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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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52
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Ferawati K, Liew K, Aramaki E, Wakamiya S. Monitoring Mentions of COVID-19 Vaccine Side Effects from Japanese and Indonesian Twitter: Infodemiological Study (Preprint). JMIR INFODEMIOLOGY 2022; 2:e39504. [PMID: 36277140 PMCID: PMC9578292 DOI: 10.2196/39504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 07/29/2022] [Accepted: 09/19/2022] [Indexed: 11/13/2022]
Abstract
Background The year 2021 was marked by vaccinations against COVID-19, which spurred wider discussion among the general population, with some in favor and some against vaccination. Twitter, a popular social media platform, was instrumental in providing information about the COVID-19 vaccine and has been effective in observing public reactions. We focused on tweets from Japan and Indonesia, 2 countries with a large Twitter-using population, where concerns about side effects were consistently stated as a strong reason for vaccine hesitancy. Objective This study aimed to investigate how Twitter was used to report vaccine-related side effects and to compare the mentions of these side effects from 2 messenger RNA (mRNA) vaccine types developed by Pfizer and Moderna, in Japan and Indonesia. Methods We obtained tweet data from Twitter using Japanese and Indonesian keywords related to COVID-19 vaccines and their side effects from January 1, 2021, to December 31, 2021. We then removed users with a high frequency of tweets and merged the tweets from multiple users as a single sentence to focus on user-level analysis, resulting in a total of 214,165 users (Japan) and 12,289 users (Indonesia). Then, we filtered the data to select tweets mentioning Pfizer or Moderna only and removed tweets mentioning both. We compared the side effect counts to the public reports released by Pfizer and Moderna. Afterward, logistic regression models were used to compare the side effects for the Pfizer and Moderna vaccines for each country. Results We observed some differences in the ratio of side effects between the public reports and tweets. Specifically, fever was mentioned much more frequently in tweets than would be expected based on the public reports. We also observed differences in side effects reported between Pfizer and Moderna vaccines from Japan and Indonesia, with more side effects reported for the Pfizer vaccine in Japanese tweets and more side effects with the Moderna vaccine reported in Indonesian tweets. Conclusions We note the possible consequences of vaccine side effect surveillance on Twitter and information dissemination, in that fever appears to be over-represented. This could be due to fever possibly having a higher severity or measurability, and further implications are discussed.
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Affiliation(s)
- Kiki Ferawati
- Graduate School of Science and Technology Nara Institute of Science and Technology Ikoma Japan
| | - Kongmeng Liew
- Graduate School of Science and Technology Nara Institute of Science and Technology Ikoma Japan
| | - Eiji Aramaki
- Graduate School of Science and Technology Nara Institute of Science and Technology Ikoma Japan
| | - Shoko Wakamiya
- Graduate School of Science and Technology Nara Institute of Science and Technology Ikoma Japan
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53
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Fieselmann J, Annac K, Erdsiek F, Yilmaz-Aslan Y, Brzoska P. What are the reasons for refusing a COVID-19 vaccine? A qualitative analysis of social media in Germany. BMC Public Health 2022; 22:846. [PMID: 35484619 PMCID: PMC9046705 DOI: 10.1186/s12889-022-13265-y] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 04/20/2022] [Indexed: 11/10/2022] Open
Abstract
Background Vaccination against COVID-19 has been available in Germany since December 2020. However, about 30% of the population report not wanting to be vaccinated. In order to increase the willingness of the population to get vaccinated, data on the acceptance of vaccination and its influencing factors are necessary. Little is known about why individuals refuse the COVID-19 vaccination. The aim of this study was to investigate the reasons leading to rejecting vaccination, based on posts from three social media sites. Methods The German-language versions of Instagram, Twitter and YouTube were searched regarding negative attitudes towards COVID-19 vaccination. Data was extracted until a saturation effect could be observed. The data included posts created from January 20, 2020 to May 2, 2021. This time frame roughly covers the period from the first reports of the spread of SARS-CoV-2 up to the general availability of vaccines against COVID-19 in Germany. We used an interpretive thematic approach to analyze the data and to inductively generate codes, subcategories and categories. Results Based on 333 posts written by 323 contributing users, we identified six main categories of reasons for refusing a COVID-19 vaccination: Low perceived benefit of vaccination, low perceived risk of contracting COVID-19, health concerns, lack of information, systemic mistrust and spiritual or religious reasons. The analysis reveals a lack of information among users and the spread of misinformation with regard to COVID-19 and vaccination. Users feel inadequately informed about vaccination or do not understand the information available. These information gaps may be related to information not being sufficiently sensitive to the needs of the target group. In addition to limited information for the general population, misinformation on the internet can also be an important reason for refusing vaccination. Conclusions The study emphasizes the relevance of providing trustworthy and quality-assured information on COVID-19 and COVID-19 vaccination to all population groups. In addition, vaccinations should be easily accessible in order to promote the population’s willingness to be vaccinated.
