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Diabetes Topics Associated With Engagement on Twitter

INTRODUCTION: Social media are widely used by the general public and by public health and health care professionals. Emerging evidence suggests engagement with public health information on social media may influence health behavior. However, the volume of data accumulating daily on Twitter and other...

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Autores principales: Harris, Jenine K., Mart, Adelina, Moreland-Russell, Sarah, Caburnay, Charlene A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Centers for Disease Control and Prevention 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4436046/
https://www.ncbi.nlm.nih.gov/pubmed/25950569
http://dx.doi.org/10.5888/pcd12.140402
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author Harris, Jenine K.
Mart, Adelina
Moreland-Russell, Sarah
Caburnay, Charlene A.
author_facet Harris, Jenine K.
Mart, Adelina
Moreland-Russell, Sarah
Caburnay, Charlene A.
author_sort Harris, Jenine K.
collection PubMed
description INTRODUCTION: Social media are widely used by the general public and by public health and health care professionals. Emerging evidence suggests engagement with public health information on social media may influence health behavior. However, the volume of data accumulating daily on Twitter and other social media is a challenge for researchers with limited resources to further examine how social media influence health. To address this challenge, we used crowdsourcing to facilitate the examination of topics associated with engagement with diabetes information on Twitter. METHODS: We took a random sample of 100 tweets that included the hashtag “#diabetes” from each day during a constructed week in May and June 2014. Crowdsourcing through Amazon’s Mechanical Turk platform was used to classify tweets into 9 topic categories and their senders into 3 Twitter user categories. Descriptive statistics and Tweedie regression were used to identify tweet and Twitter user characteristics associated with 2 measures of engagement, “favoriting” and “retweeting.” RESULTS: Classification was reliable for tweet topics and Twitter user type. The most common tweet topics were medical and nonmedical resources for diabetes. Tweets that included information about diabetes-related health problems were positively and significantly associated with engagement. Tweets about diabetes prevalence, nonmedical resources for diabetes, and jokes or sarcasm about diabetes were significantly negatively associated with engagement. CONCLUSION: Crowdsourcing is a reliable, quick, and economical option for classifying tweets. Public health practitioners aiming to engage constituents around diabetes may want to focus on topics positively associated with engagement.
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spelling pubmed-44360462015-06-02 Diabetes Topics Associated With Engagement on Twitter Harris, Jenine K. Mart, Adelina Moreland-Russell, Sarah Caburnay, Charlene A. Prev Chronic Dis Original Research INTRODUCTION: Social media are widely used by the general public and by public health and health care professionals. Emerging evidence suggests engagement with public health information on social media may influence health behavior. However, the volume of data accumulating daily on Twitter and other social media is a challenge for researchers with limited resources to further examine how social media influence health. To address this challenge, we used crowdsourcing to facilitate the examination of topics associated with engagement with diabetes information on Twitter. METHODS: We took a random sample of 100 tweets that included the hashtag “#diabetes” from each day during a constructed week in May and June 2014. Crowdsourcing through Amazon’s Mechanical Turk platform was used to classify tweets into 9 topic categories and their senders into 3 Twitter user categories. Descriptive statistics and Tweedie regression were used to identify tweet and Twitter user characteristics associated with 2 measures of engagement, “favoriting” and “retweeting.” RESULTS: Classification was reliable for tweet topics and Twitter user type. The most common tweet topics were medical and nonmedical resources for diabetes. Tweets that included information about diabetes-related health problems were positively and significantly associated with engagement. Tweets about diabetes prevalence, nonmedical resources for diabetes, and jokes or sarcasm about diabetes were significantly negatively associated with engagement. CONCLUSION: Crowdsourcing is a reliable, quick, and economical option for classifying tweets. Public health practitioners aiming to engage constituents around diabetes may want to focus on topics positively associated with engagement. Centers for Disease Control and Prevention 2015-05-07 /pmc/articles/PMC4436046/ /pubmed/25950569 http://dx.doi.org/10.5888/pcd12.140402 Text en https://creativecommons.org/licenses/by/4.0/This is a publication of the U.S. Government. This publication is in the public domain and is therefore without copyright. All text from this work may be reprinted freely. Use of these materials should be properly cited.
spellingShingle Original Research
Harris, Jenine K.
Mart, Adelina
Moreland-Russell, Sarah
Caburnay, Charlene A.
Diabetes Topics Associated With Engagement on Twitter
title Diabetes Topics Associated With Engagement on Twitter
title_full Diabetes Topics Associated With Engagement on Twitter
title_fullStr Diabetes Topics Associated With Engagement on Twitter
title_full_unstemmed Diabetes Topics Associated With Engagement on Twitter
title_short Diabetes Topics Associated With Engagement on Twitter
title_sort diabetes topics associated with engagement on twitter
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4436046/
https://www.ncbi.nlm.nih.gov/pubmed/25950569
http://dx.doi.org/10.5888/pcd12.140402
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