Literature DB >> 27840012

Evaluation of a multinational, multilingual vaccine debate on Twitter.

Benedikt F H Becker1, Heidi J Larson2, Jan Bonhoeffer3, Erik M van Mulligen4, Jan A Kors4, Miriam C J M Sturkenboom4.   

Abstract

BACKGROUND: Public confidence in an immunization programme is a pivotal determinant of the programme's success. The mining of social media is increasingly employed to provide insight into the public's sentiment. This research further explores the value of monitoring social media to understand public sentiment about an international vaccination programme.
OBJECTIVE: To gain insight into international public discussion on the paediatric pentavalent vaccine (DTP-HepB-Hib) programme by analysing Twitter messages.
METHODS: Using a multilingual search, we retrospectively collected all public Twitter messages mentioning the DTP-HepB-Hib vaccine from July 2006 until May 2015. We analysed message characteristics by frequency of referencing other websites, type of websites, and geographic focus of the discussion. In addition, a sample of messages was manually annotated for positive or negative message tone.
RESULTS: We retrieved 5771 messages. Only 3.1% of the messages were reactions to other messages, and 86.6% referred to websites, mostly news sites (70.7%), other social media (9.8%), and health-information sites (9.5%). Country mentions were identified in 70.4% of the messages, of which India (35.4%), Indonesia (18.3%), and Vietnam (13.9%) were the most prevalent. In the annotated sample, 63% of the messages showed a positive or neutral sentiment about DTP-HepB-Hib. Peaks in negative and positive messages could be related to country-specific programme events.
CONCLUSIONS: Public messages about DTP-HepB-Hib were characterized by little interaction between tweeters, and by frequent referencing of websites and other information links. Twitter messages can indirectly reflect the public's opinion about major events in the debates about the DTP-HepB-Hib vaccine. Copyright Â
© 2016 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Multilingual analysis; Pentavalent vaccine; Social media; Vaccination programme; Vaccine debate

Mesh:

Substances:

Year:  2016        PMID: 27840012     DOI: 10.1016/j.vaccine.2016.11.007

Source DB:  PubMed          Journal:  Vaccine        ISSN: 0264-410X            Impact factor:   3.641


  11 in total

1.  Facebook and Twitter vaccine sentiment in response to measles outbreaks.

Authors:  Michael S Deiner; Cherie Fathy; Jessica Kim; Katherine Niemeyer; David Ramirez; Sarah F Ackley; Fengchen Liu; Thomas M Lietman; Travis C Porco
Journal:  Health Informatics J       Date:  2017-11-17       Impact factor: 2.681

2.  Public Response to Obamacare on Twitter.

Authors:  Matthew A Davis; Kai Zheng; Yang Liu; Helen Levy
Journal:  J Med Internet Res       Date:  2017-05-26       Impact factor: 5.428

3.  Use of Deep Learning to Analyze Social Media Discussions About the Human Papillomavirus Vaccine.

Authors:  Jingcheng Du; Chongliang Luo; Ross Shegog; Jiang Bian; Rachel M Cunningham; Julie A Boom; Gregory A Poland; Yong Chen; Cui Tao
Journal:  JAMA Netw Open       Date:  2020-11-02

Review 4.  Methods for Social Media Monitoring Related to Vaccination: Systematic Scoping Review.

Authors:  Emilie Karafillakis; Sam Martin; Clarissa Simas; Kate Olsson; Judit Takacs; Sara Dada; Heidi Jane Larson
Journal:  JMIR Public Health Surveill       Date:  2021-02-08

5.  Prevalence of Health Misinformation on Social Media: Systematic Review.

Authors:  Victor Suarez-Lledo; Javier Alvarez-Galvez
Journal:  J Med Internet Res       Date:  2021-01-20       Impact factor: 5.428

6.  [Content Themes and Influential Voices Within Vaccine Opposition on Twitter, 2019].

Authors:  Erika Bonnevie; Jaclyn Goldbarg; Allison K Gallegos-Jeffry; Sarah D Rosenberg; Ellen Wartella; Joe Smyser
Journal:  Rev Panam Salud Publica       Date:  2021-05-12

7.  Beyond fragmentary: A proposed measure for travel vaccination concerns.

Authors:  Charles Atanga Adongo; Edem Kwesi Amenumey; Akwasi Kumi-Kyereme; Eve Dubé
Journal:  Tour Manag       Date:  2020-09-13

8.  Public Perception of Artificial Intelligence in Medical Care: Content Analysis of Social Media.

Authors:  Shuqing Gao; Lingnan He; Yue Chen; Dan Li; Kaisheng Lai
Journal:  J Med Internet Res       Date:  2020-07-13       Impact factor: 5.428

9.  Characterizing News Report of the Substandard Vaccine Case of Changchun Changsheng in China: A Text Mining Approach.

Authors:  Ping Zhou; Yao He; Chao Lyu; Xiaoguang Yang
Journal:  Vaccines (Basel)       Date:  2020-11-17

10.  Analysis of vaccine messages on social media (Twitter) in Scandinavia.

Authors:  H Fues Wahl; B Wikman Erlandson; C Sahlin; M Nyaku; G Benĉina
Journal:  Hum Vaccin Immunother       Date:  2022-02-01       Impact factor: 3.452

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