“How Trustworthy Is This Research?” Designing a Tool to Help Readers Understand Evidence and Uncertainty in Science Journalism

Dados Bibliográficos

ID22010695
AutoresAnders Sundnes Løvlie (0000-0003-0484-4668, IT University of Copenhagen, Copenhagen, Denmark), Astrid Waagstein (University of Copenhagen, Copenhagen, Denmark), Peter Hyldgård (ScienceCom.dk, Copenhagen, Denmark)
Ano2023
Volume11
Fascículo3
Páginas431-464
Data de publicação2023-03-16
Peer ReviewedSim
Open AccessNão
TipoARTICLE
PeriódicoDigital Journalism (JOURNAL)
Identificadores do periódicoISSN: 2167-0811 • E-ISSN: 2167-082X
EditoraInforma UK Limited (PUBLISHER • GB)
DOI10.1080/21670811.2023.2193344
OpenAlexW4287155300
IdiomaEN
Citações recebidas6
Referências citadas78

This article reports on a Research through Design study exploring how to design a tool for helping readers of science journalism understand the strength and uncertainty of scientific evidence in news stories about health science, using both textual and visual information. A central aim has been to teach readers about criteria for assessing scientific evidence, in particular in order to help readers differentiate between science and pseudoscience. Working in a research-in-the-wild collaboration with a website for popular science, the study presents the design and evaluation of the Scientific Evidence Indicator, which uses metadata about scientific publications to present an assessment of evidence strength to the readers. Evaluations of the design demonstrate some success in helping readers recognize whether studies have undergone scientific peer review or not, but point to challenges in facilitating a more in-depth understanding. Insights from the study point to a potential for developing similar tools aimed at journalists rather than directly at audiences

Data science · Engineering ethics · Epistemology · Internet privacy · Journalism · Media studies · Metadata · Pseudoscience · Scientific evidence · Sociology · Trustworthiness · World Wide Web · Climate Change Communication and Perception · Computer Science · Data Visualization and Analytics · Digital Storytelling and Education · Engineering · Psychology