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Kwok WH, Zhang Y, Wang G. Artificial intelligence in perinatal mental health research: A scoping review. Comput Biol Med 2024; 177:108685. [PMID: 38838557 DOI: 10.1016/j.compbiomed.2024.108685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 04/28/2024] [Accepted: 06/01/2024] [Indexed: 06/07/2024]
Abstract
The intersection of Artificial Intelligence (AI) and perinatal mental health research presents promising avenues, yet uncovers significant challenges for innovation. This review explicitly focuses on this multidisciplinary field and undertakes a comprehensive exploration of existing research therein. Through a scoping review guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, we searched relevant literature spanning a decade (2013-2023) and selected fourteen studies for our analysis. We first provide an overview of the main AI techniques and their development, including traditional methods across different categories, as well as recent emerging methods in the field. Then, through our analysis of the literature, we summarize the predominant AI and ML techniques adopted and their applications in perinatal mental health studies, such as identifying risk factors, predicting perinatal mental health disorders, voice assistants, and Q&A chatbots. We also discuss existing limitations and potential challenges that hinder AI technologies from improving perinatal mental health outcomes, and suggest several promising directions for future research to meet real needs in the field and facilitate the translation of research into clinical settings.
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Affiliation(s)
- Wai Hang Kwok
- School of Nursing and Midwifery, Edith Cowan University, WA, Australia
| | - Yuanpeng Zhang
- Department of Medical Informatics, Nantong University, Nantong, 226001, China
| | - Guanjin Wang
- School of Information Technology, Murdoch University, Murdoch, WA, Australia.
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Shen Y, Luo Z, Song X, Liu C. Research on the evolution of cross-platform online public opinion for public health emergencies considering stakeholders. PLoS One 2024; 19:e0304877. [PMID: 38917155 PMCID: PMC11198828 DOI: 10.1371/journal.pone.0304877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 05/21/2024] [Indexed: 06/27/2024] Open
Abstract
OBJECTIVE To explore the different processes of the themes and emotional evolution of various stakeholders in the network public opinion of sudden public health emergencies at different stages of the public opinion evolution lifecycle. METHODS This paper proposes a cross-platform analysis method for online public opinion during the public health emergencies based on stakeholders. Firstly, data from multiple platforms are collected and integrated. Secondly, stakeholders are categorized and the stages of public opinion evolution are determined based on stakeholder theory and lifecycle theory. Finally, the Latent Dirichlet Allocation (LDA)+Word2vec model and Convolutional Neural Network (CNN) model are used to analyze the themes and emotional evolution of stakeholders during different stages of public opinion evolution. RESULTS There are differences in the evolution patterns of different types of stakeholders. The evolution process of stakeholders' focus points exhibits a two-stage transition from concentration to divergence. The focus points of stakeholders are closely associated with their respective social domains. The emotions of the public undergo a three-stage process of positive-negative-positive change. CONCLUSIONS This study can provide a reference for the government to have a more comprehensive understanding of the development trend of public opinion and reduce the negative impact of public opinion.
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Affiliation(s)
- Yan Shen
- School of Management, Jiangsu University, Zhenjiang, China
| | - Zhou Luo
- School of Management, Jiangsu University, Zhenjiang, China
| | - Xinping Song
- School of Management, Jiangsu University, Zhenjiang, China
| | - Chunhua Liu
- School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, China
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Stracqualursi L, Agati P. Twitter users perceptions of AI-based e-learning technologies. Sci Rep 2024; 14:5927. [PMID: 38467685 DOI: 10.1038/s41598-024-56284-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 03/05/2024] [Indexed: 03/13/2024] Open
Abstract
Today, teaching and learning paths increasingly intersect with technologies powered by emerging artificial intelligence (AI).This work analyses public opinions and sentiments about AI applications that affect e-learning, such as ChatGPT, virtual and augmented reality, microlearning, mobile learning, adaptive learning, and gamification. The way people perceive technologies fuelled by artificial intelligence can be tracked in real time in microblog messages promptly shared by Twitter users, who currently constitute a large and ever-increasing number of individuals. The observation period was from November 30, 2022, the date on which ChatGPT was launched, to March 31, 2023. A two-step sentiment analysis was performed on the collected English-language tweets to determine the overall sentiments and emotions. A latent Dirichlet allocation model was built to identify commonly discussed topics in tweets. The results show that the majority of opinions are positive. Among the eight emotions of the Syuzhet package, 'trust' and 'joy' are the most common positive emotions observed in the tweets, while 'fear' is the most common negative emotion. Among the most discussed topics with a negative outlook, two particular aspects of fear are identified: an 'apocalyptic-fear' that artificial intelligence could lead the end of humankind, and a fear for the 'future of artistic and intellectual jobs' as AI could not only destroy human art and creativity but also make the individual contributions of students and researchers not assessable. On the other hand, among the topics with a positive outlook, trust and hope in AI tools for improving efficiency in jobs and the educational world are identified. Overall, the results suggest that AI will play a significant role in the future of the world and education, but it is important to consider the potential ethical and social implications of this technology. By leveraging the positive aspects of AI while addressing these concerns, the education system can unlock the full potential of this emerging technology and provide a better learning experience for students.
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Affiliation(s)
| | - Patrizia Agati
- Department of Statistics, University of Bologna, 40126, Bologna, Italy
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Gyftopoulos S, Drosatos G, Fico G, Pecchia L, Kaldoudi E. Analysis of Pharmaceutical Companies' Social Media Activity during the COVID-19 Pandemic and Its Impact on the Public. Behav Sci (Basel) 2024; 14:128. [PMID: 38392481 PMCID: PMC10886074 DOI: 10.3390/bs14020128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 01/30/2024] [Accepted: 02/06/2024] [Indexed: 02/24/2024] Open
Abstract
The COVID-19 pandemic, a period of great turmoil, was coupled with the emergence of an "infodemic", a state when the public was bombarded with vast amounts of unverified information from dubious sources that led to a chaotic information landscape. The excessive flow of messages to citizens, combined with the justified fear and uncertainty imposed by the unknown virus, cast a shadow on the credibility of even well-intentioned sources and affected the emotional state of the public. Several studies highlighted the mental toll this environment took on citizens by analyzing their discourse on online social networks (OSNs). In this study, we focus on the activity of prominent pharmaceutical companies on Twitter, currently known as X, as well as the public's response during the COVID-19 pandemic. Communication between companies and users is examined and compared in two discrete channels, the COVID-19 and the non-COVID-19 channel, based on the content of the posts circulated in them in the period between March 2020 and September 2022, while the emotional profile of the content is outlined through a state-of-the-art emotion analysis model. Our findings indicate significantly increased activity in the COVID-19 channel compared to the non-COVID-19 channel while the predominant emotion in both channels is joy. However, the COVID-19 channel exhibited an upward trend in the circulation of fear by the public. The quotes and replies produced by the users, with a stark presence of negative charge and diffusion indicators, reveal the public's preference for promoting tweets conveying an emotional charge, such as fear, surprise, and joy. The findings of this research study can inform the development of communication strategies based on emotion-aware messages in future crises.
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Affiliation(s)
- Sotirios Gyftopoulos
- European Alliance for Medical and Biological Engineering and Science, 3001 Leuven, Belgium
- Institute for Language and Speech Processing, Athena Research Center, 67100 Xanthi, Greece
| | - George Drosatos
- European Alliance for Medical and Biological Engineering and Science, 3001 Leuven, Belgium
- Institute for Language and Speech Processing, Athena Research Center, 67100 Xanthi, Greece
| | - Giuseppe Fico
- European Alliance for Medical and Biological Engineering and Science, 3001 Leuven, Belgium
- Life Supporting Technologies, Universidad Politécnica de Madrid, 28040 Madrid, Spain
| | - Leandro Pecchia
- European Alliance for Medical and Biological Engineering and Science, 3001 Leuven, Belgium
- School of Engineering, University of Warwick, Coventry CV4 7AL, UK
- Department of Engineering, Università Campus Bio-Medico di Roma, 00128 Rome, Italy
| | - Eleni Kaldoudi
- European Alliance for Medical and Biological Engineering and Science, 3001 Leuven, Belgium
- Institute for Language and Speech Processing, Athena Research Center, 67100 Xanthi, Greece
- School of Medicine, Democritus University of Thrace, 68100 Alexandroupoli, Greece
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Zaidi N, Munir R. The one with the rumour: COVID-19-related conversations on Pakistani Twitter. Public Health 2023; 225:277-284. [PMID: 37952344 DOI: 10.1016/j.puhe.2023.10.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 09/23/2023] [Accepted: 10/03/2023] [Indexed: 11/14/2023]
Abstract
OBJECTIVES The COVID-19 pandemic has been a massive crisis exacerbated by the spread of misinformation on social media. Twitter is a highly popular microblogging platform in Pakistan, and the large population there lacks digital literacy, making them vulnerable to various forms of online and digital propaganda. This study aims to analyse the content of COVID-19-related tweets from Pakistan. STUDY DESIGN The current study is a content analysis of COVID-19-related tweets in Pakistani Twitter during the early stages of the pandemic, with a particular emphasis on misinformation, political content, health-related content, risk framing, and rumours. METHODS The Twitter data were obtained and anonymised by a third party for this study. The selected tweets were manually coded, and the following thematic tweet categories were identified: Science, Data, Pseudoscience, Healthcare, Conspiracies, Policies and Politics, Humour, and Pandemic life. RESULTS AND CONCLUSIONS Results indicated that the Policies and Politics category contained the majority of tweets (46.3%). Most science-based tweets focussed on nonpharmaceutical interventions (68.8%). As anticipated, the categories of Pseudoscience and Conspiracies were found to contain the most misinformation. Additionally, the number of likes and retweets for different tweet categories were compared, and no significant differences were found.
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Affiliation(s)
- N Zaidi
- Cancer Research Center (CRC), University of the Punjab, Lahore, Pakistan; Cancer Biology Lab, Institute of Microbiology and Molecular Genetics, University of the Punjab, Lahore, Pakistan.
| | - R Munir
- Hormone Lab, Lahore, Pakistan
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Canaparo M, Ronchieri E, Scarso L. A natural language processing approach for analyzing COVID-19 vaccination response in multi-language and geo-localized tweets. HEALTHCARE ANALYTICS (NEW YORK, N.Y.) 2023; 3:100172. [PMID: 37064254 PMCID: PMC10088351 DOI: 10.1016/j.health.2023.100172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Revised: 03/27/2023] [Accepted: 04/05/2023] [Indexed: 04/18/2023]
Abstract
Social media platforms, such as Twitter, have been paramount in the COVID-19 context due to their ability to collect public concerns about the COVID-19 vaccination campaign, which has been underway to end the COVID-19 pandemic. This worldwide campaign has heavily relied on the actual willingness of individuals to get vaccinated independently of the language they speak or the country they reside. This study analyzes Twitter posts about Pfizer/BioNTech, Moderna, AstraZeneca/Vaxzevria, and Johnson & Johnson vaccines by considering the most spoken western languages. Tweets were sampled between April 15 and September 15, 2022, after the injections of at least three doses, collecting 9,513,063 posts that contained vaccine-related keywords. To determine the success of vaccination, temporal and sentiment analysis have been conducted, reporting opinion changes over time and their corresponding events whenever possible concerning each vaccine. Furthermore, we have extracted the main topics over languages providing potential bias due to the language-specific dictionary, such as Moderna in Spanish, and grouped them per country. Once performed the pre-processed procedure we worked with 8,343,490 tweets. Our findings show that Pfizer has been the most debated vaccine worldwide, and the main concerns have been the side effects on pregnant women and children and heart diseases.
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Affiliation(s)
- Marco Canaparo
- INFN-CNAF, Viale Berti Pichat 6/2, Bologna, 40126, Italy
| | - Elisabetta Ronchieri
- INFN-CNAF, Viale Berti Pichat 6/2, Bologna, 40126, Italy
- Department of Statistical Sciences, University of Bologna, Via Belle Arti 41, Bologna, Italy
| | - Leonardo Scarso
- Department of Medical and Surgical Sciences, University of Bologna, Via Pelagio Palagi 9, Bologna, Italy
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Hirabayashi M, Shibata D, Shinohara E, Kawazoe Y. Influence of Tweets Indicating False Rumors on COVID-19 Vaccination: Case Study. JMIR Form Res 2023; 7:e45867. [PMID: 37669092 PMCID: PMC10482055 DOI: 10.2196/45867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 07/27/2023] [Accepted: 08/01/2023] [Indexed: 09/06/2023] Open
Abstract
BACKGROUND As of December 2022, the outbreak of COVID-19 showed no sign of abating, continuing to impact people's lives, livelihoods, economies, and more. Vaccination is an effective way to achieve mass immunity. However, in places such as Japan, where vaccination is voluntary, there are people who choose not to receive the vaccine, even if an effective vaccine is offered. To promote vaccination, it is necessary to clarify what kind of information on social media can influence attitudes toward vaccines. OBJECTIVE False rumors and counterrumors are often posted and spread in large numbers on social media, especially during emergencies. In this paper, we regard tweets that contain questions or point out errors in information as counterrumors. We analyze counterrumors tweets related to the COVID-19 vaccine on Twitter. We aimed to answer the following questions: (1) what kinds of COVID-19 vaccine-related counterrumors were posted on Twitter, and (2) are the posted counterrumors related to social conditions such as vaccination status? METHODS We use the following data sets: (1) counterrumors automatically collected by the "rumor cloud" (18,593 tweets); and (2) the number of COVID-19 vaccine inoculators from September 27, 2021, to August 15, 2022, published on the Prime Minister's Office's website. First, we classified the contents contained in counterrumors. Second, we counted the number of COVID-19 vaccine-related counterrumors from data set 1. Then, we examined the cross-correlation coefficients between the numbers of data sets 1 and 2. Through this verification, we examined the correlation coefficients for the following three periods: (1) the same period of data; (2) the case where the occurrence of the suggestion of counterrumors precedes the vaccination (negative time lag); and (3) the case where the vaccination precedes the occurrence of counterrumors (positive time lag). The data period used for the validation was from October 4, 2021, to April 18, 2022. RESULTS Our classification results showed that most counterrumors about the COVID-19 vaccine were negative. Moreover, the correlation coefficients between the number of counterrumors and vaccine inoculators showed significant and strong positive correlations. The correlation coefficient was over 0.7 at -8, -7, and -1 weeks of lag. Results suggest that the number of vaccine inoculators tended to increase with an increase in the number of counterrumors. Significant correlation coefficients of 0.5 to 0.6 were observed for lags of 1 week or more and 2 weeks or more. This implies that an increase in vaccine inoculators increases the number of counterrumors. These results suggest that the increase in the number of counterrumors may have been a factor in inducing vaccination behavior. CONCLUSIONS Using quantitative data, we were able to reveal how counterrumors influence the vaccination status of the COVID-19 vaccine. We think that our findings would be a foundation for considering countermeasures of vaccination.
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Affiliation(s)
- Mai Hirabayashi
- Artificial Intelligence in Healthcare, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Daisaku Shibata
- Artificial Intelligence in Healthcare, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Emiko Shinohara
- Artificial Intelligence in Healthcare, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Yoshimasa Kawazoe
- Artificial Intelligence in Healthcare, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
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Chen D, Zhang R. COVID-19 Vaccine Adverse Event Detection Based on Multi-Label Classification With Various Label Selection Strategies. IEEE J Biomed Health Inform 2023; 27:4192-4203. [PMID: 37418397 DOI: 10.1109/jbhi.2023.3292252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/09/2023]
Abstract
Analyzing massive VAERS reports without medical context may lead to incorrect conclusions about vaccine adverse events (VAE). Facilitating VAE detection promotes continual safety improvement for new vaccines. This study proposes a multi-label classification method with various term-and topic-based label selection strategies to improve the accuracy and efficiency of VAE detection. Topic modeling methods are first used to generate rule-based label dependencies from Medical Dictionary for Regulatory Activities terms in VAE reports with two hyper-parameters. Multiple label selection strategies, namely one-vs-rest (OvsR), problem transformation (PT), algorithm adaption (AA), and deep learning (DL) methods, are used in multi-label classification to examine the model performance, respectively. Experimental results indicated that the topic-based PT methods improve the accuracy by up to 33.69% using a COVID-19 VAE reporting data set, which improves the robustness and interpretability of our models. In addition, the topic-based OvsR methods achieve an optimal accuracy of up to 98.88%. The accuracy of the AA methods with topic-based labels increased by up to 87.36%. By contrast, the state-of-art LSTM- and BERT-based DL methods have relatively poor performance with accuracy rates of 71.89% and 64.63%, respectively. Our findings reveal that the proposed method effectively improves the model accuracy and strengthens VAE interpretability by using different label selection strategies and domain knowledge in multi-label classification for VAE detection.