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Affiliation(s)
- Jana Fieselmann
- Faculty of Health, School of Medicine, Health Services Research, Witten/Herdecke University, Witten, Germany
| | - Kübra Annac
- Faculty of Health, School of Medicine, Health Services Research, Witten/Herdecke University, Witten, Germany
| | - Fabian Erdsiek
- Faculty of Health, School of Medicine, Health Services Research, Witten/Herdecke University, Witten, Germany
| | - Yüce Yilmaz-Aslan
- Faculty of Health, School of Medicine, Health Services Research, Witten/Herdecke University, Witten, Germany.,Faculty of Health Sciences, Nursing and Health Services Research, Bielefeld University, Bielefeld, Germany.,Faculty of Health Sciences, Epidemiology & International Public Health, Bielefeld University, Bielefeld, Germany
| | - Patrick Brzoska
- Faculty of Health, School of Medicine, Health Services Research, Witten/Herdecke University, Witten, Germany.
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54
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Zou H, Xiang K. Sentiment Classification Method Based on Blending of Emoticons and Short Texts. ENTROPY 2022; 24:e24030398. [PMID: 35327909 PMCID: PMC8965825 DOI: 10.3390/e24030398] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 03/09/2022] [Accepted: 03/11/2022] [Indexed: 12/04/2022]
Abstract
With the development of Internet technology, short texts have gradually become the main medium for people to obtain information and communicate. Short text reduces the threshold of information production and reading by virtue of its short length, which is in line with the trend of fragmented reading in the context of the current fast-paced life. In addition, short texts contain emojis to make the communication immersive. However, short-text content means it contains relatively little information, which is not conducive to the analysis of sentiment characteristics. Therefore, this paper proposes a sentiment classification method based on the blending of emoticons and short-text content. Emoticons and short-text content are transformed into vectors, and the corresponding word vector and emoticon vector are connected into a sentencing matrix in turn. The sentence matrix is input into a convolution neural network classification model for classification. The results indicate that, compared with existing methods, the proposed method improves the accuracy of analysis.
