Artificial Intelligence-Enabled Analysis of Public Attitudes on Facebook and Twitter Toward COVID-19 Vaccines in the United Kingdom and the United States: Observational Study

被引:186
作者
Hussain, Amir [1 ]
Tahir, Ahsen [1 ,2 ]
Hussain, Zain [3 ]
Sheikh, Zakariya [3 ]
Gogate, Mandar [1 ]
Dashtipour, Kia [1 ]
Ali, Azhar [4 ,5 ]
Sheikh, Aziz [6 ]
机构
[1] Edinburgh Napier Univ, Sch Comp, 10 Colinton Rd, Edinburgh EH10 5DT, Midlothian, Scotland
[2] Univ Engn & Technol, Dept Elect Engn, Lahore, Pakistan
[3] Univ Edinburgh, Coll Med & Vet Med, Edinburgh Med Sch, Edinburgh, Midlothian, Scotland
[4] NHS Forth Med Grp, Grangemouth, Scotland
[5] Harvard Univ, Harvard TH Chan Sch Publ Hlth, Boston, MA 02115 USA
[6] Univ Edinburgh, Edinburgh Med Sch, Usher Inst, Edinburgh, Midlothian, Scotland
基金
英国工程与自然科学研究理事会; 英国科研创新办公室;
关键词
artificial intelligence; COVID-19; deep learning; Facebook; health informatics; natural language processing; public health; sentiment analysis; social media; Twitter; infodemiology; vaccination; SCALE;
D O I
10.2196/26627
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
Background: Global efforts toward the development and deployment of a vaccine for COVID-19 are rapidly advancing. To achieve herd immunity, widespread administration of vaccines is required, which necessitates significant cooperation from the general public. As such, it is crucial that governments and public health agencies understand public sentiments toward vaccines, which can help guide educational campaigns and other targeted policy interventions. Objective: The aim of this study was to develop and apply an artificial intelligence-based approach to analyze public sentiments on social media in the United Kingdom and the United States toward COVID-19 vaccines to better understand the public attitude and concerns regarding COVID-19 vaccines. Methods: Over 300,000 social media posts related to COVID-19 vaccines were extracted, including 23,571 Facebook posts from the United Kingdom and 144,864 from the United States, along with 40,268 tweets from the United Kingdom and 98,385 from the United States from March 1 to November 22, 2020. We used natural language processing and deep learning-based techniques to predict average sentiments, sentiment trends, and topics of discussion. These factors were analyzed longitudinally and geospatially, and manual reading of randomly selected posts on points of interest helped identify underlying themes and validated insights from the analysis. Results: Overall averaged positive, negative, and neutral sentiments were at 58%, 22%, and 17% in the United Kingdom, compared to 56%, 24%, and 18% in the United States, respectively. Public optimism over vaccine development, effectiveness, and trials as well as concerns over their safety, economic viability, and corporation control were identified. We compared our findings to those of nationwide surveys in both countries and found them to correlate broadly. Conclusions: Artificial intelligence-enabled social media analysis should be considered for adoption by institutions and governments alongside surveys and other conventional methods of assessing public attitude. Such analyses could enable real-time assessment, at scale, of public confidence and trust in COVID-19 vaccines, help address the concerns of vaccine sceptics, and help develop more effective policies and communication strategies to maximize uptake.
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页数:10
相关论文
共 42 条
[11]  
Bruce G., 2020, YouGov
[12]   A hybrid Persian sentiment analysis framework: Integrating dependency grammar based rules and deep neural networks [J].
Dashtipour, Kia ;
Gogate, Mandar ;
Li, Jingpeng ;
Jiang, Fengling ;
Kong, Bin ;
Hussain, Amir .
NEUROCOMPUTING, 2020, 380 :1-10
[13]   Mapping global trends in vaccine confidence and investigating barriers to vaccine uptake: a large-scale retrospective temporal modelling study [J].
de Figueiredo, Alexandre ;
Simas, Clarissa ;
Karafillakis, Emilie ;
Paterson, Pauline ;
Larson, Heidi J. .
LANCET, 2020, 396 (10255) :898-908
[14]  
Devlin J, 2019, 2019 CONFERENCE OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: HUMAN LANGUAGE TECHNOLOGIES (NAACL HLT 2019), VOL. 1, P4171
[15]  
Doshi Peter, 2020, BMJ OPINION
[16]  
ESRC Research Data Policy, EC SOCIAL RES COUNCI
[17]   COVID-19 herd immunity: where are we? [J].
Fontanet, Arnaud ;
Cauchemez, Simon .
NATURE REVIEWS IMMUNOLOGY, 2020, 20 (10) :583-584
[18]   Ethnicity-specific factors influencing childhood immunisation decisions among Black and Asian Minority Ethnic groups in the UK: a systematic review of qualitative research [J].
Forster, Alice S. ;
Rockliffe, Lauren ;
Chorley, Amanda J. ;
Marlow, Laura A. V. ;
Bedford, Helen ;
Smith, Samuel G. ;
Waller, Jo .
JOURNAL OF EPIDEMIOLOGY AND COMMUNITY HEALTH, 2017, 71 (06) :544-549
[19]  
Franzke AS., 2020, INTERNET RES ETHICAL
[20]   Assessing the risks of 'infodemics' in response to COVID-19 epidemics [J].
Gallotti, Riccardo ;
Valle, Francesco ;
Castaldo, Nicola ;
Sacco, Pierluigi ;
De Domenico, Manlio .
NATURE HUMAN BEHAVIOUR, 2020, 4 (12) :1285-1293