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Alvarez-Mon MA, Pereira-Sanchez V, Hooker ER, Sanchez F, Alvarez-Mon M, Teo AR. Content and User Engagement of Health-Related Behavior Tweets Posted by Mass Media Outlets From Spain and the United States Early in the COVID-19 Pandemic: Observational Infodemiology Study. JMIR INFODEMIOLOGY 2023; 3:e43685. [PMID: 37347948 PMCID: PMC10445660 DOI: 10.2196/43685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 02/17/2023] [Accepted: 05/30/2023] [Indexed: 06/24/2023]
Abstract
BACKGROUND During the early pandemic, there was substantial variation in public and government responses to COVID-19 in Europe and the United States. Mass media are a vital source of health information and news, frequently disseminating this information through social media, and may influence public and policy responses to the pandemic. OBJECTIVE This study aims to describe the extent to which major media outlets in the United States and Spain tweeted about health-related behaviors (HRBs) relevant to COVID-19, compare the tweeting patterns between media outlets of both countries, and determine user engagement in response to these tweets. METHODS We investigated tweets posted by 30 major media outlets (n=17, 57% from Spain and n=13, 43% from the United States) between December 1, 2019 and May 31, 2020, which included keywords related to HRBs relevant to COVID-19. We classified tweets into 6 categories: mask-wearing, physical distancing, handwashing, quarantine or confinement, disinfecting objects, or multiple HRBs (any combination of the prior HRB categories). Additionally, we assessed the likes and retweets generated by each tweet. Poisson regression analyses compared the average predicted number of likes and retweets between the different HRB categories and between countries. RESULTS Of 50,415 tweets initially collected, 8552 contained content associated with an HRB relevant to COVID-19. Of these, 600 were randomly chosen for training, and 2351 tweets were randomly selected for manual content analysis. Of the 2351 COVID-19-related tweets included in the content analysis, 62.91% (1479/2351) mentioned at least one HRB. The proportion of COVID-19 tweets mentioning at least one HRB differed significantly between countries (P=.006). Quarantine or confinement was mentioned in nearly half of all the HRB tweets in both countries. In contrast, the least frequently mentioned HRBs were disinfecting objects in Spain 6.9% (56/809) and handwashing in the United States 9.1% (61/670). For tweets from the United States mentioning at least one HRB, disinfecting objects had the highest median likes and retweets, whereas mask-wearing- and handwashing-related tweets achieved the highest median number of likes in Spain. Tweets from Spain that mentioned social distancing or disinfecting objects had a significantly lower predicted count of likes compared with tweets mentioning a different HRB (P=.02 and P=.01, respectively). Tweets from the United States that mentioned quarantine or confinement or disinfecting objects had a significantly lower predicted number of likes compared with tweets mentioning a different HRB (P<.001), whereas mask- and handwashing-related tweets had a significantly greater predicted number of likes (P=.04 and P=.02, respectively). CONCLUSIONS The type of HRB content and engagement with media outlet tweets varied between Spain and the United States early in the pandemic. However, content related to quarantine or confinement and engagement with handwashing was relatively high in both countries.
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Affiliation(s)
- Miguel Angel Alvarez-Mon
- Department of Medicine and Medical Specialties, Faculty of Medicine and Health Sciences, University of Alcala, Alcala de Henares, Spain
- Department of Psychiatry and Mental Health, University Hospital Infanta Leonor, Madrid, Spain
- Ramón y Cajal Institute of Sanitary Research (IRYCIS), Madrid, Spain
| | - Victor Pereira-Sanchez
- Department of Child and Adolescent Psychiatry, NYU Grossman School of Medicine, New York, NY, United States
| | - Elizabeth R Hooker
- VA Portland Health Care System, Health Services Research & Development Center to Improve Veteran Involvement in Care, Portland, OR, United States
- OHSU-PSU School of Public Health, Oregon Health and Science University, Portland, OR, United States
| | - Facundo Sanchez
- Lincoln Medical and Mental Health Center, New York, NY, United States
- Devers Eye Institute, Portland, OR, United States
| | - Melchor Alvarez-Mon
- Department of Medicine and Medical Specialties, Faculty of Medicine and Health Sciences, University of Alcala, Alcala de Henares, Spain
- Ramón y Cajal Institute of Sanitary Research (IRYCIS), Madrid, Spain
| | - Alan R Teo
- VA Portland Health Care System, Health Services Research & Development Center to Improve Veteran Involvement in Care, Portland, OR, United States
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, United States
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Saleh SN, McDonald SA, Basit MA, Kumar S, Arasaratnam RJ, Perl TM, Lehmann CU, Medford RJ. Public perception of COVID-19 vaccines through analysis of Twitter content and users. Vaccine 2023; 41:4844-4853. [PMID: 37385887 PMCID: PMC10288320 DOI: 10.1016/j.vaccine.2023.06.058] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 05/03/2023] [Accepted: 06/15/2023] [Indexed: 07/01/2023]
Abstract
BACKGROUND With the global continuation of the COVID-19 pandemic, the large-scale administration of a SARS-CoV-2 vaccine is crucial to achieve herd immunity and curtail further spread of the virus, but success is contingent on public understanding and vaccine uptake. We aim to understand public perception about vaccines for COVID-19 through the wide-scale, organic discussion on Twitter. METHODS This cross-sectional observational study included Twitter posts matching the search criteria (('covid*' OR 'coronavirus') AND 'vaccine') posted during vaccine development from February 1st through December 11th, 2020. These COVID-19 vaccine related posts were analyzed with topic modeling, sentiment and emotion analysis, and demographic inference of users to provide insight into the evolution of public attitudes throughout the study period. FINDINGS We evaluated 2,287,344 English tweets from 948,666 user accounts. Individuals represented 87.9 % (n = 834,224) of user accounts. Of individuals, men (n = 560,824) outnumbered women (n = 273,400) by 2:1 and 39.5 % (n = 329,776) of individuals were ≥40 years old. Daily mean sentiment fluctuated congruent with news events, but overall trended positively. Trust, anticipation, and fear were the three most predominant emotions; while fear was the most predominant emotion early in the study period, trust outpaced fear from April 2020 onward. Fear was more prevalent in tweets by individuals (26.3 % vs. organizations 19.4 %; p < 0.001), specifically among women (28.4 % vs. males 25.4 %; p < 0.001). Multiple topics had a monthly trend towards more positive sentiment. Tweets comparing COVID-19 to the influenza vaccine had strongly negative early sentiment but improved over time. INTERPRETATION This study successfully explores sentiment, emotion, topics, and user demographics to elucidate important trends in public perception about COVID-19 vaccines. While public perception trended positively over the study period, some trends, especially within certain topic and demographic clusters, are concerning for COVID-19 vaccine hesitancy. These insights can provide targets for educational interventions and opportunity for continued real-time monitoring.
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Affiliation(s)
- Sameh N Saleh
- Department of Internal Medicine, University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX 75390, United States; Clinical Informatics Center, University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX 75390, United States.
| | - Samuel A McDonald
- Clinical Informatics Center, University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX 75390, United States; Department of Emergency Medicine, University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX 75390, United States
| | - Mujeeb A Basit
- Department of Internal Medicine, University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX 75390, United States; Clinical Informatics Center, University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX 75390, United States
| | - Sanat Kumar
- Clinical Informatics Center, University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX 75390, United States; Lebanon Trail High School, 5151 Ohio Dr, Frisco, TX 75035, United States
| | - Reuben J Arasaratnam
- Department of Internal Medicine, University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX 75390, United States
| | - Trish M Perl
- Department of Internal Medicine, University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX 75390, United States
| | - Christoph U Lehmann
- Clinical Informatics Center, University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX 75390, United States; Departments of Pediatrics, Bioinformatics, Population & Data Sciences, University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX 75390, United States
| | - Richard J Medford
- Department of Internal Medicine, University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX 75390, United States; Clinical Informatics Center, University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX 75390, United States
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Wang Y, Willis E, Yeruva VK, Ho D, Lee Y. A case study of using natural language processing to extract consumer insights from tweets in American cities for public health crises. BMC Public Health 2023; 23:935. [PMID: 37226165 DOI: 10.1186/s12889-023-15882-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 05/11/2023] [Indexed: 05/26/2023] Open
Abstract
BACKGROUND The COVID-19 pandemic was a "wake up" call for public health agencies. Often, these agencies are ill-prepared to communicate with target audiences clearly and effectively for community-level activations and safety operations. The obstacle is a lack of data-driven approaches to obtaining insights from local community stakeholders. Thus, this study suggests a focus on listening at local levels given the abundance of geo-marked data and presents a methodological solution to extracting consumer insights from unstructured text data for health communication. METHODS This study demonstrates how to combine human and Natural Language Processing (NLP) machine analyses to reliably extract meaningful consumer insights from tweets about COVID and the vaccine. This case study employed Latent Dirichlet Allocation (LDA) topic modeling, Bidirectional Encoder Representations from Transformers (BERT) emotion analysis, and human textual analysis and examined 180,128 tweets scraped by Twitter Application Programming Interface's (API) keyword function from January 2020 to June 2021. The samples came from four medium-sized American cities with larger populations of people of color. RESULTS The NLP method discovered four topic trends: "COVID Vaccines," "Politics," "Mitigation Measures," and "Community/Local Issues," and emotion changes over time. The human textual analysis profiled the discussions in the selected four markets to add some depth to our understanding of the uniqueness of the different challenges experienced. CONCLUSIONS This study ultimately demonstrates that our method used here could efficiently reduce a large amount of community feedback (e.g., tweets, social media data) by NLP and ensure contextualization and richness with human interpretation. Recommendations on communicating vaccination are offered based on the findings: (1) the strategic objective should be empowering the public; (2) the message should have local relevance; and, (3) communication needs to be timely.
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Affiliation(s)
- Ye Wang
- Department of Communication and Journalism, University of Missouri-Kansas City, 202 Haag Hall, 5120 Rockhill Road, 816-235-2735, Kansas City, MO, 64110, USA.
| | - Erin Willis
- Department of Advertising, Public Relations & Media Design, University of Colorado Boulder, 478 UCB, 1511 University Avenue, Boulder, CO, 80309-0200, USA
| | - Vijaya K Yeruva
- Division of Computing, Analytics, and Mathematics, University of Missouri-Kansas City, 801 E51st St, Kansas City, MO, 64110, USA
| | - Duy Ho
- Division of Computing, Analytics, and Mathematics, University of Missouri-Kansas City, 801 E51st St, Kansas City, MO, 64110, USA
| | - Yugyung Lee
- Division of Computing, Analytics, and Mathematics, University of Missouri-Kansas City, 801 E51st St, Kansas City, MO, 64110, USA
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12
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Dupuy-Zini A, Audeh B, Gérardin C, Duclos C, Gagneux-Brunon A, Bousquet C. Users' Reactions to Announced Vaccines Against COVID-19 Before Marketing in France: Analysis of Twitter Posts. J Med Internet Res 2023; 25:e37237. [PMID: 36596215 PMCID: PMC10132828 DOI: 10.2196/37237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 07/17/2022] [Accepted: 08/09/2022] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Within a few months, the COVID-19 pandemic had spread to many countries and had been a real challenge for health systems all around the world. This unprecedented crisis has led to a surge of online discussions about potential cures for the disease. Among them, vaccines have been at the heart of the debates and have faced lack of confidence before marketing in France. OBJECTIVE This study aims to identify and investigate the opinions of French Twitter users on the announced vaccines against COVID-19 through sentiment analysis. METHODS This study was conducted in 2 phases. First, we filtered a collection of tweets related to COVID-19 available on Twitter from February 2020 to August 2020 with a set of keywords associated with vaccine mistrust using word embeddings. Second, we performed sentiment analysis using deep learning to identify the characteristics of vaccine mistrust. The model was trained on a hand-labeled subset of 4548 tweets. RESULTS A set of 69 relevant keywords were identified as the semantic concept of the word "vaccin" (vaccine in French) and focused mainly on conspiracies, pharmaceutical companies, and alternative treatments. Those keywords enabled us to extract nearly 350,000 tweets in French. The sentiment analysis model achieved 0.75 accuracy. The model then predicted 16% of positive tweets, 41% of negative tweets, and 43% of neutral tweets. This allowed us to explore the semantic concepts of positive and negative tweets and to plot the trends of each sentiment. The main negative rhetoric identified from users' tweets was that vaccines are perceived as having a political purpose and that COVID-19 is a commercial argument for the pharmaceutical companies. CONCLUSIONS Twitter might be a useful tool to investigate the arguments for vaccine mistrust because it unveils political criticism contrasting with the usual concerns on adverse drug reactions. As the opposition rhetoric is more consistent and more widely spread than the positive rhetoric, we believe that this research provides effective tools to help health authorities better characterize the risk of vaccine mistrust.
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Affiliation(s)
- Alexandre Dupuy-Zini
- Laboratoire d'Informatique Médicale et d'Ingénierie des connaissances en e-Santé, LIMICS, Sorbonne Université, Université Sorbonne Paris Nord, Institut national de la santé et de la recherche médicale, INSERM, Paris, France
| | - Bissan Audeh
- Laboratoire d'Informatique Médicale et d'Ingénierie des connaissances en e-Santé, LIMICS, Sorbonne Université, Université Sorbonne Paris Nord, Institut national de la santé et de la recherche médicale, INSERM, Paris, France
| | - Christel Gérardin
- Institut Pierre Louis d'Epidémiologie et de Santé Publique, Département de médecine interne, Sorbonne Université, Paris, France
| | - Catherine Duclos
- Laboratoire d'Informatique Médicale et d'Ingénierie des connaissances en e-Santé, LIMICS, Sorbonne Université, Université Sorbonne Paris Nord, Institut national de la santé et de la recherche médicale, INSERM, Paris, France
| | - Amandine Gagneux-Brunon
- Groupe sur l'Immunité des Muqueuses et Agents Pathogènes, Centre International de Recherche en Infectiologie, University of Lyon, Saint Etienne, France
- Vaccinologie, Centre Hospitalier Universitaire de Saint-Etienne, Saint Etienne, France
| | - Cedric Bousquet
- Laboratoire d'Informatique Médicale et d'Ingénierie des connaissances en e-Santé, LIMICS, Sorbonne Université, Université Sorbonne Paris Nord, Institut national de la santé et de la recherche médicale, INSERM, Paris, France
- Service de santé publique et information médicale, Centre Hospitalier Universitaire de Saint Etienne, Saint Etienne, France
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13
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Davidson PD, Muniandy T, Karmegam D. Perception of COVID-19 vaccination among Indian Twitter users: computational approach. JOURNAL OF COMPUTATIONAL SOCIAL SCIENCE 2023:1-20. [PMID: 37363805 PMCID: PMC10047476 DOI: 10.1007/s42001-023-00203-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 03/01/2023] [Indexed: 06/28/2023]
Abstract
Vaccination has been a hot topic in the present COVID-19 context. The government, public health stakeholders and media are all concerned about how to get the people vaccinated. The study was intended to explore the perception and emotions of the Indians citizens toward COVID-19 vaccine from Twitter messages. The tweets were collected for the period of 6 months, from mid-January to June, 2021 using hash-tags and keywords specific to India. Topics and emotions from the tweets were extracted using Latent Dirichlet Allocation (LDA) method and National Research Council (NRC) Lexicon, respectively. Theme, sentiment and emotion wise engagement and reachability metrics were assessed. Hash-tag frequency of COVID-19 vaccine brands were also identified and evaluated. Information regarding 'Co-WIN app and availability of vaccine' was widely discussed and also received highest engagement and reachability among Twitter users. Among the various emotions, trust was expressed the most, which highlights the acceptance of vaccines among the Indian citizens. The hash-tags frequency of vaccine brands shows that Covishield was popular in the month of March 2021, and Covaxin in April 2021. The results from the study will help stakeholders to efficiently use social media to disseminate COVID-19 vaccine information on popular concerns. This in turn will encourage citizens to be vaccinated and achieve herd immunity. Similar methodology can be adopted in future to understand the perceptions and concerns of people in emergency situations. Supplementary Information The online version contains supplementary material available at 10.1007/s42001-023-00203-0.
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Affiliation(s)
| | | | - Dhivya Karmegam
- School of Public Health, SRM Institute of Science and Technology, Kattankulathur, Chennai, India
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14
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Ma N, Yu G, Jin X, Zhu X. Quantified multidimensional public sentiment characteristics on social media for public opinion management: Evidence from the COVID-19 pandemic. Front Public Health 2023; 11:1097796. [PMID: 37006559 PMCID: PMC10060635 DOI: 10.3389/fpubh.2023.1097796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 02/27/2023] [Indexed: 03/18/2023] Open
Abstract
BackgroundPublic sentiments arising from public opinion communication pose a serious psychological risk to public and interfere the communication of nonpharmacological intervention information during the COVID-19 pandemic. Problems caused by public sentiments need to be timely addressed and resolved to support public opinion management.ObjectiveThis study aims to investigate the quantified multidimensional public sentiments characteristics for helping solve the public sentiments issues and strengthen public opinion management.MethodsThis study collected the user interaction data from the Weibo platform, including 73,604 Weibo posts and 1,811,703 Weibo comments. Deep learning based on pretraining model, topics clustering and correlation analysis were used to conduct quantitative analysis on time series characteristics, content-based characteristics and audience response characteristics of public sentiments in public opinion during the pandemic.ResultsThe research findings were as follows: first, public sentiments erupted after priming, and the time series of public sentiments had window periods. Second, public sentiments were related to public discussion topics. The more negative the audience sentiments were, the more deeply the public participated in public discussions. Third, audience sentiments were independent of Weibo posts and user attributes, the steering role of opinion leaders was invalid in changing audience sentiments.DiscussionSince the COVID-19 pandemic, there has been an increasing demand for public opinion management on social media. Our study on the quantified multidimensional public sentiments characteristics is one of the methodological contributions to reinforce public opinion management from a practical perspective.