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Affiliation(s)
- Haochen Zou
- Department of Computer Science and Software Engineering, Concordia University, Montreal, QC H3G 1M8, Canada
- Correspondence:
| | - Kun Xiang
- Department of Science and Engineering, Hosei University, Koganei 184-8584, Tokyo, Japan;
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55
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Portelli B, Scaboro S, Tonino R, Chersoni E, Santus E, Serra G. Monitoring user opinions and side effects on COVID-19 vaccines in the Twittersphere: Infodemiology Study of Tweets. J Med Internet Res 2022; 24:e35115. [PMID: 35446781 PMCID: PMC9132143 DOI: 10.2196/35115] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 01/29/2022] [Accepted: 03/09/2022] [Indexed: 11/18/2022] Open
Abstract
Background In the current phase of the COVID-19 pandemic, we are witnessing the most massive vaccine rollout in human history. Like any other drug, vaccines may cause unexpected side effects, which need to be investigated in a timely manner to minimize harm in the population. If not properly dealt with, side effects may also impact public trust in the vaccination campaigns carried out by national governments. Objective Monitoring social media for the early identification of side effects, and understanding the public opinion on the vaccines are of paramount importance to ensure a successful and harmless rollout. The objective of this study was to create a web portal to monitor the opinion of social media users on COVID-19 vaccines, which can offer a tool for journalists, scientists, and users alike to visualize how the general public is reacting to the vaccination campaign. Methods We developed a tool to analyze the public opinion on COVID-19 vaccines from Twitter, exploiting, among other techniques, a state-of-the-art system for the identification of adverse drug events on social media; natural language processing models for sentiment analysis; statistical tools; and open-source databases to visualize the trending hashtags, news articles, and their factuality. All modules of the system are displayed through an open web portal. Results A set of 650,000 tweets was collected and analyzed in an ongoing process that was initiated in December 2020. The results of the analysis are made public on a web portal (updated daily), together with the processing tools and data. The data provide insights on public opinion about the vaccines and its change over time. For example, users show a high tendency to only share news from reliable sources when discussing COVID-19 vaccines (98% of the shared URLs). The general sentiment of Twitter users toward the vaccines is negative/neutral; however, the system is able to record fluctuations in the attitude toward specific vaccines in correspondence with specific events (eg, news about new outbreaks). The data also show how news coverage had a high impact on the set of discussed topics. To further investigate this point, we performed a more in-depth analysis of the data regarding the AstraZeneca vaccine. We observed how media coverage of blood clot–related side effects suddenly shifted the topic of public discussions regarding both the AstraZeneca and other vaccines. This became particularly evident when visualizing the most frequently discussed symptoms for the vaccines and comparing them month by month. Conclusions We present a tool connected with a web portal to monitor and display some key aspects of the public’s reaction to COVID-19 vaccines. The system also provides an overview of the opinions of the Twittersphere through graphic representations, offering a tool for the extraction of suspected adverse events from tweets with a deep learning model.
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Affiliation(s)
- Beatrice Portelli
- Department of Mathematics, Computer Science and Physics, University of Udine, via delle Scienze 206, Udine, IT
| | - Simone Scaboro
- Department of Mathematics, Computer Science and Physics, University of Udine, via delle Scienze 206, Udine, IT
| | - Roberto Tonino
- Department of Mathematics, Computer Science and Physics, University of Udine, via delle Scienze 206, Udine, IT
| | | | - Enrico Santus
- Decision Science and Advanced Analytics for MAPV & RA, Bayer, Bayer Pharmaceuticals, Whippany, US
| | - Giuseppe Serra
- Department of Mathematics, Computer Science and Physics, University of Udine, via delle Scienze 206, Udine, IT
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56
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Yin H, Song X, Yang S, Li J. Sentiment analysis and topic modeling for COVID-19 vaccine discussions. WORLD WIDE WEB 2022; 25:1067-1083. [PMID: 35250362 PMCID: PMC8879179 DOI: 10.1007/s11280-022-01029-y] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 12/08/2021] [Accepted: 02/17/2022] [Indexed: 05/27/2023]
Abstract
The outbreak of the novel coronavirus disease (COVID-19) has been ongoing for almost two years and has had an unprecedented impact on the daily lives of people around the world. More recently, the emergence of the Delta variant of COVID-19 has once again put the world at risk. Fortunately, many countries and companies have developed vaccines for the coronavirus. As of 23 August 2021, more than 20 vaccines have been approved by the World Health Organization (WHO), bringing light to people besieged by the pandemic. The global rollout of the COVID-19 vaccine has sparked much discussion on social media platforms, such as the effectiveness and safety of the vaccine. However, there has not been much systematic analysis of public opinion on the COVID-19 vaccine. In this study, we conduct an in-depth analysis of the discussions related to the COVID-19 vaccine on Twitter. We analyze the hot topics discussed by people and the corresponding emotional polarity from the perspective of countries and vaccine brands. The results show that most people trust the effectiveness of vaccines and are willing to get vaccinated. In contrast, negative tweets tended to be associated with news reports of post-vaccination deaths, vaccine shortages, and post-injection side effects. Overall, this study uses popular Natural Language Processing (NLP) technologies to mine people's opinions on the COVID-19 vaccine on social media and objectively analyze and visualize them. Our findings can improve the readability of the confusing information on social media platforms and provide effective data support for the government and policy makers.
