↓ Skip to main content

PLOS

When Pictures Waste a Thousand Words: Analysis of the 2009 H1N1 Pandemic on Television News

Overview of attention for article published in PLOS ONE, May 2013
Altmetric Badge

Mentioned by

news
1 news outlet
twitter
14 X users
facebook
2 Facebook pages

Citations

dimensions_citation
16 Dimensions

Readers on

mendeley
84 Mendeley
Title
When Pictures Waste a Thousand Words: Analysis of the 2009 H1N1 Pandemic on Television News
Published in
PLOS ONE, May 2013
DOI 10.1371/journal.pone.0064070
Pubmed ID
Authors

Westerly Luth, Cindy Jardine, Tania Bubela

Abstract

Effective communication by public health agencies during a pandemic promotes the adoption of recommended health behaviours. However, more information is not always the solution. Rather, attention must be paid to how information is communicated. Our study examines the television news, which combines video and audio content. We analyse (1) the content of television news about the H1N1 pandemic and vaccination campaign in Alberta, Canada; (2) the extent to which television news content conveyed key public health agency messages; (3) the extent of discrepancies in audio versus visual content.

X Demographics

X Demographics

The data shown below were collected from the profiles of 14 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 84 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 2%
United States 1 1%
Denmark 1 1%
Unknown 80 95%

Demographic breakdown

Readers by professional status Count As %
Student > Master 17 20%
Researcher 12 14%
Student > Ph. D. Student 9 11%
Student > Bachelor 8 10%
Professor 4 5%
Other 12 14%
Unknown 22 26%
Readers by discipline Count As %
Medicine and Dentistry 13 15%
Social Sciences 10 12%
Nursing and Health Professions 6 7%
Arts and Humanities 5 6%
Computer Science 4 5%
Other 18 21%
Unknown 28 33%