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Affiliation(s)
- Ning Ma
- School of Management, Harbin Institute of Technology, Harbin, China
| | - Guang Yu
- School of Management, Harbin Institute of Technology, Harbin, China
- *Correspondence: Guang Yu
| | - Xin Jin
- School of Humanities, Social Sciences and Law, Harbin Institute of Technology, Harbin, China
| | - Xiaoqian Zhu
- School of Management, Harbin Institute of Technology, Harbin, China
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15
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Borghouts J, Huang Y, Gibbs S, Hopfer S, Li C, Mark G. Understanding underlying moral values and language use of COVID-19 vaccine attitudes on twitter. PNAS NEXUS 2023; 2:pgad013. [PMID: 36896130 PMCID: PMC9991494 DOI: 10.1093/pnasnexus/pgad013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 12/23/2022] [Accepted: 01/09/2023] [Indexed: 03/11/2023]
Abstract
Public sentiment toward the COVID-19 vaccine as expressed on social media can interfere with communication by public health agencies on the importance of getting vaccinated. We investigated Twitter data to understand differences in sentiment, moral values, and language use between political ideologies on the COVID-19 vaccine. We estimated political ideology, conducted a sentiment analysis, and guided by the tenets of moral foundations theory (MFT), we analyzed 262,267 English language tweets from the United States containing COVID-19 vaccine-related keywords between May 2020 and October 2021. We applied the Moral Foundations Dictionary and used topic modeling and Word2Vec to understand moral values and the context of words central to the discussion of the vaccine debate. A quadratic trend showed that extreme ideologies of both Liberals and Conservatives expressed a higher negative sentiment than Moderates, with Conservatives expressing more negative sentiment than Liberals. Compared to Conservative tweets, we found the expression of Liberal tweets to be rooted in a wider set of moral values, associated with moral foundations of care (getting the vaccine for protection), fairness (having access to the vaccine), liberty (related to the vaccine mandate), and authority (trusting the vaccine mandate imposed by the government). Conservative tweets were found to be associated with harm (around safety of the vaccine) and oppression (around the government mandate). Furthermore, political ideology was associated with the expression of different meanings for the same words, e.g. "science" and "death." Our results inform public health outreach communication strategies to best tailor vaccine information to different groups.
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Affiliation(s)
- Judith Borghouts
- Department of Medicine, University of California Irvine, Irvine, CA 92617, USA
| | - Yicong Huang
- Department of Computer Science, University of California Irvine, Irvine, CA 92697, USA
| | - Sydney Gibbs
- Department of Computer Science, University of California Irvine, Irvine, CA 92697, USA
| | - Suellen Hopfer
- Department of Health, Society & Behavior in the Program in Public Health, University of California Irvine, Irvine, CA 92697, USA
| | - Chen Li
- Department of Computer Science, University of California Irvine, Irvine, CA 92697, USA
| | - Gloria Mark
- Department of Computer Science, University of California Irvine, Irvine, CA 92697, USA
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16
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To QG, To KG, Huynh VAN, Nguyen NTQ, Ngo DTN, Alley S, Tran ANQ, Tran ANP, Pham NTT, Bui TX, Vandelanotte C. Anti-vaccination attitude trends during the COVID-19 pandemic: A machine learning-based analysis of tweets. Digit Health 2023; 9:20552076231158033. [PMID: 36825077 PMCID: PMC9941594 DOI: 10.1177/20552076231158033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 01/31/2023] [Indexed: 02/22/2023] Open
Abstract
Objective Vaccine hesitancy has been ranked by the World Health Organization among the top 10 threats to global health. With a surge in misinformation and conspiracy theories against vaccination observed during the COVID-19 pandemic, attitudes toward vaccination may be worsening. This study investigates trends in anti-vaccination attitudes during the COVID-19 pandemic and within the United States, Canada, the United Kingdom, and Australia. Methods Vaccine-related English tweets published between 1 January 2020 and 27 June 2021 were used. A deep learning model using a dynamic word embedding method, Bidirectional Encoder Representations from Transformers (BERTs), was developed to identify anti-vaccination tweets. The classifier achieved a micro F1 score of 0.92. Time series plots and country maps were used to examine vaccination attitudes globally and within countries. Results Among 9,352,509 tweets, 232,975 (2.49%) were identified as anti-vaccination tweets. The overall number of vaccine-related tweets increased sharply after the implementation of the first vaccination round since November 2020 (daily average of 6967 before vs. 31,757 tweets after 9/11/2020). The number of anti-vaccination tweets increased after conspiracy theories spread on social media. Percentages of anti-vaccination tweets were 3.45%, 2.74%, 2.46%, and 1.86% for the United States, the United Kingdom, Australia, and Canada, respectively. Conclusions Strategies and information campaigns targeting vaccination misinformation may need to be specifically designed for regions with the highest anti-vaccination Twitter activity and when new vaccination campaigns are initiated.
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Affiliation(s)
- Quyen G To
- Appleton Institute, School of Health, Medical and Applied Sciences, Central Queensland University, Rockhampton, Australia,Quyen G. To, Appleton Institute, School of Health, Medical and Applied Sciences, Central Queensland University, Rockhampton, Australia.
| | - Kien G To
- Public Health Faculty, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | - Van-Anh N Huynh
- Public Health Faculty, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | | | - Diep TN Ngo
- Public Health Faculty, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | - Stephanie Alley
- Appleton Institute, School of Health, Medical and Applied Sciences, Central Queensland University, Rockhampton, Australia
| | - Anh NQ Tran
- Public Health Faculty, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | - Anh NP Tran
- Public Health Faculty, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | - Ngan TT Pham
- Public Health Faculty, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | - Thanh X Bui
- Public Health Faculty, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | - Corneel Vandelanotte
- Appleton Institute, School of Health, Medical and Applied Sciences, Central Queensland University, Rockhampton, Australia
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17
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Zang S, Zhang X, Xing Y, Chen J, Lin L, Hou Z. Applications of Social Media and Digital Technologies in COVID-19 Vaccination: Scoping Review. J Med Internet Res 2023; 25:e40057. [PMID: 36649235 PMCID: PMC9924059 DOI: 10.2196/40057] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 12/18/2022] [Accepted: 01/13/2023] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND Social media and digital technologies have played essential roles in disseminating information and promoting vaccination during the COVID-19 pandemic. There is a need to summarize the applications and analytical techniques of social media and digital technologies in monitoring vaccine attitudes and administering COVID-19 vaccines. OBJECTIVE We aimed to synthesize the global evidence on the applications of social media and digital technologies in COVID-19 vaccination and to explore their avenues to promote COVID-19 vaccination. METHODS We searched 6 databases (PubMed, Scopus, Web of Science, Embase, EBSCO, and IEEE Xplore) for English-language articles from December 2019 to August 2022. The search terms covered keywords relating to social media, digital technology, and COVID-19 vaccines. Articles were included if they provided original descriptions of applications of social media or digital health technologies/solutions in COVID-19 vaccination. Conference abstracts, editorials, letters, commentaries, correspondence articles, study protocols, and reviews were excluded. A modified version of the Appraisal Tool for Cross-Sectional Studies (AXIS tool) was used to evaluate the quality of social media-related studies. The review was undertaken with the guidance of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. RESULTS A total of 178 articles were included in our review, including 114 social media articles and 64 digital technology articles. Social media has been applied for sentiment/emotion analysis, topic analysis, behavioral analysis, dissemination and engagement analysis, and information quality analysis around COVID-19 vaccination. Of these, sentiment analysis and topic analysis were the most common, with social media data being primarily analyzed by lexicon-based and machine learning techniques. The accuracy and reliability of information on social media can seriously affect public attitudes toward COVID-19 vaccines, and misinformation often leads to vaccine hesitancy. Digital technologies have been applied to determine the COVID-19 vaccination strategy, predict the vaccination process, optimize vaccine distribution and delivery, provide safe and transparent vaccination certificates, and perform postvaccination surveillance. The applied digital technologies included algorithms, blockchain, mobile health, the Internet of Things, and other technologies, although with some barriers to their popularization. CONCLUSIONS The applications of social media and digital technologies in addressing COVID-19 vaccination-related issues represent an irreversible trend. Attention should be paid to the ethical issues and health inequities arising from the digital divide while applying and promoting these technologies.
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Affiliation(s)
- Shujie Zang
- School of Public Health, Fudan University, Shanghai, China
- Global Health Institute, Fudan University, Shanghai, China
| | - Xu Zhang
- School of Public Health, Fudan University, Shanghai, China
- Global Health Institute, Fudan University, Shanghai, China
| | - Yuting Xing
- School of Public Health, Fudan University, Shanghai, China
- Global Health Institute, Fudan University, Shanghai, China
| | - Jiaxian Chen
- School of Public Health, Fudan University, Shanghai, China
| | - Leesa Lin
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong, SAR, China
| | - Zhiyuan Hou
- School of Public Health, Fudan University, Shanghai, China
- Global Health Institute, Fudan University, Shanghai, China
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18
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Wang S, Huang X, Hu T, She B, Zhang M, Wang R, Gruebner O, Imran M, Corcoran J, Liu Y, Bao S. A global portrait of expressed mental health signals towards COVID-19 in social media space. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION : ITC JOURNAL 2023; 116:103160. [PMID: 36570490 PMCID: PMC9759272 DOI: 10.1016/j.jag.2022.103160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 11/07/2022] [Accepted: 12/15/2022] [Indexed: 06/17/2023]
Abstract
Globally, the COVID-19 pandemic has induced a mental health crisis. Social media data offer a unique opportunity to track the mental health signals of a given population and quantify their negativity towards COVID-19. To date, however, we know little about how negative sentiments differ across countries and how these relate to the shifting policy landscape experienced through the pandemic. Using 2.1 billion individual-level geotagged tweets posted between 1 February 2020 and 31 March 2021, we track, monitor and map the shifts in negativity across 217 countries and unpack its relationship with COVID-19 policies. Findings reveal that there are important geographic, demographic, and socioeconomic disparities of negativity across continents, different levels of a nation's income, population density, and the level of COVID-19 infection. Countries with more stringent policies were associated with lower levels of negativity, a relationship that weakened in later phases of the pandemic. This study provides the first global and multilingual evaluation of the public's real-time mental health signals to COVID-19 at a large spatial and temporal scale. We offer an empirical framework to monitor mental health signals globally, helping international authorizations, including the United Nations and World Health Organization, to design smart country-specific mental health initiatives in response to the ongoing pandemic and future public emergencies.
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Affiliation(s)
- Siqin Wang
- School of Earth and Environmental Sciences, University of Queensland, Brisbane, Queensland, Australia
- Spatial Data Lab, Centre for Geographic Analysis, Harvard University, MA, USA
| | - Xiao Huang
- Department of Geosciences, University of Arkansas, AR, USA
- Spatial Data Lab, Centre for Geographic Analysis, Harvard University, MA, USA
| | - Tao Hu
- Department of Geography, Oklahoma State University, OK, USA
- Spatial Data Lab, Centre for Geographic Analysis, Harvard University, MA, USA
| | - Bing She
- Institute for Social Research, University of Michigan, MI, USA
| | - Mengxi Zhang
- Department of Nutrition and Health Science, Ball State University, IN, USA
- Spatial Data Lab, Centre for Geographic Analysis, Harvard University, MA, USA
| | - Ruomei Wang
- School of Earth and Environmental Sciences, University of Queensland, Brisbane, Queensland, Australia
| | - Oliver Gruebner
- Department of Geography, University of Zurich, Switzerland
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Switzerland
| | - Muhammad Imran
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Qatar
| | - Jonathan Corcoran
- School of Earth and Environmental Sciences, University of Queensland, Brisbane, Queensland, Australia
| | - Yan Liu
- School of Earth and Environmental Sciences, University of Queensland, Brisbane, Queensland, Australia
| | - Shuming Bao
- China Data Institute and Future Data Lab, MI, USA
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19
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Mavragani A, Suh YK. A Comprehensive Analysis of COVID-19 Vaccine Discourse by Vaccine Brand on Twitter in Korea: Topic and Sentiment Analysis. J Med Internet Res 2023; 25:e42623. [PMID: 36603153 PMCID: PMC9891356 DOI: 10.2196/42623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 10/28/2022] [Accepted: 01/05/2023] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND The unprecedented speed of COVID-19 vaccine development and approval has raised public concern about its safety. However, studies on public discourses and opinions on social media focusing on adverse events (AEs) related to COVID-19 vaccine are rare. OBJECTIVE This study aimed to analyze Korean tweets about COVID-19 vaccines (Pfizer, Moderna, AstraZeneca, Janssen, and Novavax) after the vaccine rollout, explore the topics and sentiments of tweets regarding COVID-19 vaccines, and examine their changes over time. We also analyzed topics and sentiments focused on AEs related to vaccination using only tweets with terms about AEs. METHODS We devised a sophisticated methodology consisting of 5 steps: keyword search on Twitter, data collection, data preprocessing, data analysis, and result visualization. We used the Twitter Representational State Transfer application programming interface for data collection. A total of 1,659,158 tweets were collected from February 1, 2021, to March 31, 2022. Finally, 165,984 data points were analyzed after excluding retweets, news, official announcements, advertisements, duplicates, and tweets with <2 words. We applied a variety of preprocessing techniques that are suitable for the Korean language. We ran a suite of analyses using various Python packages, such as latent Dirichlet allocation, hierarchical latent Dirichlet allocation, and sentiment analysis. RESULTS The topics related to COVID-19 vaccines have a very large spectrum, including vaccine-related AEs, emotional reactions to vaccination, vaccine development and supply, and government vaccination policies. Among them, the top major topic was AEs related to COVID-19 vaccination. The AEs ranged from the adverse reactions listed in the safety profile (eg, myalgia, fever, fatigue, injection site pain, myocarditis or pericarditis, and thrombosis) to unlisted reactions (eg, irregular menstruation, changes in appetite and sleep, leukemia, and deaths). Our results showed a notable difference in the topics for each vaccine brand. The topics pertaining to the Pfizer vaccine mainly mentioned AEs. Negative public opinion has prevailed since the early stages of vaccination. In the sentiment analysis based on vaccine brand, the topics related to the Pfizer vaccine expressed the strongest negative sentiment. CONCLUSIONS Considering the discrepancy between academic evidence and public opinions related to COVID-19 vaccination, the government should provide accurate information and education. Furthermore, our study suggests the need for management to correct the misinformation related to vaccine-related AEs, especially those affecting negative sentiments. This study provides valuable insights into the public discourses and opinions regarding COVID-19 vaccination.
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Affiliation(s)
| | - Young-Kyoon Suh
- School of Computer Science and Engineering, Kyungpook National University, Daegu, Republic of Korea.,Department of Data Convergence Computing, Kyungpook National University, Daegu, Republic of Korea
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20
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Guo Y, Zu L, Chen D, Zhang H. A Study of Public Attitudes toward Shanghai's Image under the Influence of COVID-19: Evidence from Comments on Sina Weibo. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:2297. [PMID: 36767664 PMCID: PMC9915454 DOI: 10.3390/ijerph20032297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 01/21/2023] [Accepted: 01/24/2023] [Indexed: 06/18/2023]
Abstract
With the advent of the Internet era, Chinese users tend to choose to express their opinions on social media platforms represented by Sina Weibo. The changes in people's emotions toward cities from the microblogging texts can reflect the image of cities presented on mainstream social media, and thus target a good image of cities. In this paper, we collected microblog data containing "Shanghai" from 1 January 2019 to 1 September 2022 by Python technology, and we used three methods: Term Frequency-Inverse Document Frequency keyword statistics, Latent Dirichlet Allocation theme model construction, and sentiment analysis by Zhiwang Sentiment Dictionary. We also explore the impact of the COVID-19 epidemic on Shanghai's urban image in the context of the "Shanghai Territorial Static Management", an important public opinion topic during the COVID-19 epidemic. The results of the study show that the "Shanghai-wide static management" of COVID-19 epidemic has significantly reduced the public's perception of Shanghai and negatively affected the city's image. By analyzing the data results, we summarize the basic characteristics of Shanghai's city image and provide strategies for communicating Shanghai's city image in the post-epidemic era.
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Affiliation(s)
- Yanlong Guo
- Social Innovation Design Research Centre, Anhui University, Hefei 203106, China
- Anhui Institute of Contemporary Studies, Anhui Academy of Social Sciences, Hefei 203106, China
| | - Lan Zu
- Social Innovation Design Research Centre, Anhui University, Hefei 203106, China
| | - Denghang Chen
- Department of Science and Technology Communication, University of Science and Technology of China, Hefei 203106, China
| | - Han Zhang
- College of Environmental Science and Engineering, Ocean University of China, Qingdao 266000, China
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Weerasinghe S, Oyebode O, Orji R, Matwin S. Dynamics of emotion trends in Canadian Twitter users during COVID-19 confinement in relation to caseloads: Artificial intelligence-based emotion detection approach. Digit Health 2023; 9:20552076231171496. [PMID: 37252262 PMCID: PMC10214063 DOI: 10.1177/20552076231171496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 04/06/2023] [Indexed: 05/31/2023] Open
Abstract
Multiple waves of COVID-19 have significantly impacted the emotional well-being of all, but many were subject to additional risks associated with forced regulations. The objective of this research was to assess the immediate emotional impact, expressed by Canadian Twitter users, and to estimate the linear relationship, with the vicissitudes of COVID caseloads, using ARIMA time-series regression. We developed two Artificial Intelligence-based algorithms to extract tweets using 18 semantic terms related to social confinement and locked down and then geocoded them to tag Canadian provinces. Tweets (n = 64,732) were classified as positive, negative, and neutral sentiments using a word-based Emotion Lexicon. Our results indicated: that Tweeters were expressing a higher daily percentage of negative sentiments representing, negative anticipation (30.1%), fear (28.1%), and anger (25.3%), than positive sentiments comprising positive anticipation (43.7%), trust (41.4%), and joy (14.9%), and neutral sentiments with mostly no emotions, when hash-tagged social confinement and locked down. In most provinces, negative sentiments took on average two to three days after caseloads increase to emerge, whereas positive sentiments took a slightly longer period of six to seven days to submerge. As daily caseloads increase, negative sentiment percentage increases in Manitoba (by 68% for 100 caseloads increase) and Atlantic Canada (by 89% with 100 caseloads increase) in wave 1(with 30% variations explained), while other provinces showed resilience. The opposite was noted in the positive sentiments. The daily percentage of emotional expression variations explained by daily caseloads in wave one were 30% for negative, 42% for neutral, and 2.1% for positive indicating that the emotional impact is multifactorial. These provincial-level impact differences with varying latency periods should be considered when planning geographically targeted, time-sensitive, confinement-related psychological health promotion efforts. Artificial Intelligence-based Geo-coded sentiment analysis of Twitter data opens possibilities for targeted rapid emotion sentiment detection opportunities.
