Choosing the perfect shot – the loaded narrative of imagery in online news coverage of vaccines

AG Wu, AS Shah, TS Haelle, SA Lunos, MB Pitt

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Images in health communication have been shown to affect perspectives and attitudes towards health issues including vaccination. We seek to quantify the frequency of images used in online news coverage of vaccines that may convey varying sentiments about vaccination. To capture a breadth of vaccine-related news coverage, including international sources, we searched the following terms in Google News Archives: “autism and vaccine”, “flu and vaccine”, and “measles and Disneyland”. We developed a coding tool that classified images as negative (eg, screaming child), positive (eg, happy child), neutral (eg, vaccine vial), or irrelevant (eg, picture of journalist). All images were coded independently by two researchers and discussed for consensus. We analyzed 734 images. Of the images which featured vaccines and/or a medical encounter (322), 28% had negative features and 30% had positive features. The remaining 137 images (43%) were neutral. There was no statistically significant difference between proportions of negative and positive imagery for each pair of search terms, which may be a reflection of random image selection. Ultimately, nearly one in eight images included in vaccine-related news coverage contains negative features which may be selected without careful consideration of the potential negative impact on public health initiatives regarding vaccination.

Original languageEnglish (US)
Article numbere0199870
JournalPloS one
Volume13
Issue number6
DOIs
StatePublished - Jun 2018

Bibliographical note

Publisher Copyright:
© 2018 Wu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.

PubMed: MeSH publication types

  • Journal Article

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