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Affiliation(s)
- Hui Yin
- School of IT, Deakin University, Geelong, Australia
| | - Xiangyu Song
- School of IT, Deakin University, Geelong, Australia
| | - Shuiqiao Yang
- School of Computer Science and Engineering, University of New South Wales, Sydney, Australia
| | - Jianxin Li
- School of IT, Deakin University, Geelong, Australia
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57
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Jang H, Rempel E, Roe I, Adu PA, Carenini G, Janjua NZ. Tracking Public Attitudes toward COVID-19 Vaccination on Tweets in Canada: Using Aspect-based Sentiment Analysis. J Med Internet Res 2022; 24:e35016. [PMID: 35275835 PMCID: PMC8966890 DOI: 10.2196/35016] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 02/22/2022] [Accepted: 02/22/2022] [Indexed: 01/16/2023] Open
Abstract
Background The development and approval of COVID-19 vaccines have generated optimism for the end of the COVID-19 pandemic and a return to normalcy. However, vaccine hesitancy, often fueled by misinformation, poses a major barrier to achieving herd immunity. Objective We aim to investigate Twitter users’ attitudes toward COVID-19 vaccination in Canada after vaccine rollout. Methods We applied a weakly supervised aspect-based sentiment analysis (ABSA) technique, which involves the human-in-the-loop system, on COVID-19 vaccination–related tweets in Canada. Automatically generated aspect and opinion terms were manually corrected by public health experts to ensure the accuracy of the terms and make them more domain-specific. Then, based on these manually corrected terms, the system inferred sentiments toward the aspects. We observed sentiments toward key aspects related to COVID-19 vaccination, and investigated how sentiments toward “vaccination” changed over time. In addition, we analyzed the most retweeted or liked tweets by observing most frequent nouns and sentiments toward key aspects. Results After applying the ABSA system, we obtained 170 aspect terms (eg, “immunity” and “pfizer”) and 6775 opinion terms (eg, “trustworthy” for the positive sentiment and “jeopardize” for the negative sentiment). While manually verifying or editing these terms, our public health experts selected 20 key aspects related to COVID-19 vaccination for analysis. The sentiment analysis results for the 20 key aspects revealed negative sentiments related to “vaccine distribution,” “side effects,” “allergy,” “reactions,” and “anti-vaxxer,” and positive sentiments related to “vaccine campaign,” “vaccine candidates,” and “immune response.” These results indicate that the Twitter users express concerns about the safety of vaccines but still consider vaccines as the option to end the pandemic. In addition, compared to the sentiment of the remaining tweets, the most retweeted or liked tweets showed more positive sentiment overall toward key aspects (P<.001), especially vaccines (P<.001) and vaccination (P=.009). Further investigation of the most retweeted or liked tweets revealed two opposing trends in Twitter users who showed negative sentiments toward vaccines: the “anti-vaxxer” population that used negative sentiments as a means to discourage vaccination and the “Covid Zero” population that used negative sentiments to encourage vaccinations while critiquing the public health response. Conclusions Our study examined public sentiments toward COVID-19 vaccination on tweets over an extended period in Canada. Our findings could inform public health agencies to design and implement interventions to promote vaccination.