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Affiliation(s)
- Swarna Weerasinghe
- Department of Community Health and
Epidemiology, Faculty of Medicine, Dalhousie University, Halifax, Canada
| | - Oladapo Oyebode
- Faculty of Computer Science, Dalhousie University, Halifax, Canada
| | - Rita Orji
- Faculty of Computer Science, Dalhousie University, Halifax, Canada
| | - Stan Matwin
- Faculty of Computer Science, Dalhousie University, Halifax, Canada
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Liu Y, Shi J, Zhao C, Zhang C. Generalizing factors of COVID-19 vaccine attitudes in different regions: A summary generation and topic modeling approach. Digit Health 2023; 9:20552076231188852. [PMID: 37485330 PMCID: PMC10359653 DOI: 10.1177/20552076231188852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Accepted: 06/26/2023] [Indexed: 07/25/2023] Open
Abstract
Objective The goal of this study is to use summary generation and topic modeling to identify factors contributing to vaccine attitudes for three different vaccine brands, with the aim of generalizing these factors across different regions. Methods A total of 5562 tweets about three vaccine brands (Sinovac, AstraZeneca, and Pfizer) were collected from 14 December 2020 to 30 December 2021. BERTopic clustering is used to group the tweets into topics, and then contrastive learning (CL) is adopted to generate summaries of each topic. The main content of each topic is generalized into three factors that contribute to vaccine attitudes: vaccine-related factors, health system-related factors, and individual social attributes. Results BERTopic clustering outperforms Latent Dirichlet Allocation clustering in our analysis. It can also be found that using CL for summary generation helped to better model the topics, particularly at the center-point of the clustering. Our model identifies three main factors contributing to vaccine attitudes that are consistent across different regions. Conclusions Our study demonstrates the effectiveness of deep learning methods for identifying factors contributing to vaccine attitudes in different regions. By determining these factors, policymakers and medical institutions can develop more effective strategies for addressing concerns related to the vaccination process.
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Affiliation(s)
- Yang Liu
- School of Information Management, Wuhan University, Wuhan, China
| | - Jiale Shi
- School of Computer Science, Wuhan University, Wuhan, China
| | - Chenxu Zhao
- School of Computer Science, Wuhan University, Wuhan, China
| | - Chengzhi Zhang
- Department of Information Management, Nanjing University of Science & Technology, Nanjing, China
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Singla RK, De R, Efferth T, Mezzetti B, Sahab Uddin M, Ntie-Kang F, Wang D, Schultz F, Kharat KR, Devkota HP, Battino M, Sur D, Lordan R, Patnaik SS, Tsagkaris C, Sai CS, Tripathi SK, Găman MA, Ahmed MEO, González-Burgos E, Babiaka SB, Paswan SK, Odimegwu JI, Akram F, Simal-Gandara J, Urquiza MS, Tikhonov A, Mondal H, Singla S, Lonardo SD, Mulholland EJ, Cenanovic M, Maigoro AY, Giampieri F, Lee S, Tzvetkov NT, Louka AM, Verma P, Chopra H, Olea SP, Khan J, Alvarez Suarez JM, Zheng X, Tomczyk M, Sabnani MK, Medina CDV, Khalid GM, Boyina HK, Georgiev MI, Supuran CT, Sobarzo-Sánchez E, Fan TP, Pittala V, Sureda A, Braidy N, Russo GL, Vacca RA, Banach M, Lizard G, Zarrouk A, Hammami S, Orhan IE, Aggarwal BB, Perry G, Miller MJ, Heinrich M, Bishayee A, Kijjoa A, Arkells N, Bredt D, Wink M, Fiebich BL, Kiran G, Yeung AWK, Gupta GK, Santini A, Lucarini M, Durazzo A, El-Demerdash A, Dinkova-Kostova AT, Cifuentes A, Souto EB, Zubair MAM, Badhe P, Echeverría J, Horbańczuk JO, Horbanczuk OK, Sheridan H, Sheshe SM, Witkowska AM, Abu-Reidah IM, Riaz M, Ullah H, Oladipupo AR, Lopez V, Sethiya NK, Shrestha BG, Ravanan P, Gupta SC, Alzahrani QE, Dama Sreedhar P, Xiao J, Moosavi MA, Subramani PA, Singh AK, Chettupalli AK, Patra JK, Singh G, Karpiński TM, Al-Rimawi F, Abiri R, Ahmed AF, Barreca D, Vats S, Amrani S, Fimognari C, Mocan A, Hritcu L, Semwal P, Shiblur Rahaman M, Emerald M, Akinrinde AS, Singh A, Joshi A, Joshi T, Khan SY, Balla GOA, Lu A, Pai SR, Ghzaiel I, Acar N, Es-Safi NE, Zengin G, Kureshi AA, Sharma AK, Baral B, Rani N, Jeandet P, Gulati M, Kapoor B, Mohanta YK, Emam-Djomeh Z, Onuku R, Depew JR, Atrooz OM, Goh BH, Andrade JC, Konwar B, Shine VJ, Ferreira JMLD, Ahmad J, Chaturvedi VK, Skalicka-Woźniak K, Sharma R, Gautam RK, Granica S, Parisi S, Kumar R, Atanasov AG, Shen B. The International Natural Product Sciences Taskforce (INPST) and the power of Twitter networking exemplified through #INPST hashtag analysis. PHYTOMEDICINE : INTERNATIONAL JOURNAL OF PHYTOTHERAPY AND PHYTOPHARMACOLOGY 2023; 108:154520. [PMID: 36334386 DOI: 10.1016/j.phymed.2022.154520] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 07/12/2022] [Accepted: 10/21/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND The development of digital technologies and the evolution of open innovation approaches have enabled the creation of diverse virtual organizations and enterprises coordinating their activities primarily online. The open innovation platform titled "International Natural Product Sciences Taskforce" (INPST) was established in 2018, to bring together in collaborative environment individuals and organizations interested in natural product scientific research, and to empower their interactions by using digital communication tools. METHODS In this work, we present a general overview of INPST activities and showcase the specific use of Twitter as a powerful networking tool that was used to host a one-week "2021 INPST Twitter Networking Event" (spanning from 31st May 2021 to 6th June 2021) based on the application of the Twitter hashtag #INPST. RESULTS AND CONCLUSION The use of this hashtag during the networking event period was analyzed with Symplur Signals (https://www.symplur.com/), revealing a total of 6,036 tweets, shared by 686 users, which generated a total of 65,004,773 impressions (views of the respective tweets). This networking event's achieved high visibility and participation rate showcases a convincing example of how this social media platform can be used as a highly effective tool to host virtual Twitter-based international biomedical research events.
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Affiliation(s)
- Rajeev K Singla
- Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Xinchuan Road 2222, Chengdu, Sichuan, China; School of Pharmaceutical Sciences, Lovely Professional University, Phagwara, Punjab-144411, India
| | - Ronita De
- ICMR-National Institute of Cholera and Enteric Diseases, P-33, CIT Rd, Subhas Sarobar Park, Phool Bagan, Beleghata, Kolkata, West Bengal 700010, India
| | - Thomas Efferth
- Department of Pharmaceutical Biology, Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg University, Mainz, Germany
| | - Bruno Mezzetti
- Department of Agriculture, Food and Environmental Sciences (D3A) Università Politecnica Delle Marche Ancona, IT, Italy
| | - Md Sahab Uddin
- Department of Pharmacy, Southeast University, Dhaka, Bangladesh; Pharmakon Neuroscience Research Network, Dhaka, Bangladesh
| | - Fidele Ntie-Kang
- Department of Chemistry, Faculty of Science, University of Buea, Buea P.O. Box 63, Cameroon
| | - Dongdong Wang
- Centre for Metabolism, Obesity, and Diabetes Research, Department of Medicine, McMaster University, HSC 4N71, 1280 Main Street West, Hamilton, ON L8S 4K1, Canada
| | - Fabien Schultz
- Technical University of Berlin, Institute of Biotechnology, Faculty III - Process Sciences, Gustav-Meyer-Allee 25, Berlin 13355, Germany; Neubrandenburg University of Applied Sciences, Department of Agriculture and Food Sciences, Brodaer Str. 2, Neubrandenburg 17033, Germany
| | | | - Hari Prasad Devkota
- Graduate School of Pharmaceutical Sciences, Kumamoto University, 5-1Oe-honmachi, Kumamoto 862-0973, Japan; Program for Leading Graduate Schools, HIGO Program, Kumamoto University, Japan
| | - Maurizio Battino
- Department of Clinical Sciences, Faculty of Medicine, Polytechnic University of Marche, Ancona 60131, Italy; International Research Center for Food Nutrition and Safety, Jiangsu University, Zhenjiang 212013, China
| | - Daniel Sur
- Department of Medical Oncology, "Iuliu Hatieganu" University of Medicine and Pharmacy Cluj-Napoca, Romania
| | - Ronan Lordan
- Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, PA, United States
| | - Sourav S Patnaik
- Department of Bioengineering, University of Texas at Dallas, Richardson, TX, United States
| | | | - Chandragiri Siva Sai
- Amity Institute of Pharmacy, Amity University, Uttar Pradesh, Lucknow Campus, Gomati Nagar, Lucknow, Uttar Pradesh 226010, India
| | - Surya Kant Tripathi
- Cancer Drug Resistance Laboratory, National Institute of Technology Rourkela, Odisha-769008, India
| | - Mihnea-Alexandru Găman
- ″Carol Davila" University of Medicine and Pharmacy, 8 Eroii Sanitari Boulevard, Bucharest, Romania; Center of Hematology and Bone Marrow Transplantation, Fundeni Clinical Institute, 258 Fundeni Road, Bucharest, Romania
| | - Mosa E O Ahmed
- Department of Pharmacognosy, Faculty of Pharmacy, Al Neelain University, Khartoum, Sudan
| | - Elena González-Burgos
- Department of Pharmacology, Pharmacognosy and Botany, University Complutense of Madrid, Spain
| | - Smith B Babiaka
- Department of Chemistry, Faculty of Science, University of Buea, Buea P.O. Box 63, Cameroon
| | | | | | - Faizan Akram
- Bahawalpur College of Pharmacy (BCP), Bahawalpur Medical and Dental College (BMDC), Bahawalpur, Pakistan
| | - Jesus Simal-Gandara
- Universidade de Vigo, Nutrition and Bromatology Group, Department of Analytical Chemistry and Food Science, Faculty of Science, Ourense E-32004, Spain
| | | | - Aleksei Tikhonov
- Translational Research Laboratory in Immunotherapy, Gustave Roussy, Villejuif, France
| | - Himel Mondal
- Department of Physiology, All India Institute of Medical Sciences, Deoghar, Jharkhand, India
| | - Shailja Singla
- iGlobal Research and Publishing Foundation, New Delhi, India
| | - Sara Di Lonardo
- Research Institute on Terrestrial Ecosystems-Italian National Research Council (IRET-CNR), Via Madonna del Piano 10, Sesto Fiorentino Fi 50019, Italy
| | - Eoghan J Mulholland
- Gastrointestinal Stem Cell Biology Laboratory, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom; Somerville College, University of Oxford, Oxford, United Kingdom
| | | | | | - Francesca Giampieri
- Department of Biochemistry, Faculty of Sciences, King Abdulaziz University, Jeddah, Saudi Arabia; Research Group on Food, Nutritional Biochemistry and Health, Universidad Europea del Atlántico, Santander, Spain
| | - Soojin Lee
- Department of Bioscience and Biotechnology, Chungnam National University, Republic of Korea
| | - Nikolay T Tzvetkov
- Department of Biochemical Pharmacology and Drug Design, Institute of Molecular Biology "Roumen Tsanev", Bulgarian Academy of Sciences, Bulgaria
| | | | - Pritt Verma
- Department of Pharmacology, CSIR-NBRI, Lucknow, India
| | - Hitesh Chopra
- Chitkara College of Pharmacy, Chitkara University, Punjab, India
| | | | - Johra Khan
- Department of Medical Laboratory Sciences, College of Applied Medical Sciences, Majmaah University, Al Majmaah 11952, Saudi Arabia
| | - José M Alvarez Suarez
- Departamento de Ingeniería en Alimentos, Colegio de Ciencias e Ingenierías, Universidad San Francisco de Quito, Quito, Ecuador
| | - Xiaonan Zheng
- Department of Urology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Michał Tomczyk
- Department of Pharmacognosy, Faculty of Pharmacy with the Division of Laboratory Medicine, Medical University of Białystok, ul. Mickiewicza 2a, Białystok 15-230, Poland
| | - Manoj Kumar Sabnani
- The University of Texas at Arlington, United States; Alloy Therapeutics, United States
| | | | - Garba M Khalid
- Pharmaceutical Engineering Group, School of Pharmacy, Queen's University, Belfast BT9, United Kingdom
| | - Hemanth Kumar Boyina
- School of Pharmacy, Department of Pharmacology, Anurag University, Venkatapur, Medchal, Hyderabad, Telangana 500088, India
| | - Milen I Georgiev
- Laboratory of Metabolomics, Stephan Angeloff Institute of Microbiology, Bulgarian Academy of Sciences, 139 Ruski Blvd., Plovdiv 4000, Bulgaria
| | | | - Eduardo Sobarzo-Sánchez
- Instituto de Investigación y Postgrado, Facultad de Ciencias de la Salud, Universidad Central de Chile, Santiago 8330507, Chile; Department of Organic Chemistry, Faculty of Pharmacy, University of Santiago de Compostela, Santiago de Compostela 15782, Spain
| | - Tai-Ping Fan
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, Faculty of Life Science and Medicine, Northwest University, Xi'an, China
| | - Valeria Pittala
- Department of Drug and Health Sciences, University of Catania, Catania, Italy
| | - Antoni Sureda
- Research Group in Community Nutrition and Oxidative Stress, University of the Balearic Islands-IUNICS, Health Research Institute of Balearic Islands (IdISBa), and CIBEROBN (Physiopathology of Obesity and Nutrition), Palma, Balearic Islands E-07122, Spain
| | - Nady Braidy
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, Australia
| | - Gian Luigi Russo
- National Research Council, Institute of Food Sciences, Avellino 83100, Italy
| | - Rosa Anna Vacca
- Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, National Council of Research, Bari 70126, Italy
| | - Maciej Banach
- Department of Preventive Cardiology and Lipidology, Medical University of Lodz (MUL), Lodz, Poland; Cardiovascular Research Centre, University of Zielona Gora, Zielona Gora, Poland
| | - Gérard Lizard
- Université de Bourgogne / Inserm, Laboratoire Bio-PeroxIL, Faculté des Sciences Gabriel, 6 Boulevard Gabriel, Dijon 21000 France
| | - Amira Zarrouk
- University of Monastir (Tunisia), Faculty of Medicine, LR-NAFS 'Nutrition - Functional Food & Vascular Health', Tunisia
| | - Sonia Hammami
- University of Monastir (Tunisia), Faculty of Medicine, LR-NAFS 'Nutrition - Functional Food & Vascular Health', Tunisia
| | - Ilkay Erdogan Orhan
- Department of Pharmacognosy, Faculty of Pharmacy, Gazi University, Ankara 06330, Türkiye
| | | | - George Perry
- Department of Neuroscience, Developmental, and Regenerative Biology, University of Texas, United States
| | | | | | - Anupam Bishayee
- College of Osteopathic Medicine, Lake Erie College of Osteopathic Medicine, Bradenton, FL 34211, United States
| | - Anake Kijjoa
- Instituto de Ciências Biomédicas Abel Salazar e CIIMAR, Universidade do Porto, Portugal
| | - Nicolas Arkells
- International Natural Product Sciences Taskforce (INSPT), United States
| | | | - Michael Wink
- Heidelberg University, Institute of Pharmacy and Molecular Biotechnology (IPMB), Heidelberg 69120, Germany
| | - Bernd L Fiebich
- Neurochemistry and Neuroimmunology Research Group, Department of Psychiatry and Psychotherapy, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | | | - Andy Wai Kan Yeung
- Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, University of Hong Kong, Hong Kong, China
| | - Girish Kumar Gupta
- Department of Pharmaceutical Chemistry, Sri Sai College of Pharmacy, Badhani, Pathankot, Punjab, India
| | - Antonello Santini
- University of Napoli Federico II, Department of Pharmacy. Via D Montesano 49, Napoli 80131, Italy
| | - Massimo Lucarini
- CREA-Research Centre for Food and Nutrition, Via Ardeatina 546 00178 Rome, Italy
| | - Alessandra Durazzo
- CREA-Research Centre for Food and Nutrition, Via Ardeatina 546 00178 Rome, Italy
| | - Amr El-Demerdash
- Metabolic Biology & Biological Chemistry Department, John Innes Centre, Norwich Research Park, Norwich NR4 7UH, United Kingdom; Organic Chemistry Division, Chemistry Department, Faculty of Science, Mansoura University, Mansoura 35516, Egypt
| | | | | | - Eliana B Souto
- Department of Pharmaceutical Technology, Faculty of Pharmacy, University of Porto, Rua de Jorge Viterbo Ferreira, 228, Porto 4050-313, Portugal
| | | | - Pravin Badhe
- Swalife Foundation, India; Swalife Biotech Ltd, Ireland; Sinhgad College of Pharmacy, Vadgaon (BK) Pune Maharashtra India
| | - Javier Echeverría
- Departamento de Ciencias del Ambiente, Facultad de Química y Biología, Universidad de Santiago de Chile, Chile
| | - Jarosław Olav Horbańczuk
- Institute of Genetics and Animal Biotechnology of the Polish Academy of Sciences, Jastrzębiec 05-552, Poland
| | - Olaf K Horbanczuk
- Department of Technique and Food Product Development, Warsaw University of Life Sciences (WULS-SGGW) 159c Nowoursynowska, Warsaw 02-776, Poland
| | - Helen Sheridan
- The NatPro Centre. Trinity College Dublin. Dublin 2, Ireland
| | | | | | - Ibrahim M Abu-Reidah
- School of Science and the Environment, Grenfell Campus, Memorial University of Newfoundland, Corner Brook A2H 5G4, Canada
| | - Muhammad Riaz
- Department of Pharmacy, Shaheed Benazir Bhutto University, Sheringal 18050, Pakistan
| | - Hammad Ullah
- Department of Pharmacy, University of Naples Federico II, Naples 80131, Italy
| | - Akolade R Oladipupo
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Lagos, Nigeria; Department of Chemistry, Nelson Mandela University, Port Elizabeth, South Africa
| | - Víctor Lopez
- Department of Pharmacy, Universidad San Jorge, Villanueva de Gállego (Zaragoza), Spain
| | | | | | - Palaniyandi Ravanan
- Department of Microbiology, School of Life Sciences, Central University of Tamil Nadu, Thiruvarur, India
