TY - JOUR
T1 - Content and source analysis of popular tweets following a recent case of diphtheria in Spain
AU - Porat, Talya
AU - Garaizar, Pablo
AU - Ferrero, Marta
AU - Jones, Hilary
AU - Ashworth, Mark
AU - Vadillo, Miguel A.
N1 - Publisher Copyright:
© The Author(s) 2018. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved.
PY - 2019/2/1
Y1 - 2019/2/1
N2 - Despite major progress in global vaccination coverage, immunization rates are falling, resulting in outbreaks of vaccine-preventable diseases. This study analyses content and source of the most popular tweets related to a recent case in Spain where an unvaccinated child contracted and later died from diphtheria. Understanding the characteristics of these tweets in the context of vaccination could inform efforts by health promotion professionals to increase their reach and impact. We extracted tweets containing keywords related to the diphtheria case (from 1 May to 15 July 2015). We explored the prevalence of terms relating to policy and misinformation and manually coded the 194 most popular tweets (retweeted 100 or more times) with regard to source, topic, tone and sentiment. A total of 722 974 tweets were collected. Prevalence of terms relating to policy and misinformation increased at the onset of the case and after the death of the child. Popular tweets (194) were either pro-vaccination (58%) or neutral, with none classified as anti-vaccination. Popular topics included criticism towards anti-vaccination groups (35%) and effectiveness of immunization (22%). Popular tweets were informative (47%) or opinions (53%), which mainly expressed frustration (24%) or humour/sarcasm (23%). Popular Twitter accounts were newspaper and TV channels (15%), as well as individual journalists and authors of popular science (13.4%). Healthcare organizations could collaborate with popular journalists or news outlets and employ authors of popular science to disseminate health information on social media, while addressing public concerns and misinformation in accessible ways.
AB - Despite major progress in global vaccination coverage, immunization rates are falling, resulting in outbreaks of vaccine-preventable diseases. This study analyses content and source of the most popular tweets related to a recent case in Spain where an unvaccinated child contracted and later died from diphtheria. Understanding the characteristics of these tweets in the context of vaccination could inform efforts by health promotion professionals to increase their reach and impact. We extracted tweets containing keywords related to the diphtheria case (from 1 May to 15 July 2015). We explored the prevalence of terms relating to policy and misinformation and manually coded the 194 most popular tweets (retweeted 100 or more times) with regard to source, topic, tone and sentiment. A total of 722 974 tweets were collected. Prevalence of terms relating to policy and misinformation increased at the onset of the case and after the death of the child. Popular tweets (194) were either pro-vaccination (58%) or neutral, with none classified as anti-vaccination. Popular topics included criticism towards anti-vaccination groups (35%) and effectiveness of immunization (22%). Popular tweets were informative (47%) or opinions (53%), which mainly expressed frustration (24%) or humour/sarcasm (23%). Popular Twitter accounts were newspaper and TV channels (15%), as well as individual journalists and authors of popular science (13.4%). Healthcare organizations could collaborate with popular journalists or news outlets and employ authors of popular science to disseminate health information on social media, while addressing public concerns and misinformation in accessible ways.
UR - http://www.scopus.com/inward/record.url?scp=85060587260&partnerID=8YFLogxK
U2 - 10.1093/eurpub/cky144
DO - 10.1093/eurpub/cky144
M3 - Article
C2 - 30084926
AN - SCOPUS:85060587260
SN - 1101-1262
VL - 29
SP - 117
EP - 122
JO - European Journal of Public Health
JF - European Journal of Public Health
IS - 1
ER -