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Zhang F, Liu HM, Wang YF, Tang TY, Li HB, Huang YS, Yan YT, Liu KX. Mediating effect of COVID-19 related negative sentiment on the relationship between COVID-19 infection indicators and burnout among Chinese anaesthesiologists in the post-pandemic era. Br J Anaesth 2023; 131:e160-e162. [PMID: 37741723 DOI: 10.1016/j.bja.2023.08.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 08/18/2023] [Accepted: 08/20/2023] [Indexed: 09/25/2023] Open
Affiliation(s)
- Fu Zhang
- Department of Anesthesiology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Hua-Min Liu
- Department of Anesthesiology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Yi-Fan Wang
- Department of Anesthesiology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Tian-Ying Tang
- Department of Anesthesiology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Hai-Bo Li
- Department of Anesthesiology, Chifeng Municipal Hospital, Chifeng, Inner Mongolia, China
| | - Yi-Sheng Huang
- Department of Anesthesiology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China; Department of Anesthesiology, Ganzhou People's Hospital, Ganzhou, Jiangxi, China
| | - Yang-Tian Yan
- Department of Anesthesiology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Ke-Xuan Liu
- Department of Anesthesiology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China.
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Ng QX, Teo YQJ, Kiew CY, Lim BPY, Lim YL, Liew TM. Examining the Prevailing Negative Sentiments Surrounding Measles Vaccination: Unsupervised Deep Learning of Twitter Posts from 2017 to 2022. Cyberpsychol Behav Soc Netw 2023. [PMID: 37358808 DOI: 10.1089/cyber.2023.0025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 06/27/2023]
Abstract
Despite the proven safety and clinical efficacy of the Measles vaccine, many countries are seeing new heights of vaccine hesitancy or refusal, and are experiencing a resurgence of measles infections as a consequence. With the use of novel machine learning tools, we investigated the prevailing negative sentiments related to Measles vaccination through an analysis of public Twitter posts over a 5-year period. We extracted original tweets using the search terms related to "measles" and "vaccine," and posted in English from January 1, 2017, to December 15, 2022. Of these, 155,363 tweets were identified to be negative sentiment tweets from unique individuals, through the use of Bidirectional Encoder Representations from Transformers (BERT) Named Entity Recognition and SieBERT, a pretrained sentiment in English analysis model. This was followed by topic modeling and qualitative thematic analysis performed inductively by the study investigators. A total of 11 topics were generated after applying BERTopic. To facilitate a global discussion of results, the topics were grouped into four different themes through iterative thematic analysis. These include (a) the rejection of "anti-vaxxers" or antivaccine sentiments, (b) misbeliefs and misinformation regarding Measles vaccination, (c) negative transference due to COVID-19 related policies, and (d) public reactions to contemporary Measles outbreaks. Theme 1 highlights that the current public discourse may further alienate those who are vaccine hesitant because of the disparaging language often used, while Themes 2 and 3 highlight the typology of misperceptions and misinformation underlying the negative sentiments related to Measles vaccination and the psychological tendency of disconfirmation bias. Nonetheless, the analysis was based solely on Twitter and only tweets in English were included; hence, the findings may not necessarily generalize to non-Western communities. It is important to further understand the thinking and feeling of those who are vaccine hesitant to address the issues at hand.
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Affiliation(s)
- Qin Xiang Ng
- Health Services Research Unit, Singapore General Hospital, Singapore, Singapore
- MOH Holdings Pte Ltd., Singapore, Singapore
| | - Yu Qing Jolene Teo
- School of Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Chee Yu Kiew
- School of Medicine, University College Cork, Cork, Ireland
| | | | | | - Tau Ming Liew
- Department of Psychiatry, Singapore General Hospital, Singapore, Singapore
- SingHealth Duke-NUS Medicine Academic Clinical Programme, Duke-NUS Medical School, Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
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Ng QX, Lee DYX, Ng CX, Yau CE, Lim YL, Liew TM. Examining the Negative Sentiments Related to Influenza Vaccination from 2017 to 2022: An Unsupervised Deep Learning Analysis of 261,613 Twitter Posts. Vaccines (Basel) 2023; 11:1018. [PMID: 37376407 DOI: 10.3390/vaccines11061018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 05/11/2023] [Accepted: 05/22/2023] [Indexed: 06/29/2023] Open
Abstract
Several countries are witnessing significant increases in influenza cases and severity. Despite the availability, effectiveness and safety of influenza vaccination, vaccination coverage remains suboptimal globally. In this study, we examined the prevailing negative sentiments related to influenza vaccination via a deep learning analysis of public Twitter posts over the past five years. We extracted original tweets containing the terms 'flu jab', '#flujab', 'flu vaccine', '#fluvaccine', 'influenza vaccine', '#influenzavaccine', 'influenza jab', or '#influenzajab', and posted in English from 1 January 2017 to 1 November 2022. We then identified tweets with negative sentiment from individuals, and this was followed by topic modelling using machine learning models and qualitative thematic analysis performed independently by the study investigators. A total of 261,613 tweets were analyzed. Topic modelling and thematic analysis produced five topics grouped under two major themes: (1) criticisms of governmental policies related to influenza vaccination and (2) misinformation related to influenza vaccination. A significant majority of the tweets were centered around perceived influenza vaccine mandates or coercion to vaccinate. Our analysis of temporal trends also showed an increase in the prevalence of negative sentiments related to influenza vaccination from the year 2020 onwards, which possibly coincides with misinformation related to COVID-19 policies and vaccination. There was a typology of misperceptions and misinformation underlying the negative sentiments related to influenza vaccination. Public health communications should be mindful of these findings.