| | - Subash Chandra Gupta
- Department of Biochemistry, Institute of Science, Banaras Hindu University, Varanasi, India; Department of Biochemistry, All India Institute of Medical Sciences, Guwahati, Assam, India
| | - Qushmua E Alzahrani
- Department of Pharmacy/Nursing Medicine Health and Environment, University of the Region of Joinville (UNIVILLE) Brazil, Sana Catarina, Joinville, Brazil
| | | | | | - Mohammad Amin Moosavi
- Molecular Medicine Department, Institute of Medical Biotechnology, National Institute of Genetics Engineering and Biotechnology, Tehran P.O. Box: 14965/161, Iran
| | - Parasuraman Aiya Subramani
- Independent Researcher, Vels Institute of Science, Technology and Advanced Studies (VISTAS), Chennai, India - 600048. formerly, Pallavaram, Chennai 600117, India
| | - Amit Kumar Singh
- Department of Biochemistry, University of Allahabad, Prayagraj 211002 India
| | | | - Jayanta Kumar Patra
- Research Institute of Integrative Life Sciences, Dongguk University-Seoul, Goyangsi 10326, Republic of Korea
| | - Gopal Singh
- Department of Plant Functional Metabolomics, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland
| | - Tomasz M Karpiński
- Chair and Department of Medical Microbiology, Poznań University of Medical Sciences, Wieniawskiego 3, Poznań 61-712, Poland
| | | | - Rambod Abiri
- Department of Forestry Science and Biodiversity, Faculty of Forestry and Environment, Universiti Putra Malaysia, UPM Serdang, Selangor 43400, Malaysia
| | - Atallah F Ahmed
- Department of Pharmacognosy, College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia; Department of Pharmacognosy, Faculty of Pharmacy, Mansoura University, Mansoura 35516, Egypt
| | - Davide Barreca
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, Università degli Studi di Messina, Messina, Italy
| | - Sharad Vats
- Department of Bioscience and Biotechnology, Banasthali Vidyapith, Rajasthan 304022, India
| | - Said Amrani
- Laboratoire de Biologie et de Physiologie des Organismes, Faculté des Sciences Biologiques, USTHB, Bab Ezzouar, Alger, Algeria
| | | | - Andrei Mocan
- Department of Pharmaceutical Botany, Iuliu Hațieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Lucian Hritcu
- Department of Biology, Alexandru Ioan Cuza University of Iasi, Bd. Carol I, No. 11, Iasi 700506, Romania
| | - Prabhakar Semwal
- Department of Life Sciences, Graphic Era Deemed to be University, Dehradun, Uttarakhand 248002, India
| | - Md Shiblur Rahaman
- Department of Environmental and Preventive Medicine, Jichi Medical University School of Medicine, 3311-1 Yakushiji, Shimotsuke, Tochigi 329-0498, Japan
| | - Mila Emerald
- PHYTOCEUTICALS International™ & NOVOTEK Global Solutions™, Canada
| | - Akinleye Stephen Akinrinde
- Department of Veterinary Physiology and Biochemistry, Faculty of Veterinary Medicine, University of Ibadan, Ibadan, Nigeria
| | | | - Ashima Joshi
- Sardar Bhagwan Singh University, Balawala, Dehradun, India
| | - Tanuj Joshi
- Department of Pharmaceutical Sciences, Bhimtal, Kumaun University (Nainital), India
| | - Shafaat Yar Khan
- Research Lab III, Hematology & Vascular Biology, Department of Zoology, University of Sargodha, Sargodha, Pakistan
| | - Gareeballah Osman Adam Balla
- Department of Pharmacology, College of Veterinary Medicine, Sudan University of Science and Technology, Hilat Kuku, Khartoum North P.O. Box No. 204, Sudan
| | - Aiping Lu
- School of Chinese Medicine, Hong Kong Baptist University, HongKong, China
| | - Sandeep Ramchandra Pai
- Department of Botany, Rayat Shikshan Sanstha's, Dada Patil Mahavidyalaya, Karjat, Maharashtra, India
| | - Imen Ghzaiel
- Université de Bourgogne, Inserm, Laboratoire Bio - PeroxIL, Faculté des Sciences Gabriel, 6 Boulevard Gabriel, Dijon 21000 France; University Tunis El Manar, Tunis, Tunisia
| | | | - Nour Eddine Es-Safi
- Mohammed V University in Rabat, LPCMIO, Materials Science Center (MSC), Ecole Normale Supérieure, Rabat, Morocco
| | - Gokhan Zengin
- Department of Biology, Science Faculty, Selcuk University, Konya, Turkey
| | - Azazahemad A Kureshi
- Department of Chemistry, Sardar Vallabhbhai National Institute of Technology, Surat, India
| | | | | | - Neeraj Rani
- Department of Pharmaceutical Sciences, Chaudhary Bansilal University, Bhiwani, Haryana, India
| | - Philippe Jeandet
- University of Reims, Research Unit Induced Resistance and Plant Bioprotection, USC INRAe 1488, Reims, France
| | - Monica Gulati
- School of Pharmaceutical Sciences, Lovely Professional University, Jalandhar-Delhi G.T. Road (NH 1) Phagwara, Punjab 144411 India
| | - Bhupinder Kapoor
- School of Pharmaceutical Sciences, Lovely Professional University, Jalandhar-Delhi G.T. Road (NH 1) Phagwara, Punjab 144411 India
| | - Yugal Kishore Mohanta
- Department of Applied Biology, School of Biological Sciences, University of Science and Technology Meghalaya (USTM), Techno City, Kling Road, Baridua, Ri-Bhoi, Meghalaya 793101, India
| | | | - Raphael Onuku
- Department of Pharmaceutical and Medicinal Chemistry, Faculty of Pharmaceutical Sciences, University of Nigeria, Nigeria
| | | | - Omar M Atrooz
- Department of Biological Sciences, Mutah University, Jordan
| | - Bey Hing Goh
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China; Biofunctional Molecule Exploratory (BMEX) Research Group, School of Pharmacy, Monash University Malaysia, Subang Jaya, Malaysia
| | - Jose Carlos Andrade
- TOXRUN - Toxicology Research Unit, University Institute of Health Sciences, CESPU, Gandra, Portugal
| | | | - V J Shine
- Cancer Research Program, Rajiv Gandhi Centre for Biotechnology (RGCB), Thiruvananthapuram, Kerala 695014, India
| | | | - Jamil Ahmad
- Department of Human Nutrition, The University of Agriculture Peshawar, Khyber Pakhtunkhwa, Pakistan
| | - Vivek K Chaturvedi
- Department of Gastroenterology, Institute of Medical Sciences, Banaras Hindu University, Varanasi 221005, India
| | | | - Rohit Sharma
- Department of Rasa Shastra and Bhaishajya Kalpana, Faculty of Ayurveda, Institute of Medical Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh 221005, India
| | - Rupesh K Gautam
- Deparment of Pharmacology, Indore Institute of Pharmacy, IIST Campus, Rau-Indore-453331, India
| | - Sebastian Granica
- Microbiota Lab, Department of Pharmacognosy and Molecular Basis of Phytotherapy, Medical University of Warsaw, Poland
| | - Salvatore Parisi
- Lourdes Matha Institute of Hotel Management and Catering Technology, Kerala State, India
| | - Rishabh Kumar
- School of Medical and Allied Sciences, K.R. Mangalam University, Sohna Road, Gurugram, Haryana 122103, India
| | - Atanas G Atanasov
- Ludwig Boltzmann Institute for Digital Health and Patient Safety, Medical University of Vienna, Spitalgasse 23, Vienna 1090, Austria; Department of Pharmaceutical Sciences, University of Vienna, Althanstraße 14, Vienna 1090, Austria; Institute of Genetics and Animal Biotechnology of the Polish Academy of Sciences, Jastrzebiec, Magdalenka 05-552, Poland.
| | - Bairong Shen
- Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Xinchuan Road 2222, Chengdu, Sichuan, China.
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Basch C, Eysenbach G, Nakayama Y, Suda T, Uno T, Hashimoto T, Toyoda M, Yoshinaga N, Kitsuregawa M, Rocha LEC. Evolution of Public Opinion on COVID-19 Vaccination in Japan: Large-Scale Twitter Data Analysis. J Med Internet Res 2022; 24:e41928. [PMID: 36343186 PMCID: PMC9856430 DOI: 10.2196/41928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 10/09/2022] [Accepted: 10/24/2022] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND Vaccines are promising tools to control the spread of COVID-19. An effective vaccination campaign requires government policies and community engagement, sharing experiences for social support, and voicing concerns about vaccine safety and efficiency. The increasing use of online social platforms allows us to trace large-scale communication and infer public opinion in real time. OBJECTIVE This study aimed to identify the main themes in COVID-19 vaccine-related discussions on Twitter in Japan and track how the popularity of the tweeted themes evolved during the vaccination campaign. Furthermore, we aimed to understand the impact of critical social events on the popularity of the themes. METHODS We collected more than 100 million vaccine-related tweets written in Japanese and posted by 8 million users (approximately 6.4% of the Japanese population) from January 1 to October 31, 2021. We used Latent Dirichlet Allocation to perform automated topic modeling of tweet text during the vaccination campaign. In addition, we performed an interrupted time series regression analysis to evaluate the impact of 4 critical social events on public opinion. RESULTS We identified 15 topics grouped into the following 4 themes: (1) personal issue, (2) breaking news, (3) politics, and (4) conspiracy and humor. The evolution of the popularity of themes revealed a shift in public opinion, with initial sharing of attention over personal issues (individual aspect), collecting information from news (knowledge acquisition), and government criticism to focusing on personal issues. Our analysis showed that the Tokyo Olympic Games affected public opinion more than other critical events but not the course of vaccination. Public opinion about politics was significantly affected by various social events, positively shifting attention in the early stages of the vaccination campaign and negatively shifting attention later. CONCLUSIONS This study showed a striking shift in public interest in Japan, with users splitting their attention over various themes early in the vaccination campaign and then focusing only on personal issues, as trust in vaccines and policies increased. An interrupted time series regression analysis showed that the vaccination rollout to the general population (under 65 years) increased the popularity of tweets about practical advice and personal vaccination experience, and the Tokyo Olympic Games disrupted public opinion but not the course of the vaccination campaign. The methodology developed here allowed us to monitor the evolution of public opinion and evaluate the impact of social events on public opinion, using large-scale Twitter data.
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Affiliation(s)
| | | | - Yuri Nakayama
- Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan
| | - Towa Suda
- Principles of Informatics Research Division, National Institute of Informatics, Tokyo, Japan
| | - Takeaki Uno
- Principles of Informatics Research Division, National Institute of Informatics, Tokyo, Japan
| | - Takako Hashimoto
- Faculty of Commerce and Economics, Chiba University of Commerce, Ichikawa, Japan
| | - Masashi Toyoda
- Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
| | - Naoki Yoshinaga
- Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
| | - Masaru Kitsuregawa
- Institute of Industrial Science, The University of Tokyo, Tokyo, Japan.,National Institute of Informatics, Tokyo, Japan
| | - Luis E C Rocha
- Department of Economics, Ghent University, Ghent, Belgium.,Department of Physics and Astronomy, Ghent University, Ghent, Belgium
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Stracqualursi L, Agati P. Covid-19 vaccines in Italian public opinion: Identifying key issues using Twitter and Natural Language Processing. PLoS One 2022; 17:e0277394. [PMID: 36395254 PMCID: PMC9671418 DOI: 10.1371/journal.pone.0277394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 10/26/2022] [Indexed: 11/18/2022] Open
Abstract
The COVID-19 pandemic has changed society and people's lives. The vaccination campaign started December 27th 2020 in Italy, together with most countries in the European Union. Social media platforms can offer relevant information about how citizens have experienced and perceived the availability of vaccines and the start of the vaccination campaign. This study aims to use machine learning methods to extract sentiments and topics relating to COVID-19 vaccination from Twitter. Between February and May 2021, we collected over 71,000 tweets containing vaccines-related keywords from Italian Twitter users. To get the dominant sentiment throughout the Italian population, spatial and temporal sentiment analysis was performed using VADER, highlighting sentiment fluctuations strongly influenced by news of vaccines' side effects. Additionally, we investigated the opinions of Italians with respect to different vaccine brands. As a result, 'Oxford-AstraZeneca' vaccine was the least appreciated among people. The application of the Dynamic Latent Dirichlet Allocation (DLDA) model revealed three fundamental topics, which remained stable over time: vaccination plan info, usefulness of vaccinating and concerns about vaccines (risks, side effects and safety). To the best of our current knowledge, this one the first study on Twitter to identify opinions about COVID-19 vaccination in Italy and their progression over the first months of the vaccination campaign. Our results can help policymakers and research communities track public attitudes towards COVID-19 vaccines and help them make decisions to promote the vaccination campaign.
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Affiliation(s)
- Luisa Stracqualursi
- Department of Statistics, University of Bologna, Bologna, BO, Italy
- * E-mail:
| | - Patrizia Agati
- Department of Statistics, University of Bologna, Bologna, BO, Italy
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Hall K, Chang V, Jayne C. A review on Natural Language Processing Models for COVID-19 research. HEALTHCARE ANALYTICS 2022. [PMID: 37520621 PMCID: PMC9295335 DOI: 10.1016/j.health.2022.100078] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
This survey paper reviews Natural Language Processing Models and their use in COVID-19 research in two main areas. Firstly, a range of transformer-based biomedical pretrained language models are evaluated using the BLURB benchmark. Secondly, models used in sentiment analysis surrounding COVID-19 vaccination are evaluated. We filtered literature curated from various repositories such as PubMed and Scopus and reviewed 27 papers. When evaluated using the BLURB benchmark, the novel T-BPLM BioLinkBERT gives groundbreaking results by incorporating document link knowledge and hyperlinking into its pretraining. Sentiment analysis of COVID-19 vaccination through various Twitter API tools has shown the public’s sentiment towards vaccination to be mostly positive. Finally, we outline some limitations and potential solutions to drive the research community to improve the models used for NLP tasks.
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Discussions About COVID-19 Vaccination on Twitter in Turkey: Sentiment Analysis. Disaster Med Public Health Prep 2022; 17:e266. [PMID: 36226686 DOI: 10.1017/dmp.2022.229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVES The present study aims to examine coronavirus disease 2019 (COVID-19) vaccination discussions on Twitter in Turkey and conduct sentiment analysis. METHODS The current study performed sentiment analysis of Twitter data with the artificial intelligence (AI) Natural Language Processing (NLP) method. The tweets were retrieved retrospectively from March 10, 2020, when the first COVID-19 case was seen in Turkey, to April 18, 2022. A total of 10,308 tweets accessed. The data were filtered before analysis due to excessive noise. First, the text is tokenized. Many steps were applied in normalizing texts. Tweets about the COVID-19 vaccines were classified according to basic emotion categories using sentiment analysis. The resulting dataset was used for training and testing ML (ML) classifiers. RESULTS It was determined that 7.50% of the tweeters had positive, 0.59% negative, and 91.91% neutral opinions about the COVID-19 vaccination. When the accuracy values of the ML algorithms used in this study were examined, it was seen that the XGBoost (XGB) algorithm had higher scores. CONCLUSIONS Three of 4 tweets consist of negative and neutral emotions. The responsibility of professional chambers and the public is essential in transforming these neutral and negative feelings into positive ones.
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Hu M, Conway M. Perspectives of the COVID-19 Pandemic on Reddit: Comparative Natural Language Processing Study of the United States, the United Kingdom, Canada, and Australia. JMIR INFODEMIOLOGY 2022; 2:e36941. [PMID: 36196144 PMCID: PMC9521381 DOI: 10.2196/36941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 08/13/2022] [Accepted: 09/15/2022] [Indexed: 11/13/2022]
Abstract
Background
Since COVID-19 was declared a pandemic by the World Health Organization on March 11, 2020, the disease has had an unprecedented impact worldwide. Social media such as Reddit can serve as a resource for enhancing situational awareness, particularly regarding monitoring public attitudes and behavior during the crisis. Insights gained can then be utilized to better understand public attitudes and behaviors during the COVID-19 crisis, and to support communication and health-promotion messaging.
Objective
The aim of this study was to compare public attitudes toward the 2020-2021 COVID-19 pandemic across four predominantly English-speaking countries (the United States, the United Kingdom, Canada, and Australia) using data derived from the social media platform Reddit.