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Affiliation(s)
- Hyeju Jang
- Department of Computer Science, University of British Columbia, Vancouver, CA.,British Columbia Centre for Disease Control, Vancouver, CA
| | - Emily Rempel
- British Columbia Centre for Disease Control, Vancouver, CA
| | - Ian Roe
- British Columbia Centre for Disease Control, Vancouver, CA
| | - Prince A Adu
- British Columbia Centre for Disease Control, Vancouver, CA
| | - Giuseppe Carenini
- Department of Computer Science, University of British Columbia, Vancouver, CA
| | - Naveed Zafar Janjua
- British Columbia Centre for Disease Control, Vancouver, CA.,School of Population and Public Health, University of British Columbia, Vancouver, CA.,Centre for Health Evaluation and Outcome Sciences, St. Paul's Hospital, Vancouver, CA
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58
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Wang S, Huang X, Hu T, Zhang M, Li Z, Ning H, Corcoran J, Khan A, Liu Y, Zhang J, Li X. The times, they are a-changin': tracking shifts in mental health signals from early phase to later phase of the COVID-19 pandemic in Australia. BMJ Glob Health 2022; 7:e007081. [PMID: 35058303 PMCID: PMC8889467 DOI: 10.1136/bmjgh-2021-007081] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 12/09/2021] [Indexed: 11/24/2022] Open
Abstract
INTRODUCTION Widespread problems of psychological distress have been observed in many countries following the outbreak of COVID-19, including Australia. What is lacking from current scholarship is a national-scale assessment that tracks the shifts in mental health during the pandemic timeline and across geographic contexts. METHODS Drawing on 244 406 geotagged tweets in Australia from 1 January 2020 to 31 May 2021, we employed machine learning and spatial mapping techniques to classify, measure and map changes in the Australian public's mental health signals, and track their change across the different phases of the pandemic in eight Australian capital cities. RESULTS Australians' mental health signals, quantified by sentiment scores, have a shift from pessimistic (early pandemic) to optimistic (middle pandemic), reflected by a 174.1% (95% CI 154.8 to 194.5) increase in sentiment scores. However, the signals progressively recessed towards a more pessimistic outlook (later pandemic) with a decrease in sentiment scores by 48.8% (95% CI 34.7 to 64.9). Such changes in mental health signals vary across capital cities. CONCLUSION We set out a novel empirical framework using social media to systematically classify, measure, map and track the mental health of a nation. Our approach is designed in a manner that can readily be augmented into an ongoing monitoring capacity and extended to other nations. Tracking locales where people are displaying elevated levels of pessimistic mental health signals provide important information for the smart deployment of finite mental health services. This is especially critical in a time of crisis during which resources are stretched beyond normal bounds.
