Literature DB >> 30476105

World Pneumonia Day 2011-2016: Twitter contents and retweets.

Md Mohiuddin Adnan1, Jingjing Yin1, Ashley M Jackson1, Zion Tsz Ho Tse2, Hai Liang3, King-Wa Fu4,5, Nitin Saroha6, Benjamin M Althouse7,8,9, Isaac Chun-Hai Fung1.   

Abstract

BACKGROUND: Twitter is used for World Pneumonia Day (WPD; November 12) communication. We evaluate if themes of #pneumonia tweets were associated with retweet frequency.
METHODS: A total of 28 181 original #pneumonia tweets were retrieved (21 November 2016), from which six subcorpora, 1 mo before and 1 mo after WPD 2011-2016, were extracted (n=6721). Underlying topics were identified via latent Dirichlet allocation and were manually coded into themes. The association of themes with retweet count was assessed via multivariable hurdle regression.
RESULTS: Compared with personal experience tweets, tweets that both raised awareness and promoted intervention were 2.62 times as likely to be retweeted (adjusted odds ratio [aOR] 2.62 [95% 1.79 to 3.85]) and if retweeted had 37% more retweets (adjusted prevalence ratio [aPR] 1.37 [95% CI 1.06 to 1.78]). Tweets that raised concerns about vaccine price were twice as likely to be retweeted (aOR 2.29 [95% CI 1.36 to 3.84]) and if retweeted, had double the retweet count (aPR 2.05 [95% CI 1.27 to 3.29]) of tweets sharing personal experience.
CONCLUSIONS: The #pneumonia tweets that both raised awareness and promoted interventions and those discussing vaccine price were more likely to engage users than tweets about personal experience. These results help health professionals craft WPD messages that will engage the audience.
© The Author(s) 2018. Published by Oxford University Press on behalf of Royal Society of Tropical Medicine and Hygiene. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Entities:  

Keywords:  health communication; health marketing; machine learning; manual coding; social media

Year:  2019        PMID: 30476105     DOI: 10.1093/inthealth/ihy087

Source DB:  PubMed          Journal:  Int Health        ISSN: 1876-3405            Impact factor:   2.473


  3 in total

1.  Categorising patient concerns using natural language processing techniques.

Authors:  Paul Fairie; Zilong Zhang; Adam G D'Souza; Tara Walsh; Hude Quan; Maria J Santana
Journal:  BMJ Health Care Inform       Date:  2021-06

Review 2.  Promoting the use of social networks in pneumonia.

Authors:  Catia Cillóniz; Leith Greenslade; Cristina Dominedò; Carolina Garcia-Vidal
Journal:  Pneumonia (Nathan)       Date:  2020-05-25

3.  Aggregating Twitter Text through Generalized Linear Regression Models for Tweet Popularity Prediction and Automatic Topic Classification.

Authors:  Chen Mo; Jingjing Yin; Isaac Chun-Hai Fung; Zion Tsz Ho Tse
Journal:  Eur J Investig Health Psychol Educ       Date:  2021-11-26
  3 in total

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