Methods
We utilized a topic modeling natural language processing method (more specifically latent Dirichlet allocation). Topic modeling is a popular unsupervised learning technique that can be used to automatically infer topics (ie, semantically related categories) from a large corpus of text. We derived our data from six country-specific, COVID-19–related subreddits (r/CoronavirusAustralia, r/CoronavirusDownunder, r/CoronavirusCanada, r/CanadaCoronavirus, r/CoronavirusUK, and r/coronavirusus). We used topic modeling methods to investigate and compare topics of concern for each country.
Results
Our consolidated Reddit data set consisted of 84,229 initiating posts and 1,094,853 associated comments collected between February and November 2020 for the United States, the United Kingdom, Canada, and Australia. The volume of posting in COVID-19–related subreddits declined consistently across all four countries during the study period (February 2020 to November 2020). During lockdown events, the volume of posts peaked. The UK and Australian subreddits contained much more evidence-based policy discussion than the US or Canadian subreddits.
Conclusions
This study provides evidence to support the contention that there are key differences between salient topics discussed across the four countries on the Reddit platform. Further, our approach indicates that Reddit data have the potential to provide insights not readily apparent in survey-based approaches.
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Affiliation(s)
- Mengke Hu
- Department of Biomedical Informatics University of Utah Salt Lake City, UT United States
| | - Mike Conway
- Department of Biomedical Informatics University of Utah Salt Lake City, UT United States
- School of Computing & Information Systems University of Melbourne Carlton Australia
- Centre for Digital Transformation of Health University of Melbourne Carlton Australia
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Mir AA, Sevukan R. Sentiment analysis of Indian Tweets about Covid-19 vaccines. J Inf Sci 2022. [PMCID: PMC9482880 DOI: 10.1177/01655515221118049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
People are becoming more reliant on social media networks to express their opinions about various topics and obtain health information. The study is intended to explore and analyse the sentiments of Indian people related to Covid-19 vaccines as well as to visualise the top most frequently occurring terms individuals have used to communicate their ideas on Twitter about Covid-19 vaccines in India. The Tweet Archiver was used to retrieve the Tweets against ‘Covid19vaccine’ and ‘Coronavirusvaccine’ hashtags for the period of 2 months 18 days (4 January 2021–22 March 2021). After collecting data, the Orange software and VOSviewer were used for further analysis. The Tweets were posted across the country, with an immense contribution from Maharashtra (223, 15.58%), followed by Delhi (220, 15.37%) and Tamil Nadu (73, 5.10%). The majority (639, 44.65%) of the Tweets reflect positive sentiments, followed by neutral (521, 38.50%) and negative (241, 16.84%) sentiments, respectively. This signifies that most Twitter users have a favourable opinion towards Covid vaccines in India. Based on the relevance score of the words, the words ‘Delhi heart’, ‘Lung institute’, ‘Gift’, ‘Unite2fightcorona’, and ‘Covid-19 Vaccine’ are the leading words appearing in Tweets. The study illustrates the sentiments of the Indian people towards ‘Covid-19 vaccines’, gains some insights into overall public communication about the topic and complements the existing literature. It can assist health policymakers and administrators in better understanding the polarity (positive, negative, and neutral) of Tweets about Covid-19 vaccines on Twitter to raise public awareness about health concerns and misinformation about the vaccine.
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Affiliation(s)
- Aasif Ahmad Mir
- Department of Library and Information Science, Pondicherry University, India
| | - Rathinam Sevukan
- Department of Library and Information Science, Pondicherry University, India
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Yousef M, Dietrich T, Rundle-Thiele S. Actions Speak Louder Than Words: Sentiment and Topic Analysis of COVID-19 Vaccination on Twitter and Vaccine Uptake. JMIR Form Res 2022; 6:e37775. [PMID: 36007136 PMCID: PMC9484485 DOI: 10.2196/37775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 06/30/2022] [Accepted: 08/23/2022] [Indexed: 11/13/2022] Open
Abstract
Background
The lack of trust in vaccines is a major contributor to vaccine hesitancy. To overcome vaccine hesitancy for the COVID-19 vaccine, the Australian government launched multiple public health campaigns to encourage vaccine uptake. This sentiment analysis examines the effect of public health campaigns and COVID-19–related events on sentiment and vaccine uptake.
Objective
This study aims to examine the relationship between sentiment and COVID-19 vaccine uptake and government actions that impacted public sentiment about the vaccine.
Methods
Using machine learning methods, we collected 137,523 publicly available English language tweets published in Australia between February and October 2021 that contained COVID-19 vaccine–related keywords. Machine learning methods were used to extract topics and sentiments relating to COVID-19 vaccination. The relationship between public vaccination sentiment on Twitter and vaccine uptake was examined.
Results
The majority of collected tweets expressed negative (n=91,052, 66%) rather than positive (n=21,686, 16%) or neutral (n=24,785, 18%) sentiments. Topics discussed within the study time frame included the role of the government in the vaccination rollout, availability and accessibility of the vaccine, and vaccine efficacy. There was a significant positive correlation between negative sentiment and the number of vaccine doses administered daily (r267=.15, P<.05), with positive sentiment showing the inverse effect. Public health campaigns, lockdowns, and antivaccination protests were associated with increased negative sentiment, while vaccination mandates had no significant effect on sentiment.
Conclusions
The study findings demonstrate that negative sentiment was more prevalent on Twitter during the Australian vaccination rollout but vaccine uptake remained high. Australians expressed anger at the slow rollout and limited availability of the vaccine during the study period. Public health campaigns, lockdowns, and antivaccination rallies increased negative sentiment. In contrast, news of increased vaccine availability for the public and government acquisition of more doses were key government actions that reduced negative sentiment. These findings can be used to inform government communication planning.
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Affiliation(s)
- Murooj Yousef
- Social Marketing @ Griffith, Department of Marketing, Griffith University, Nathan, Australia
| | - Timo Dietrich
- Social Marketing @ Griffith, Department of Marketing, Griffith University, Nathan, Australia
| | - Sharyn Rundle-Thiele
- Social Marketing @ Griffith, Department of Marketing, Griffith University, Nathan, Australia
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Abstract
The coronavirus disease 2019 (COVID-19), with new variants, continues to be a constant pandemic threat that is generating socio-economic and health issues in manifold countries. The principal goal of this study is to develop a machine learning experiment to assess the effects of vaccination on the fatality rate of the COVID-19 pandemic. Data from 192 countries are analysed to explain the phenomena under study. This new algorithm selected two targets: the number of deaths and the fatality rate. Results suggest that, based on the respective vaccination plan, the turnout in the participation in the vaccination campaign, and the doses administered, countries under study suddenly have a reduction in the fatality rate of COVID-19 precisely at the point where the cut effect is generated in the neural network. This result is significant for the international scientific community. It would demonstrate the effective impact of the vaccination campaign on the fatality rate of COVID-19, whatever the country considered. In fact, once the vaccination has started (for vaccines that require a booster, we refer to at least the first dose), the antibody response of people seems to prevent the probability of death related to COVID-19. In short, at a certain point, the fatality rate collapses with increasing doses administered. All these results here can help decisions of policymakers to prepare optimal strategies, based on effective vaccination plans, to lessen the negative effects of the COVID-19 pandemic crisis in socioeconomic and health systems.
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Zulfiker MS, Kabir N, Biswas AA, Zulfiker S, Uddin MS. Analyzing the public sentiment on COVID-19 vaccination in social media: Bangladesh context. ARRAY 2022; 15:100204. [PMID: 35722449 PMCID: PMC9188682 DOI: 10.1016/j.array.2022.100204] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 05/31/2022] [Accepted: 06/03/2022] [Indexed: 01/25/2023] Open
Abstract
Since December 2019, the world has been fighting against the COVID-19 pandemic. This epidemic has revealed a bitter truth that though humans have advanced to unprecedented heights in the last few decades in terms of technology, they are lagging far behind in the fields of medical science and health care. Several institutes and research organizations have stepped up to introduce different vaccines to combat the pandemic. Bangladesh government has also taken steps to provide widespread vaccinations from January 2021. The Bangladeshi netizens are frequently sharing their thoughts, emotions, and experiences about the COVID-19 vaccines and the vaccination process on different social media sites like Facebook, Twitter, etc. This study has analyzed the views and opinions that they have expressed on different social media platforms about the vaccines and the ongoing vaccination program. For performing this study, the reactions of the Bangladeshi netizens on social media have been collected. The Latent Dirichlet Allocation (LDA) model has been used to extract the most common topics expressed by the netizens regarding the vaccines and vaccination process in Bangladesh. Finally, this study has applied different deep learning as well as traditional machine learning algorithms to identify the sentiments and polarity of the opinions of the netizens. The performance of these models has been assessed using a variety of metrics such as accuracy, precision, sensitivity, specificity, and F1-score to identify the best one. Sentiment analysis lessons from these opinions can help the government to prepare itself for the future pandemic.
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Affiliation(s)
- Md. Sabab Zulfiker
- Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh,Corresponding author
| | - Nasrin Kabir
- Department of Computer Science and Engineering, Jahangirnagar University, Dhaka, Bangladesh
| | - Al Amin Biswas
- Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh
| | - Sunjare Zulfiker
- Department of Electrical and Computer Engineering, North South University, Dhaka, Bangladesh
| | - Mohammad Shorif Uddin
- Department of Computer Science and Engineering, Jahangirnagar University, Dhaka, Bangladesh
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Huang X, Wang S, Zhang M, Hu T, Hohl A, She B, Gong X, Li J, Liu X, Gruebner O, Liu R, Li X, Liu Z, Ye X, Li Z. Social media mining under the COVID-19 context: Progress, challenges, and opportunities. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION : ITC JOURNAL 2022; 113:102967. [PMID: 36035895 PMCID: PMC9391053 DOI: 10.1016/j.jag.2022.102967] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Revised: 06/17/2022] [Accepted: 08/05/2022] [Indexed: 05/21/2023]
Abstract
Social media platforms allow users worldwide to create and share information, forging vast sensing networks that allow information on certain topics to be collected, stored, mined, and analyzed in a rapid manner. During the COVID-19 pandemic, extensive social media mining efforts have been undertaken to tackle COVID-19 challenges from various perspectives. This review summarizes the progress of social media data mining studies in the COVID-19 contexts and categorizes them into six major domains, including early warning and detection, human mobility monitoring, communication and information conveying, public attitudes and emotions, infodemic and misinformation, and hatred and violence. We further document essential features of publicly available COVID-19 related social media data archives that will benefit research communities in conducting replicable and reproducible studies. In addition, we discuss seven challenges in social media analytics associated with their potential impacts on derived COVID-19 findings, followed by our visions for the possible paths forward in regard to social media-based COVID-19 investigations. This review serves as a valuable reference that recaps social media mining efforts in COVID-19 related studies and provides future directions along which the information harnessed from social media can be used to address public health emergencies.
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Affiliation(s)
- Xiao Huang
- Department of Geosciences, University of Arkansas, Fayetteville, AR 72701, USA
| | - Siqin Wang
- School of Earth Environmental Sciences, University of Queensland, Brisbane, Queensland 4076, Australia
| | - Mengxi Zhang
- Department of Nutrition and Health Science, Ball State University, Muncie, IN 47304, USA
| | - Tao Hu
- Department of Geography, Oklahoma State University, Stillwater, OK 74078, USA
| | - Alexander Hohl
- Department of Geography, The University of Utah, Salt Lake City, UT 84112, USA
| | - Bing She
- Institute for social research, University of Michigan, Ann Arbor, MI 48109, USA
| | - Xi Gong
- Department of Geography & Environmental Studies, University of New Mexico, Albuquerque, NM 87131, USA
| | - Jianxin Li
- School of Information Technology, Deakin University, Geelong, Victoria 3220, Australia
| | - Xiao Liu
- School of Information Technology, Deakin University, Geelong, Victoria 3220, Australia
| | - Oliver Gruebner
- Department of Geography, University of Zurich, Zürich CH-8006, Switzerland
| | - Regina Liu
- Department of Biology, Mercer University, Macon, GA 31207, USA
| | - Xiao Li
- Texas A&M Transportation Institute, Bryan, TX 77807, USA
| | - Zhewei Liu
- Department of Land Surveying and Geo-informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China
| | - Xinyue Ye
- Department of Landscape Architecture and Urban Planning, Texas A&M University, College Station, TX 77840, USA
| | - Zhenlong Li
- Geoinformation and Big Data Research Lab, Department of Geography, University of South Carolina, Columbia, SC 29208, USA
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Ljajić A, Prodanović N, Medvecki D, Bašaragin B, Mitrović J. Uncovering the Reasons behind COVID-19 Vaccine Hesitancy in Serbia: Sentiment-Based Topic Modeling (Preprint). J Med Internet Res 2022; 24:e42261. [DOI: 10.2196/42261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 09/29/2022] [Accepted: 09/29/2022] [Indexed: 11/13/2022] Open
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Alhuzali H, Zhang T, Ananiadou S. A comparative geolocation and text mining analysis of emotions and topics during the COVID-19 Pandemic in the UK. J Med Internet Res 2022; 24:e40323. [PMID: 36150046 PMCID: PMC9536769 DOI: 10.2196/40323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 08/06/2022] [Accepted: 08/10/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND In recent years, the COVID-19 pandemic has brought great changes to public health, society and the economy. Social media provides a platform for people to discuss health concerns, living conditions and policies during the epidemic, which allows policy makers to use its contents to analyse the public emotions and attitudes for decision making. OBJECTIVE In this study, we aim to use deep learning-based methods to understand public emotions on topics related to the COVID-19 pandemic in the UK through a comparative geolocation and text mining analysis on Twitter. METHODS Over 500,000 tweets related to COVID-19 from 48 different cities in the UK were extracted, and the data cover the period of the last 2 years (from February 2020 to November 2021). We leveraged three advanced deep learning-based models: SenticNet 6 for sentiment analysis, SpanEmo for emotion recognition, and Combined Topic Modelling (CTM) for topic modelling to geospatially analyse the sentiment, emotion and topics of tweets in the UK. RESULTS According to the analysis, we observed a significant change in the number of tweets as the epidemiological situation and vaccination these two years. There was a sharp increase in the number of tweets from January 2020 to February 2020 due to the outbreak of COVID-19 in the UK. Then, the number of tweets gradually declined from February 2020. Moreover, with the identification of the COVID-19 Omicron variant in the UK in November 2021, the number of tweets grew. Our findings reveal people's attitudes and emotions towards topics related to COVID-19. For sentiment, about 60% of tweets are positive, 20% neutral and 20% are negative. For emotion, people tend to express highly positive emotions in the beginning of 2020, while expressing highly negative emotions as the time changes towards the end of 2021. The topics are also changing during the pandemic. CONCLUSIONS Through large scale text mining of Twitter, our study found that there were meaningful differences in public emotions and topics regarding the COVID-19 pandemic among different UK cities. Furthermore, efficient location-based and time-based comparative analysis can be used to track people's thoughts, feelings and understand their behaviours. Based on our analysis, positive attitudes were common during the pandemic; optimism and anticipation were the dominant emotions. With the outbreak epidemiological change, the government developed control measures, vaccination policies and the topics also shifted over time. Overall, the proportion and expressions of emojis, sentiments, emotions and topics varied geographically and temporally. Therefore, our approach of exploring public emotions and topics on the pandemic from Twitter can potentially lead to informing how public policies are received in a particular geographical area.
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Affiliation(s)
- Hassan Alhuzali
- College of Computers and Information Systems, Umm Al-Qura University, SA., Makkah, SA
| | - Tianlin Zhang
- Department of Computer Science, The University of Manchester, National Centre for Text Mining, Manchester, UK, National Centre for Text Mining Manchester Institute of Biotechnology 131 Princess Street Manchester M1 7DN UK, Manchester, GB
| | - Sophia Ananiadou
- Department of Computer Science, The University of Manchester, National Centre for Text Mining, Manchester, UK, National Centre for Text Mining Manchester Institute of Biotechnology 131 Princess Street Manchester M1 7DN UK, Manchester, GB
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36
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Using data mining technology to analyse the spatiotemporal public opinion of COVID-19 vaccine on social media. ELECTRONIC LIBRARY 2022. [DOI: 10.1108/el-03-2022-0062] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Purpose
The deployment of vaccines is the primary task in curbing the COVID-19 pandemic. The purpose of this paper is to understand the public’s opinions on vaccines and then design effective interventions to promote vaccination coverage.
Design/methodology/approach
This paper proposes a research framework based on the spatiotemporal perspective to analyse the public opinion evolution towards COVID-19 vaccine in China. The framework first obtains data through crawler tools. Then, with the help of data mining technologies, such as emotion computing and topic extraction, the evolution characteristics of discussion volume, emotions and topics are explored from spatiotemporal perspectives.
Findings
In the temporal perspective, the public emotion declines in the later stage, but overall emotion performance is positive and stabilizing. This decline in emotion is mainly associated with ambiguous information about the COVID-19 vaccine. The research progress of vaccines and the schedule of vaccination have driven the evolution of public discussion topics. In the spatial perspective, the public emotion tends to be positive in 31 regions, whereas local emotion increases and decreases in different stages. The dissemination of distinctive information and the local epidemic prevention and control status may be potential drivers of topic evolution in local regions.
Originality/value
The analysis results of media information can assist decision-makers to accurately grasp the subjective thoughts and emotional expressions of the public in terms of spatiotemporal perspective and provide decision support for macro-control response strategies and risk communication.
