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Vaccine Images on Twitter: Analysis of What Images are Shared

BACKGROUND: Visual imagery plays a key role in health communication; however, there is little understanding of what aspects of vaccine-related images make them effective communication aids. Twitter, a popular venue for discussions related to vaccination, provides numerous images that are shared with...

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Detalles Bibliográficos
Autores principales: Chen, Tao, Dredze, Mark
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5904451/
https://www.ncbi.nlm.nih.gov/pubmed/29615386
http://dx.doi.org/10.2196/jmir.8221
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author Chen, Tao
Dredze, Mark
author_facet Chen, Tao
Dredze, Mark
author_sort Chen, Tao
collection PubMed
description BACKGROUND: Visual imagery plays a key role in health communication; however, there is little understanding of what aspects of vaccine-related images make them effective communication aids. Twitter, a popular venue for discussions related to vaccination, provides numerous images that are shared with tweets. OBJECTIVE: The objectives of this study were to understand how images are used in vaccine-related tweets and provide guidance with respect to the characteristics of vaccine-related images that correlate with the higher likelihood of being retweeted. METHODS: We collected more than one million vaccine image messages from Twitter and characterized various properties of these images using automated image analytics. We fit a logistic regression model to predict whether or not a vaccine image tweet was retweeted, thus identifying characteristics that correlate with a higher likelihood of being shared. For comparison, we built similar models for the sharing of vaccine news on Facebook and for general image tweets. RESULTS: Most vaccine-related images are duplicates (125,916/237,478; 53.02%) or taken from other sources, not necessarily created by the author of the tweet. Almost half of the images contain embedded text, and many include images of people and syringes. The visual content is highly correlated with a tweet’s textual topics. Vaccine image tweets are twice as likely to be shared as nonimage tweets. The sentiment of an image and the objects shown in the image were the predictive factors in determining whether an image was retweeted. CONCLUSIONS: We are the first to study vaccine images on Twitter. Our findings suggest future directions for the study and use of vaccine imagery and may inform communication strategies around vaccination. Furthermore, our study demonstrates an effective study methodology for image analysis.
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spelling pubmed-59044512018-04-24 Vaccine Images on Twitter: Analysis of What Images are Shared Chen, Tao Dredze, Mark J Med Internet Res Original Paper BACKGROUND: Visual imagery plays a key role in health communication; however, there is little understanding of what aspects of vaccine-related images make them effective communication aids. Twitter, a popular venue for discussions related to vaccination, provides numerous images that are shared with tweets. OBJECTIVE: The objectives of this study were to understand how images are used in vaccine-related tweets and provide guidance with respect to the characteristics of vaccine-related images that correlate with the higher likelihood of being retweeted. METHODS: We collected more than one million vaccine image messages from Twitter and characterized various properties of these images using automated image analytics. We fit a logistic regression model to predict whether or not a vaccine image tweet was retweeted, thus identifying characteristics that correlate with a higher likelihood of being shared. For comparison, we built similar models for the sharing of vaccine news on Facebook and for general image tweets. RESULTS: Most vaccine-related images are duplicates (125,916/237,478; 53.02%) or taken from other sources, not necessarily created by the author of the tweet. Almost half of the images contain embedded text, and many include images of people and syringes. The visual content is highly correlated with a tweet’s textual topics. Vaccine image tweets are twice as likely to be shared as nonimage tweets. The sentiment of an image and the objects shown in the image were the predictive factors in determining whether an image was retweeted. CONCLUSIONS: We are the first to study vaccine images on Twitter. Our findings suggest future directions for the study and use of vaccine imagery and may inform communication strategies around vaccination. Furthermore, our study demonstrates an effective study methodology for image analysis. JMIR Publications 2018-04-03 /pmc/articles/PMC5904451/ /pubmed/29615386 http://dx.doi.org/10.2196/jmir.8221 Text en ©Tao Chen, Mark Dredze. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 03.04.2018. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Chen, Tao
Dredze, Mark
Vaccine Images on Twitter: Analysis of What Images are Shared
title Vaccine Images on Twitter: Analysis of What Images are Shared
title_full Vaccine Images on Twitter: Analysis of What Images are Shared
title_fullStr Vaccine Images on Twitter: Analysis of What Images are Shared
title_full_unstemmed Vaccine Images on Twitter: Analysis of What Images are Shared
title_short Vaccine Images on Twitter: Analysis of What Images are Shared
title_sort vaccine images on twitter: analysis of what images are shared
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5904451/
https://www.ncbi.nlm.nih.gov/pubmed/29615386
http://dx.doi.org/10.2196/jmir.8221
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