Predicting COVID: Understanding audience responses to predictive journalism via online comments

Mowafak Allaham*, Nicholas Diakopoulos

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The COVID-19 pandemic triggered a global health crisis that stimulated journalists to frame their stories around predictive models and forecasts aiming to predict the future trend of the pandemic. This article examines the audience response to predictive journalism by qualitatively analyzing readers’ comments to articles covering COVID that were published in a small sample of mainstream media. Based on a thematic analysis of readers’ comments, this research contributes a typology of audience response types to the models incorporated in such predictive journalism. We elaborate on each of three primary themes—reflecting affective, action-oriented, and evaluative responses—and discuss the implications of our findings and the importance of expanding research to answer questions related to the role of predictive journalism in shaping affective response, encouraging action-oriented responses and collective planning around responsibility for taking future actions, and considering the ways in which supportive and critical comments triggered by the models may be harnessed to improve communication.

Original languageEnglish (US)
JournalNew Media and Society
DOIs
StateAccepted/In press - 2022

Keywords

  • COVID-19
  • data journalism
  • online comments
  • predictive journalism

ASJC Scopus subject areas

  • Communication
  • Sociology and Political Science

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