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Cevik E, Kirci Altinkeski B, Cevik EI, Dibooglu S. Investor sentiments and stock markets during the COVID-19 pandemic. FINANCIAL INNOVATION 2022; 8:69. [PMID: 35814528 PMCID: PMC9253256 DOI: 10.1186/s40854-022-00375-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Accepted: 06/24/2022] [Indexed: 05/25/2023]
Abstract
This study examines the relationship between positive and negative investor sentiments and stock market returns and volatility in Group of 20 countries using various methods, including panel regression with fixed effects, panel quantile regressions, a panel vector autoregression (PVAR) model, and country-specific regressions. We proxy for negative and positive investor sentiments using the Google Search Volume Index for terms related to the coronavirus disease (COVID-19) and COVID-19 vaccine, respectively. Using weekly data from March 2020 to May 2021, we document significant relationships between positive and negative investor sentiments and stock market returns and volatility. Specifically, an increase in positive investor sentiment leads to an increase in stock returns while negative investor sentiment decreases stock returns at lower quantiles. The effect of investor sentiment on volatility is consistent across the distribution: negative sentiment increases volatility, whereas positive sentiment reduces volatility. These results are robust as they are corroborated by Granger causality tests and a PVAR model. The findings may have portfolio implications as they indicate that proxies for positive and negative investor sentiments seem to be good predictors of stock returns and volatility during the pandemic.
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Affiliation(s)
- Emre Cevik
- Kırklareli University, Kırklareli, Turkey
| | | | | | - Sel Dibooglu
- Tekirdag Namik Kemal Universitesi, Tekirdaǧ, Turkey
- University of Sharjah, Sharjah, United Arab Emirates
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Marcec R, Likic R. Using Twitter for sentiment analysis towards AstraZeneca/Oxford, Pfizer/BioNTech and Moderna COVID-19 vaccines. Postgrad Med J 2022; 98:544-550. [PMID: 34373343 PMCID: PMC8354810 DOI: 10.1136/postgradmedj-2021-140685] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Accepted: 07/30/2021] [Indexed: 02/04/2023]
Abstract
INTRODUCTION A worldwide vaccination campaign is underway to bring an end to the SARS-CoV-2 pandemic; however, its success relies heavily on the actual willingness of individuals to get vaccinated. Social media platforms such as Twitter may prove to be a valuable source of information on the attitudes and sentiment towards SARS-CoV-2 vaccination that can be tracked almost instantaneously. MATERIALS AND METHODS The Twitter academic Application Programming Interface was used to retrieve all English-language tweets mentioning AstraZeneca/Oxford, Pfizer/BioNTech and Moderna vaccines in 4 months from 1 December 2020 to 31 March 2021. Sentiment analysis was performed using the AFINN lexicon to calculate the daily average sentiment of tweets which was evaluated longitudinally and comparatively for each vaccine throughout the 4 months. RESULTS A total of 701 891 tweets have been retrieved and included in the daily sentiment analysis. The sentiment regarding Pfizer and Moderna vaccines appeared positive and stable throughout the 4 months, with no significant differences in sentiment between the months. In contrast, the sentiment regarding the AstraZeneca/Oxford vaccine seems to be decreasing over time, with a significant decrease when comparing December with March (p<0.0000000001, mean difference=-0.746, 95% CI=-0.915 to -0.577). CONCLUSION Lexicon-based Twitter sentiment analysis is a valuable and easily implemented tool to track the sentiment regarding SARS-CoV-2 vaccines. It is worrisome that the sentiment regarding the AstraZeneca/Oxford vaccine appears to be turning negative over time, as this may boost hesitancy rates towards this specific SARS-CoV-2 vaccine.
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Affiliation(s)
- Robert Marcec
- University of Zagreb School of Medicine, Zagreb, Croatia
| | - Robert Likic
- Department of Internal Medicine, Division of Clinical Pharmacology and Therapeutics, Clinical Hospital Centre Zagreb and University of Zagreb Medical School, Zagreb, Croatia
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Xu WW, Tshimula JM, Dubé È, Graham JE, Greyson D, MacDonald NE, Meyer SB. Unmasking the Twitter Discourses on Masks During the COVID-19 Pandemic: User Cluster-Based BERT Topic Modeling Approach. JMIR INFODEMIOLOGY 2022; 2:e41198. [PMID: 36536763 PMCID: PMC9749113 DOI: 10.2196/41198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 09/26/2022] [Accepted: 11/02/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND The COVID-19 pandemic has spotlighted the politicization of public health issues. A public health monitoring tool must be equipped to reveal a public health measure's political context and guide better interventions. In its current form, infoveillance tends to neglect identity and interest-based users, hence being limited in exposing how public health discourse varies by different political groups. Adopting an algorithmic tool to classify users and their short social media texts might remedy that limitation. OBJECTIVE We aimed to implement a new computational framework to investigate discourses and temporal changes in topics unique to different user clusters. The framework was developed to contextualize how web-based public health discourse varies by identity and interest-based user clusters. We used masks and mask wearing during the early stage of the COVID-19 pandemic in the English-speaking world as a case study to illustrate the application of the framework. METHODS We first clustered Twitter users based on their identities and interests as expressed through Twitter bio pages. Exploratory text network analysis reveals salient political, social, and professional identities of various user clusters. It then uses BERT Topic modeling to identify topics by the user clusters. It reveals how web-based discourse has shifted over time and varied by 4 user clusters: conservative, progressive, general public, and public health professionals. RESULTS This study demonstrated the importance of a priori user classification and longitudinal topical trends in understanding the political context of web-based public health discourse. The framework reveals that the political groups and the general public focused on the science of mask wearing and the partisan politics of mask policies. A populist discourse that pits citizens against elites and institutions was identified in some tweets. Politicians (such as Donald Trump) and geopolitical tensions with China were found to drive the discourse. It also shows limited participation of public health professionals compared with other users. CONCLUSIONS We conclude by discussing the importance of a priori user classification in analyzing web-based discourse and illustrating the fit of BERT Topic modeling in identifying contextualized topics in short social media texts.
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Affiliation(s)
- Weiai Wayne Xu
- Department of Communication University of Massachusetts Amherst Amherst, MA United States
| | - Jean Marie Tshimula
- Department of Computer Science Université de Sherbrooke Sherbrooke, QC Canada
| | - Ève Dubé
- Axe maladies infectieuses et immunitaires, Centre de Recherche du CHU de Québec Laval University Quebec City, QC Canada
- Direction des risques biologiques et de la santé au travail Institut National de Santé Publique du Québec Quebec, QC Canada
| | - Janice E Graham
- Department of Pediatrics Dalhousie University Halifax, NS Canada
| | - Devon Greyson
- School of Population and Public Health University of British Columbia Vancouver, BC Canada
| | - Noni E MacDonald
- Department of Pediatrics Dalhousie University Halifax, NS Canada
| | - Samantha B Meyer
- School of Public Health Sciences University of Waterloo Waterloo, ON Canada
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Hagen L, Fox A, O'Leary H, Dyson D, Walker K, Lengacher CA, Hernandez R. The Role of Influential Actors in Fostering the Polarized COVID-19 Vaccine Discourse on Twitter: Mixed Methods of Machine Learning and Inductive Coding. JMIR INFODEMIOLOGY 2022; 2:e34231. [PMID: 35814809 PMCID: PMC9254747 DOI: 10.2196/34231] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 01/20/2022] [Accepted: 03/31/2022] [Indexed: 11/13/2022]
Abstract
Background Since COVID-19 vaccines became broadly available to the adult population, sharp divergences in uptake have emerged along partisan lines. Researchers have indicated a polarized social media presence contributing to the spread of mis- or disinformation as being responsible for these growing partisan gaps in uptake. Objective The major aim of this study was to investigate the role of influential actors in the context of the community structures and discourse related to COVID-19 vaccine conversations on Twitter that emerged prior to the vaccine rollout to the general population and discuss implications for vaccine promotion and policy. Methods We collected tweets on COVID-19 between July 1, 2020, and July 31, 2020, a time when attitudes toward the vaccines were forming but before the vaccines were widely available to the public. Using network analysis, we identified different naturally emerging Twitter communities based on their internal information sharing. A PageRank algorithm was used to quantitively measure the level of “influentialness” of Twitter accounts and identifying the “influencers,” followed by coding them into different actor categories. Inductive coding was conducted to describe discourses shared in each of the 7 communities. Results Twitter vaccine conversations were highly polarized, with different actors occupying separate “clusters.” The antivaccine cluster was the most densely connected group. Among the 100 most influential actors, medical experts were outnumbered both by partisan actors and by activist vaccine skeptics or conspiracy theorists. Scientists and medical actors were largely absent from the conservative network, and antivaccine sentiment was especially salient among actors on the political right. Conversations related to COVID-19 vaccines were highly polarized along partisan lines, with “trust” in vaccines being manipulated to the political advantage of partisan actors. Conclusions These findings are informative for designing improved vaccine information communication strategies to be delivered on social media especially by incorporating influential actors. Although polarization and echo chamber effect are not new in political conversations in social media, it was concerning to observe these in health conversations on COVID-19 vaccines during the vaccine development process.
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Affiliation(s)
- Loni Hagen
- School of Information University of South Florida Tampa, FL United States
| | - Ashley Fox
- Rockefeller College of Public Affairs and Policy University at Albany State University of New York Albany, NY United States
| | - Heather O'Leary
- Department of Anthropology University of South Florida St. Petersburg, FL United States
| | - DeAndre Dyson
- School of Information University of South Florida Tampa, FL United States
| | - Kimberly Walker
- Zimmerman School of Advertising and Mass Communications University of South Florida Tampa, FL United States
| | | | - Raquel Hernandez
- Institute for Clinical and Translational Research Johns Hopkins All Children's Hospital St. Petersburg, FL United States
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Nyawa S, Tchuente D, Fosso-Wamba S. COVID-19 vaccine hesitancy: a social media analysis using deep learning. ANNALS OF OPERATIONS RESEARCH 2022:1-39. [PMID: 35729983 PMCID: PMC9202977 DOI: 10.1007/s10479-022-04792-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 05/17/2022] [Indexed: 06/15/2023]
Abstract
Hesitant attitudes have been a significant issue since the development of the first vaccines-the WHO sees them as one of the most critical global health threats. The increasing use of social media to spread questionable information about vaccination strongly impacts the population's decision to get vaccinated. Developing text classification methods that can identify hesitant messages on social media could be useful for health campaigns in their efforts to address negative influences from social media platforms and provide reliable information to support their strategies against hesitant-vaccination sentiments. This study aims to evaluate the performance of different machine learning models and deep learning methods in identifying vaccine-hesitant tweets that are being published during the COVID-19 pandemic. Our concluding remarks are that Long Short-Term Memory and Recurrent Neural Network models have outperformed traditional machine learning models on detecting vaccine-hesitant messages in social media, with an accuracy rate of 86% against 83%.
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Affiliation(s)
- Serge Nyawa
- Department of Information, Operations and Management Sciences, TBS Business School, 1 Place Alphonse Jourdain, 31068 Toulouse, France
| | - Dieudonné Tchuente
- Department of Information, Operations and Management Sciences, TBS Business School, 1 Place Alphonse Jourdain, 31068 Toulouse, France
| | - Samuel Fosso-Wamba
- Department of Information, Operations and Management Sciences, TBS Business School, 1 Place Alphonse Jourdain, 31068 Toulouse, France
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Niu Q, Liu J, Kato M, Nagai-Tanima M, Aoyama T. The Effect of Fear of Infection and Sufficient Vaccine Reservation Information on Rapid COVID-19 Vaccination in Japan: Evidence From a Retrospective Twitter Analysis. J Med Internet Res 2022; 24:e37466. [PMID: 35649182 PMCID: PMC9186499 DOI: 10.2196/37466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 05/09/2022] [Accepted: 05/30/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND The global public health and socioeconomic impacts of the COVID-19 pandemic have been substantial, rendering herd immunity by COVID-19 vaccination an important factor for protecting people and retrieving the economy. Among all the countries, Japan became one of the countries with the highest COVID-19 vaccination rates in several months, although vaccine confidence in Japan is the lowest worldwide. OBJECTIVE We attempted to find the reasons for rapid COVID-19 vaccination in Japan given its lowest vaccine confidence levels worldwide, through Twitter analysis. METHODS We downloaded COVID-19-related Japanese tweets from a large-scale public COVID-19 Twitter chatter data set within the timeline of February 1 and September 30, 2021. The daily number of vaccination cases was collected from the official website of the Prime Minister's Office of Japan. After preprocessing, we applied unigram and bigram token analysis and then calculated the cross-correlation and Pearson correlation coefficient (r) between the term frequency and daily vaccination cases. We then identified vaccine sentiments and emotions of tweets and used the topic modeling to look deeper into the dominant emotions. RESULTS We selected 190,697 vaccine-related tweets after filtering. Through n-gram token analysis, we discovered the top unigrams and bigrams over the whole period. In all the combinations of the top 6 unigrams, tweets with both keywords "reserve" and "venue" showed the largest correlation with daily vaccination cases (r=0.912; P<.001). On sentiment analysis, negative sentiment overwhelmed positive sentiment, and fear was the dominant emotion across the period. For the latent Dirichlet allocation model on tweets with fear emotion, the two topics were identified as "infect" and "vaccine confidence." The expectation of the number of tweets generated from topic "infect" was larger than that generated from topic "vaccine confidence." CONCLUSIONS Our work indicates that awareness of the danger of COVID-19 might increase the willingness to get vaccinated. With a sufficient vaccine supply, effective delivery of vaccine reservation information may be an important factor for people to get vaccinated. We did not find evidence for increased vaccine confidence in Japan during the period of our study. We recommend policy makers to share accurate and prompt information about the infectious diseases and vaccination and to make efforts on smoother delivery of vaccine reservation information.
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Affiliation(s)
- Qian Niu
- Department of Human Health Sciences, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Junyu Liu
- Department of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University, Kyoto, Japan
| | - Masaya Kato
- Department of Human Health Sciences, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Momoko Nagai-Tanima
- Department of Human Health Sciences, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Tomoki Aoyama
- Department of Human Health Sciences, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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Yin B, Yuan CH. Detecting latent topics and trends in blended learning using LDA topic modeling. EDUCATION AND INFORMATION TECHNOLOGIES 2022; 27:12689-12712. [PMID: 35692870 PMCID: PMC9169034 DOI: 10.1007/s10639-022-11118-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Accepted: 05/17/2022] [Indexed: 06/15/2023]
Abstract
With the rapid application of blended learning around the world, a large amount of literature has been accumulated. The analysis of the main research topics and development trends based on a large amount of literature is of great significance. To address this issue, this paper collected abstracts from 3772 eligible papers published between 2003 and 2021 from the Web of Science core collection. Through LDA topic modeling, abstract text content was analyzed, then 7 well-defined research topics were obtained. According to the topic development trends analysis results, the emphasis of topic research shifted from the initial courses about health, medicine, nursing, chemistry and mathematics to learning key elements such as learning outcomes, teacher factors, and presences. Among 7 research topics, the popularity of presences increased significantly, while formative assessment was a rare topic requiring careful intervention. The other five topics had no significant increase or decrease trends, but still accounted for a considerable proportion. Through word cloud analysis technology, the keyword characteristics of each stage and research focus changes of research were obtained. This study provides useful insights and implications for blended learning related research.
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Affiliation(s)
- Bin Yin
- School of Economics and Commerce, University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan, China
| | - Chih-Hung Yuan
- School of Economics and Commerce, University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan, China
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Stracqualursi L, Agati P. Tweet topics and sentiments relating to distance learning among Italian Twitter users. Sci Rep 2022; 12:9163. [PMID: 35654806 PMCID: PMC9163328 DOI: 10.1038/s41598-022-12915-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 05/18/2022] [Indexed: 11/09/2022] Open
Abstract
The outbreak of COVID-19 forced a dramatic shift in education, from in-person learning to an increased use of distance learning over the past 2 years. Opinions and sentiments regarding this switch from traditional to remote classes can be tracked in real time in microblog messages promptly shared by Twitter users, who constitute a large and ever-increasing number of individuals today. Given this framework, the present study aims to investigate sentiments and topics related to distance learning in Italy from March 2020 to November 2021. A two-step sentiment analysis was performed using the VADER model and the syuzhet package to understand the overall sentiments and emotions. A dynamic latent Dirichlet allocation model (DLDA) was built to identify commonly discussed topics in tweets and their evolution over time. The results show a modest majority of negative opinions, which shifted over time until the trend reversed. Among the eight emotions of the syuzhet package, ‘trust’ was the most positive emotion observed in the tweets, while ‘fear’ and ‘sadness’ were the top negative emotions. Our analysis also identified three topics: (1) requests for support measures for distance learning, (2) concerns about distance learning and its application, and (3) anxiety about the government decrees introducing the red zones and the corresponding restrictions. People’s attitudes changed over time. The concerns about distance learning and its future applications (topic 2) gained importance in the latter stages of 2021, while the first and third topics, which were ranked highly at first, started a steep descent in the last part of the period. The results indicate that even if current distance learning ends, the Italian people are concerned that any new emergency will bring distance learning back into use again.
