OJA and Sigma Awards). Journalism Practice. Scopus. https://doi.org/10.1080/17512786.2021.1981773
  • Cushion, S., Lewis, J., Sambrook, R., & Callaghan, R. (2016). Impartiality Review: BBC Reporting of Statistics Report on qualitative research with the BBC audience. http://downloads.bbc.co.uk/bbctrust/assets/files/pdf/our_work/stats_impartiality/content_analysis.pdf
  • Dong, E., Du, H., & Gardner, L. (2020). An interactive web-based dashboard to track COVID-19 in real time. The Lancet Infectious Diseases, 20(5), 533–534. https://doi.org/10.1016/S1473-3099(20)30120-1
  • Engebretsen, M., Kennedy, H., & Weber, W. (2018). Data visualisation in Scandinavian newsrooms: Emerging trends in journalistic visualisation practices. Nordicom Review.
  • Gray, J., Gerlitz, C., & Bounegru, L. (2018). Data infrastructure literacy. Big Data & Society, 5(2), 205395171878631. https://doi.org/10.1177/2053951718786316
  • Hanusch, F. (2022). Change and Continuity in Digital Journalism: The Covid-19 Pandemic as Situational Context for Broader Arguments about the Field. Digital Journalism, 10(6), 1135–1140. https://doi.org/10.1080/21670811.2022.2092020
  • Johns Hopkins University. (2022). Johns Hopkins Coronavirus Resource Center. https://coronavirus.jhu.edu/about
  • Jukes, S., Fowler-Watt, K., & Rees, G. (2021). Reporting the Covid-19 Pandemic: Trauma on Our Own Doorstep. Digital Journalism, 0(0), 1–18. https://doi.org/10.1080/21670811.2021.1965489
  • Koch, T. (2021). Welcome to the revolution: COVID-19 and the democratization of spatial-temporal data. Patterns, 2(7), 100272. https://doi.org/10.1016/j.patter.2021.100272
  • Korn, M. (2020). 2020’s Go-To Data Source: Johns Hopkins Coronavirus Resource Center. Time. https://time.com/collection/best-inventions-2020/5911434/johns-hopkins-coronavirus-resource-center/
  • Krippendorff, K. (2018). Content Analysis: An Introduction to Its Methodology. SAGE Publications.
  • Lacy, S., Watson, B. R., Riffe, D., & Lovejoy, J. (2015). Issues and Best Practices in Content Analysis. Journalism & Mass Communication Quarterly, 92(4), 791–811. https://doi.org/10.1177/1077699015607338
  • Lewis, S. C. (2020). The Objects and Objectives of Journalism Research During the Coronavirus Pandemic and Beyond. Digital Journalism, 8(5), 681–689. https://doi.org/10.1080/21670811.2020.1773292
  • Loosen, W., Reimer, J., & De Silva-Schmidt, F. (2020). Data-driven reporting: An on-going (r)evolution? An analysis of projects nominated for the Data Journalism Awards 2013–2016. Journalism, 21(9), 1246–1263. Scopus. https://doi.org/10.1177/1464884917735691
  • MacDonald, N. E. (2021). COVID-19, public health and constructive journalism in Canada. Canadian Journal of Public Health, 112(2), 179–182. https://doi.org/10.17269/s41997-021-00494-8
  • Makri, A. (2021). What do journalists say about covering science during the COVID-19 pandemic? Nature Medicine, 27(1), 17–20. https://doi.org/10.1038/s41591-020-01207-3
  • Marconi, F. (2020). Newsmakers: Artificial Intelligence and the Future of Journalism. Columbia University Press.
  • Mayer-Schönberger, V., & Cukier, K. (2013). Big Data: A Revolution that Will Transform how We Live, Work, and Think. Houghton Mifflin Harcourt.
  • Nisbet, M. C., & Fahy, D. (2015). The Need for Knowledge-Based Journalism in Politicized Science Debates. The ANNALS of the American Academy of Political and Social Science, 658(1), 223–234. https://doi.org/10.1177/0002716214559887