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Affiliation(s)
- Qin Xiang Ng
- Health Services Research Unit, Singapore General Hospital, Singapore 169608, Singapore
- MOH Holdings Pte Ltd., 1 Maritime Square, Singapore 099253, Singapore
| | - Dawn Yi Xin Lee
- School of Medicine, Dentistry and Nursing, University of Glasgow, Glasgow G12 8QQ, UK
| | - Clara Xinyi Ng
- NUS Yong Loo Lin School of Medicine, Singapore 117597, Singapore
| | - Chun En Yau
- NUS Yong Loo Lin School of Medicine, Singapore 117597, Singapore
| | - Yu Liang Lim
- MOH Holdings Pte Ltd., 1 Maritime Square, Singapore 099253, Singapore
| | - Tau Ming Liew
- Department of Psychiatry, Singapore General Hospital, Singapore 169608, Singapore
- SingHealth Duke-NUS Medicine Academic Clinical Programme, Duke-NUS Medical School, Singapore 169857, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore 117549, Singapore
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Ng QX, Lim SR, Yau CE, Liew TM. Examining the Prevailing Negative Sentiments Related to COVID-19 Vaccination: Unsupervised Deep Learning of Twitter Posts over a 16 Month Period. Vaccines (Basel) 2022; 10:vaccines10091457. [PMID: 36146535 PMCID: PMC9503543 DOI: 10.3390/vaccines10091457] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 08/30/2022] [Accepted: 08/30/2022] [Indexed: 01/17/2023] Open
Abstract
Despite the demonstrated efficacy, safety, and availability of COVID-19 vaccines, efforts in global mass vaccination have been met with widespread scepticism and vaccine hesitancy or refusal. Understanding the reasons for the public's negative opinions towards COVID-19 vaccination using Twitter may help make new headways in improving vaccine uptake. This study, therefore, examined the prevailing negative sentiments towards COVID-19 vaccination via the analysis of public twitter posts over a 16 month period. Original tweets (in English) from 1 April 2021 to 1 August 2022 were extracted. A bidirectional encoder representations from transformers (BERT)-based model was applied, and only negative sentiments tweets were selected. Topic modelling was used, followed by manual thematic analysis performed iteratively by the study investigators, with independent reviews of the topic labels and themes. A total of 4,448,314 tweets were analysed. The analysis generated six topics and three themes related to the prevailing negative sentiments towards COVID-19 vaccination. The themes could be broadly understood as either emotional reactions to perceived invidious policies or safety and effectiveness concerns related to the COVID-19 vaccines. The themes uncovered in the present infodemiology study fit well into the increasing vaccination model, and they highlight important public conversations to be had and potential avenues for future policy intervention and campaign efforts.
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Affiliation(s)
- Qin Xiang Ng
- Health Services Research Unit, Singapore General Hospital, Singapore 169608, Singapore
| | - Shu Rong Lim
- Health Services Research Unit, Singapore General Hospital, Singapore 169608, Singapore
| | - Chun En Yau
- NUS Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
| | - Tau Ming Liew
- Department of Psychiatry, Singapore General Hospital, Singapore 169608, Singapore
- SingHealth Duke-NUS Medicine Academic Clinical Programme, Duke-NUS Medical School, Singapore 169857, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore 117549, Singapore
- Correspondence: ; Tel.: +65-6222-3322
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