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Affiliation(s)
| | - Patrizia Agati
- Department of Statistics, University of Bologna, 40126, Bologna, Italy
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Rahmanti AR, Chien CH, Nursetyo AA, Husnayain A, Wiratama BS, Fuad A, Yang HC, Li YCJ. Social media sentiment analysis to monitor the performance of vaccination coverage during the early phase of the national COVID-19 vaccine rollout. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 221:106838. [PMID: 35567863 PMCID: PMC9045866 DOI: 10.1016/j.cmpb.2022.106838] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 01/21/2022] [Accepted: 04/24/2022] [Indexed: 05/27/2023]
Abstract
BACKGROUND AND OBJECTIVE Social media sentiment analysis based on Twitter data can facilitate real-time monitoring of COVID-19 vaccine-related concerns. Thus, the governments can adopt proactive measures to address misinformation and inappropriate behaviors surrounding the COVID-19 vaccine, threatening the success of the national vaccination campaign. This study aims to identify the correlation between COVID-19 vaccine sentiments expressed on Twitter and COVID-19 vaccination coverage, case increase, and case fatality rate in Indonesia. METHODS We retrieved COVID-19 vaccine-related tweets collected from Indonesian Twitter users between October 15, 2020, to April 12, 2021, using Drone Emprit Academic (DEA) platform. We collected the daily trend of COVID-19 vaccine coverage and the rate of case increase and case fatality from the Ministry of Health (MoH) official website and the KawalCOVID19 database, respectively. We identified the public sentiments, emotions, word usage, and trend of all filtered tweets 90 days before and after the national vaccination rollout in Indonesia. RESULTS Using a total of 555,892 COVID-19 vaccine-related tweets, we observed the negative sentiments outnumbered positive sentiments for 59 days (65.50%), with the predominant emotion of anticipation among 90 days of the beginning of the study period. However, after the vaccination rollout, the positive sentiments outnumbered negative sentiments for 56 days (62.20%) with the growth of trust emotion, which is consistent with the positive appeals of the recent news about COVID-19 vaccine safety and the government's proactive risk communication. In addition, there was a statistically significant trend of vaccination sentiment scores, which strongly correlated with the increase of vaccination coverage (r = 0.71, P<.0001 both first and second doses) and the decreasing of case increase rate (r = -0.70, P<.0001) and case fatality rate (r = -0.74, P<.0001). CONCLUSIONS Our results highlight the utility of social media sentiment analysis as government communication strategies to build public trust, affecting individual willingness to get vaccinated. This finding will be useful for countries to identify and develop strategies for speed up the vaccination rate by monitoring the dynamic netizens' reactions and expression in social media, especially Twitter, using sentiment analysis.
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Affiliation(s)
- Annisa Ristya Rahmanti
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan; International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan; Department of Health Policy and Management, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Chia-Hui Chien
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan; International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan; Office of Public Affairs, Taipei Medical University, Taipei, Taiwan
| | - Aldilas Achmad Nursetyo
- Center for Health Policy Management, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Atina Husnayain
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan; Department of Biostatistics, Epidemiology, and Population Health, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta City, Indonesia
| | - Bayu Satria Wiratama
- Department of Biostatistics, Epidemiology, and Population Health, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta City, Indonesia; Graduate Institute of Injury Prevention and Control, College of Public Health, Taipei Medical University, Taipei, Taiwan
| | - Anis Fuad
- Department of Biostatistics, Epidemiology, and Population Health, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta City, Indonesia
| | - Hsuan-Chia Yang
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan; International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan; Research Center of Big Data and Meta-analysis, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
| | - Yu-Chuan Jack Li
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan; International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan; Research Center of Big Data and Meta-analysis, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan; Department of Dermatology, Wan Fang Hospital, Taipei, 5126, Taiwan; TMU Research Center of Cancer Translational Medicine, Taipei Medical University, Taiwan.
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Cascini F, Pantovic A, Al-Ajlouni YA, Failla G, Puleo V, Melnyk A, Lontano A, Ricciardi W. Social media and attitudes towards a COVID-19 vaccination: A systematic review of the literature. EClinicalMedicine 2022; 48:101454. [PMID: 35611343 PMCID: PMC9120591 DOI: 10.1016/j.eclinm.2022.101454] [Citation(s) in RCA: 88] [Impact Index Per Article: 44.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 04/24/2022] [Accepted: 04/27/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Vaccine hesitancy continues to limit global efforts in combatting the COVID-19 pandemic. Emerging research demonstrates the role of social media in disseminating information and potentially influencing people's attitudes towards public health campaigns. This systematic review sought to synthesize the current evidence regarding the potential role of social media in shaping COVID-19 vaccination attitudes, and to explore its potential for shaping public health interventions to address the issue of vaccine hesitancy. METHODS We performed a systematic review of the studies published from inception to 13 of March2022 by searching PubMed, Web of Science, Embase, PsychNET, Scopus, CINAHL, and MEDLINE. Studies that reported outcomes related to coronavirus disease 2019 (COVID-19) vaccine (attitudes, opinion, etc.) gathered from the social media platforms, and those analyzing the relationship between social media use and COVID-19 hesitancy/acceptance were included. Studies that reported no outcome of interest or analyzed data from sources other than social media (websites, newspapers, etc.) will be excluded. The Newcastle Ottawa Scale (NOS) was used to assess the quality of all cross-sectional studies included in this review. This study is registered with PROSPERO (CRD42021283219). FINDINGS Of the 2539 records identified, a total of 156 articles fully met the inclusion criteria. Overall, the quality of the cross-sectional studies was moderate - 2 studies received 10 stars, 5 studies received 9 stars, 9 studies were evaluated with 8, 12 studies with 7,16 studies with 6, 11 studies with 5, and 6 studies with 4 stars. The included studies were categorized into four categories. Cross-sectional studies reporting the association between reliance on social media and vaccine intentions mainly observed a negative relationship. Studies that performed thematic analyses of extracted social media data, mainly observed a domination of vaccine hesitant topics. Studies that explored the degree of polarization of specific social media contents related to COVID-19 vaccines observed a similar degree of content for both positive and negative tone posted on different social media platforms. Finally, studies that explored the fluctuations of vaccination attitudes/opinions gathered from social media identified specific events as significant cofactors that affect and shape vaccination intentions of individuals. INTERPRETATION This thorough examination of the various roles social media can play in disseminating information to the public, as well as how individuals behave on social media in the context of public health events, articulates the potential of social media as a platform of public health intervention to address vaccine hesitancy. FUNDING None.
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Affiliation(s)
- Fidelia Cascini
- Department of Life Sciences and Public Health, Section of Hygiene and Public Health, Università Cattolica del Sacro Cuore, L.go Francesco Vito 1, Rome 00168, Italy
- Corresponding author.
| | - Ana Pantovic
- Faculty of Biology, University of Belgrade, Belgrade, Serbia
| | | | - Giovanna Failla
- Department of Life Sciences and Public Health, Section of Hygiene and Public Health, Università Cattolica del Sacro Cuore, L.go Francesco Vito 1, Rome 00168, Italy
| | - Valeria Puleo
- Department of Life Sciences and Public Health, Section of Hygiene and Public Health, Università Cattolica del Sacro Cuore, L.go Francesco Vito 1, Rome 00168, Italy
| | - Andriy Melnyk
- Department of Life Sciences and Public Health, Section of Hygiene and Public Health, Università Cattolica del Sacro Cuore, L.go Francesco Vito 1, Rome 00168, Italy
| | - Alberto Lontano
- Department of Life Sciences and Public Health, Section of Hygiene and Public Health, Università Cattolica del Sacro Cuore, L.go Francesco Vito 1, Rome 00168, Italy
| | - Walter Ricciardi
- Department of Life Sciences and Public Health, Section of Hygiene and Public Health, Università Cattolica del Sacro Cuore, L.go Francesco Vito 1, Rome 00168, Italy
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Text Mining and Determinants of Sentiments towards the COVID-19 Vaccine Booster of Twitter Users in Malaysia. Healthcare (Basel) 2022; 10:healthcare10060994. [PMID: 35742045 PMCID: PMC9222954 DOI: 10.3390/healthcare10060994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 05/20/2022] [Accepted: 05/24/2022] [Indexed: 11/18/2022] Open
Abstract
Vaccination is the primary preventive measure against the COVID-19 infection, and an additional vaccine dosage is crucial to increase the immunity level of the community. However, public bias, as reflected on social media, may have a significant impact on the vaccination program. We aim to investigate the attitudes to the COVID-19 vaccination booster in Malaysia by using sentiment analysis. We retrieved 788 tweets containing COVID-19 vaccine booster keywords and identified the common topics discussed in tweets that related to the booster by using latent Dirichlet allocation (LDA) and performed sentiment analysis to understand the determinants for the sentiments to receiving the vaccination booster in Malaysia. We identified three important LDA topics: (1) type of vaccination booster; (2) effects of vaccination booster; (3) vaccination program operation. The type of vaccination further transformed into attributes of “az”, “pfizer”, “sinovac”, and “mix” for determinants’ assessments. Effect and type of vaccine booster associated stronger than program operation topic for the sentiments, and “pfizer” and “mix” were the strongest determinants of the tweet’s sentiments after the Boruta feature selection and validated from the performance of regression analysis. This study provided a comprehensive workflow to retrieve and identify important healthcare topic from social media.
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Skafle I, Nordahl-Hansen A, Quintana DS, Wynn R, Gabarron E. Misinformation about Covid-19 Vaccines on Social Media: Rapid Review. J Med Internet Res 2022; 24:e37367. [PMID: 35816685 PMCID: PMC9359307 DOI: 10.2196/37367] [Citation(s) in RCA: 69] [Impact Index Per Article: 34.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 04/25/2022] [Accepted: 05/24/2022] [Indexed: 12/24/2022] Open
Abstract
Background The development of COVID-19 vaccines has been crucial in fighting the pandemic. However, misinformation about the COVID-19 pandemic and vaccines is spread on social media platforms at a rate that has made the World Health Organization coin the phrase infodemic. False claims about adverse vaccine side effects, such as vaccines being the cause of autism, were already considered a threat to global health before the outbreak of COVID-19. Objective We aimed to synthesize the existing research on misinformation about COVID-19 vaccines spread on social media platforms and its effects. The secondary aim was to gain insight and gather knowledge about whether misinformation about autism and COVID-19 vaccines is being spread on social media platforms. Methods We performed a literature search on September 9, 2021, and searched PubMed, PsycINFO, ERIC, EMBASE, Cochrane Library, and the Cochrane COVID-19 Study Register. We included publications in peer-reviewed journals that fulfilled the following criteria: original empirical studies, studies that assessed social media and misinformation, and studies about COVID-19 vaccines. Thematic analysis was used to identify the patterns (themes) of misinformation. Narrative qualitative synthesis was undertaken with the guidance of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 Statement and the Synthesis Without Meta-analysis reporting guideline. The risk of bias was assessed using the Joanna Briggs Institute Critical Appraisal tool. Ratings of the certainty of evidence were based on recommendations from the Grading of Recommendations Assessment, Development and Evaluation Working Group. Results The search yielded 757 records, with 45 articles selected for this review. We identified 3 main themes of misinformation: medical misinformation, vaccine development, and conspiracies. Twitter was the most studied social media platform, followed by Facebook, YouTube, and Instagram. A vast majority of studies were from industrialized Western countries. We identified 19 studies in which the effect of social media misinformation on vaccine hesitancy was measured or discussed. These studies implied that the misinformation spread on social media had a negative effect on vaccine hesitancy and uptake. Only 1 study contained misinformation about autism as a side effect of COVID-19 vaccines. Conclusions To prevent these misconceptions from taking hold, health authorities should openly address and discuss these false claims with both cultural and religious awareness in mind. Our review showed that there is a need to examine the effect of social media misinformation on vaccine hesitancy with a more robust experimental design. Furthermore, this review also demonstrated that more studies are needed from the Global South and on social media platforms other than the major platforms such as Twitter and Facebook. Trial Registration PROSPERO International Prospective Register of Systematic Reviews CRD42021277524; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021277524 International Registered Report Identifier (IRRID) RR2-10.31219/osf.io/tyevj
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Affiliation(s)
- Ingjerd Skafle
- Faculty of Health, Welfare, and Organisation, Østfold University College, B R A Veien 4, Halden, NO
| | | | - Daniel S Quintana
- Department of Psychology, University of Oslo, Oslo, NO.,K.G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, NO.,NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, NO.,NevSom, Department of Rare Disorders & Disabilities, Oslo University Hospital, Oslo, NO
| | - Rolf Wynn
- Department of Clinical Medicine, The Artic University of Norway, Tromsø, NO.,Division of Mental Health and Substance Use, University Hospital of North Norway, Tromsø, NO
| | - Elia Gabarron
- Department of Education, ICT, and Learning, Østfold University College, Halden, NO.,Norwegian Centre for E-health Research, Tromsø, NO
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Feizollah A, Anuar NB, Mehdi R, Firdaus A, Sulaiman A. Understanding COVID-19 Halal Vaccination Discourse on Facebook and Twitter Using Aspect-Based Sentiment Analysis and Text Emotion Analysis. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:6269. [PMID: 35627806 PMCID: PMC9140743 DOI: 10.3390/ijerph19106269] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Revised: 05/08/2022] [Accepted: 05/11/2022] [Indexed: 02/04/2023]
Abstract
The COVID-19 pandemic introduced unprecedented challenges for people and governments. Vaccines are an available solution to this pandemic. Recipients of the vaccines are of different ages, gender, and religion. Muslims follow specific Islamic guidelines that prohibit them from taking a vaccine with certain ingredients. This study aims at analyzing Facebook and Twitter data to understand the discourse related to halal vaccines using aspect-based sentiment analysis and text emotion analysis. We searched for the term "halal vaccine" and limited the timeline to the period between 1 January 2020, and 30 April 2021, and collected 6037 tweets and 3918 Facebook posts. We performed data preprocessing on tweets and Facebook posts and built the Latent Dirichlet Allocation (LDA) model to identify topics. Calculating the sentiment analysis for each topic was the next step. Finally, this study further investigates emotions in the data using the National Research Council of Canada Emotion Lexicon. Our analysis identified four topics in each of the Twitter dataset and Facebook dataset. Two topics of "COVID-19 vaccine" and "halal vaccine" are shared between the two datasets. The other two topics in tweets are "halal certificate" and "must halal", while "sinovac vaccine" and "ulema council" are two other topics in the Facebook dataset. The sentiment analysis shows that the sentiment toward halal vaccine is mostly neutral in Twitter data, whereas it is positive in Facebook data. The emotion analysis indicates that trust is the most present emotion among the top three emotions in both datasets, followed by anticipation and fear.
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Affiliation(s)
- Ali Feizollah
- Universiti Malaya Halal Research Centre, Universiti Malaya, Kuala Lumpur 50603, Malaysia;
- Department of Computer System & Technology, Faculty of Computer Science & Information Technology, Universiti Malaya, Kuala Lumpur 50603, Malaysia;
| | - Nor Badrul Anuar
- Department of Computer System & Technology, Faculty of Computer Science & Information Technology, Universiti Malaya, Kuala Lumpur 50603, Malaysia;
| | - Riyadh Mehdi
- Department of Information Technology, College of Engineering and Information Technology, Ajman University, Ajman P.O. Box 346, United Arab Emirates;
| | - Ahmad Firdaus
- Faculty of Computer Systems and Software Engineering, Universiti Malaysia Pahang, Gambang, Kuantan 26300, Malaysia;
| | - Ainin Sulaiman
- Universiti Malaya Halal Research Centre, Universiti Malaya, Kuala Lumpur 50603, Malaysia;
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Bari A, Heymann M, Cohen RJ, Zhao R, Szabo L, Apas Vasandani S, Khubchandani A, DiLorenzo M, Coffee M. Exploring Coronavirus Disease 2019 Vaccine Hesitancy on Twitter Using Sentiment Analysis and Natural Language Processing Algorithms. Clin Infect Dis 2022; 74:e4-e9. [PMID: 35568473 DOI: 10.1093/cid/ciac141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Vaccination can help control the coronavirus disease 2019 (COVID-19) pandemic but is undermined by vaccine hesitancy. Social media disseminates information and misinformation regarding vaccination. Tracking and analyzing social media vaccine sentiment could better prepare health professionals for vaccination conversations and campaigns. METHODS A real-time big data analytics framework was developed using natural language processing sentiment analysis, a form of artificial intelligence. The framework ingests, processes, and analyzes tweets for sentiment and content themes, such as natural health or personal freedom, in real time. A later dataset evaluated the relationship between Twitter sentiment scores and vaccination rates in the United States. RESULTS The real-time analytics framework showed a widening gap in sentiment with more negative sentiment after vaccine rollout. After rollout, using a static dataset, an increase in positive sentiment was followed by an increase in vaccination. Lag cross-correlation analysis across US regions showed evidence that once all adults were eligible for vaccination, the sentiment score consistently correlated with vaccination rate with a lag of around 1 week. The Granger causality test further demonstrated that tweet sentiment scores may help predict vaccination rates. CONCLUSIONS Social media has influenced the COVID-19 response through valuable information and misinformation and distrust. This tool was used to collect and analyze tweets at scale in real time to study sentiment and key terms of interest. Separate tweet analysis showed that vaccination rates tracked regionally with Twitter vaccine sentiment and might forecast changes in vaccine uptake and/or guide targeted social media and vaccination strategies. Further work is needed to analyze the interplay between specific populations, vaccine sentiment, and vaccination rates.
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Affiliation(s)
- Anasse Bari
- Courant Institute of Mathematical Sciences, Department of Computer Science, New York University, New York, New York, USA
| | - Matthias Heymann
- Courant Institute of Mathematical Sciences, Department of Computer Science, New York University, New York, New York, USA
| | - Ryan J Cohen
- Courant Institute of Mathematical Sciences, Department of Computer Science, New York University, New York, New York, USA
| | - Robin Zhao
- Courant Institute of Mathematical Sciences, Department of Computer Science, New York University, New York, New York, USA
| | - Levente Szabo
- Courant Institute of Mathematical Sciences, Department of Computer Science, New York University, New York, New York, USA
| | - Shailesh Apas Vasandani
- Courant Institute of Mathematical Sciences, Department of Computer Science, New York University, New York, New York, USA
| | - Aashish Khubchandani
- Courant Institute of Mathematical Sciences, Department of Computer Science, New York University, New York, New York, USA
| | - Madeline DiLorenzo
- Grossman School of Medicine, Department of Medicine, Division of Infectious Diseases and Immunology, New York University, New York, New York, USA
| | - Megan Coffee
- Grossman School of Medicine, Department of Medicine, Division of Infectious Diseases and Immunology, New York University, New York, New York, USA
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