  • OECD. (2016). Skills Matter: Further Results from the Survey of Adult Skills. OECD. https://doi.org/10.1787/9789264258051-en
  • Ojo, A., & Heravi, B. (2018). Patterns in Award Winning Data Storytelling. Digital Journalism, 6(6), 693–718. https://doi.org/10.1080/21670811.2017.1403291
  • Parasie, S., & Dagiral, E. (2013). Data-driven journalism and the public good: “Computer-assisted-reporters” and “programmer-journalists” in Chicago. New Media & Society, 15(6), 853–871. https://doi.org/10.1177/1461444812463345
  • Pentzold, C., Fechner, D. J., & Zuber, C. (2021). “Flatten the Curve”: Data-Driven Projections and the Journalistic Brokering of Knowledge during the COVID-19 Crisis. Digital Journalism, 0(0), 1–24. https://doi.org/10.1080/21670811.2021.1950018
  • Porlezza, C., & Splendore, S. (2019). From Open Journalism to Closed Data: Data Journalism in Italy. Digital Journalism, 7, 1–23. https://doi.org/10.1080/21670811.2019.1657778
  • Quandt, T., & Wahl-Jorgensen, K. (2022). The Coronavirus Pandemic and the Transformation of (Digital) Journalism. Digital Journalism, 10(6), 923–929. https://doi.org/10.1080/21670811.2022.2090018
  • Royal, C. (2012). The Journalist as Programmer: A Case Study of The New York Times Interactive News Technology Department. ISOJ Journal, Volume 2, Number 1, Spring, 5–24.
  • Stalph, F. (2018). Classifying Data Journalism. A Content Analysis of Data-Driven Stories. Journalism Practice, Vol. 12, No. 10, 1332–1350. https://doi.org/10.1080/17512786.2017.1386583
  • Stephan, M., Register, J., Reinke, L., Robinson, C., Pugalenthi, P., & Pugalee, D. (2021). People use math as a weapon: Critical mathematics consciousness in the time of COVID-19. Educational Studies in Mathematics. https://doi.org/10.1007/s10649-021-10062-z
  • Stoto, M. A., Woolverton, A., Kraemer, J., Barlow, P., & Clarke, M. (2022). COVID-19 data are messy: Analytic methods for rigorous impact analyses with imperfect data. Globalization and Health, 18(1), 2. https://doi.org/10.1186/s12992-021-00795-0
  • The Sigma Awards database. (2023). GitHub. https://github.com/Sigma-Awards
  • Van Witsen, A. (2020). How Daily Journalists Use Numbers and Statistics: The Case of Global Average Temperature. Journalism Practice, 14(9), 1047–1065. Scopus. https://doi.org/10.1080/17512786.2019.1682944
  • Widholm, A., & Appelgren, E. (2020). A softer kind of hard news? Data journalism and the digital renewal of public service news in Sweden. New Media & Society, 1461444820975411. https://doi.org/10.1177/1461444820975411
  • Wihbey, J. (2017). Journalists’ Use of Knowledge in an Online World: Examining reporting habits, sourcing practices and institutional norms. Journalism Practice, 11(10), 1267–1282. Scopus. https://doi.org/10.1080/17512786.2016.1249004
  • Young, M. L., Hermida, A., & Fulda, J. (2018). What Makes for Great Data Journalism? Journalism Practice, 12(1), 115–135. https://doi.org/10.1080/17512786.2016.1270171
  • Zhang, S., & Wang, Q. (2022). Refracting the Pandemic: A Field Theory Approach to Chinese Journalists’ Sourcing Options in the Age of COVID-19. Digital Journalism, 10(6), 1115–1134. https://doi.org/10.1080/21670811.2022.2029521

Document Type

Publication order reference

Identifiers

DOI
10.51480/1899-5101.15.3(32).3
Biblioteka Nauki
2176108

YADDA identifier

bwmeta1.element.ojs-doi-10_51480_1899-5101_15_3_32__3
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