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Affiliation(s)
- Siqin Wang
- School of Earth and Environmental Sciences, The University of Queensland, Saint Lucia, Queensland, Australia
| | - Xiao Huang
- Department of Geosciences, University of Arkansas, Fayetteville, Arkansas, USA
| | - Tao Hu
- Department of Geography, Oklahoma State University, Stillwater, Oklahoma, USA
| | - Mengxi Zhang
- Department of Nutrition and Health Science, Ball State University, Muncie, Indiana, USA
| | - Zhenlong Li
- Geoinformation and Big Data Research Laboratory, Department of Geography, University of South Carolina, Columbia, South Carolina, USA
- Big Data Health Science Center, University of South Carolina, Columbia, South Carolina, USA
| | - Huan Ning
- Geoinformation and Big Data Research Laboratory, Department of Geography, University of South Carolina, Columbia, South Carolina, USA
- Big Data Health Science Center, University of South Carolina, Columbia, South Carolina, USA
| | - Jonathan Corcoran
- School of Earth and Environmental Sciences, The University of Queensland, Saint Lucia, Queensland, Australia
| | - Asaduzzaman Khan
- School of Health and Rehabilitation Sciences, The University of Queensland - Saint Lucia Campus, Saint Lucia, Queensland, Australia
| | - Yan Liu
- School of Earth and Environmental Sciences, The University of Queensland, Saint Lucia, Queensland, Australia
| | - Jiajia Zhang
- Big Data Health Science Center, University of South Carolina, Columbia, South Carolina, USA
- Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, USA
| | - Xiaoming Li
- Big Data Health Science Center, University of South Carolina, Columbia, South Carolina, USA
- Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, USA
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COVID-19 analytics: Towards the effect of vaccine brands through analyzing public sentiment of tweets. INFORMATICS IN MEDICINE UNLOCKED 2022; 31:100969. [PMID: 35620215 PMCID: PMC9121735 DOI: 10.1016/j.imu.2022.100969] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 05/11/2022] [Accepted: 05/12/2022] [Indexed: 02/08/2023] Open
Abstract
The COVID-19 outbreak has created effects on everyday life worldwide. Many research teams at major pharmaceutical companies and research institutes in various countries have been producing vaccines since the beginning of the outbreak. There is an impact of gender on vaccine responses, acceptance, and outcomes. Worldwide promotion of the COVID-19 vaccine additionally generates a huge amount of discussions on social media platforms about diverse factors of vaccines including protection and efficacy. Twitter is considered one of the most well-known social media platforms which have been widely used to share a public opinion on vaccine-related problems in the COVID-19 pandemic. However, there is a lack of research work to analyze the public perception of COVID-19 vaccines systematically from a gender perspective. In this paper, we perform an in-depth analysis of the coronavirus vaccine-related tweets to understand the people's sentiment towards various vaccine brands corresponding to the gender level. The proposed method focuses on the effect of COVID-19 vaccines on gender by taking into account descriptive, diagnostic, predictive, and prescriptive analytics on the Twitter dataset. We also conduct experiments with deep learning models to determine the sentiment polarities of tweets, which are positive, neutral, and negative. The results reveal that LSTM performs better compared to other models with an accuracy rate of 85.7%.
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60
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Saini V, Liang LL, Yang YC, Le HM, Wu CY. The Association Between Dissemination and Characteristics of Pro-/Anti-COVID-19 Vaccine Messages on Twitter: Application of the Elaboration Likelihood Model. JMIR INFODEMIOLOGY 2022; 2:e37077. [PMID: 35783451 PMCID: PMC9239316 DOI: 10.2196/37077] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 05/28/2022] [Accepted: 06/16/2022] [Indexed: 01/16/2023]
Abstract
Background Messages on one's stance toward vaccination on microblogging sites may affect the reader's decision on whether to receive a vaccine. Understanding the dissemination of provaccine and antivaccine messages relating to COVID-19 on social media is crucial; however, studies on this topic have remained limited. Objective This study applies the elaboration likelihood model (ELM) to explore the characteristics of vaccine stance messages that may appeal to Twitter users. First, we examined the associations between the characteristics of vaccine stance tweets and the likelihood and number of retweets. Second, we identified the relative importance of the central and peripheral routes in decision-making on sharing a message. Methods English-language tweets from the United States that contained provaccine and antivaccine hashtags (N=150,338) were analyzed between April 26 and August 26, 2021. Logistic and generalized negative binomial regressions were conducted to predict retweet outcomes. The content-related central-route predictors were measured using the numbers of hashtags and mentions, emotional valence, emotional intensity, and concreteness. The content-unrelated peripheral-route predictors were measured using the numbers of likes and followers and whether the source was a verified user. Results Content-related characteristics played a prominent role in shaping decisions regarding whether to retweet antivaccine messages. Particularly, positive valence (incidence rate ratio [IRR]=1.32, P=.03) and concreteness (odds ratio [OR]=1.17, P=.01) were associated with higher numbers and likelihood of retweets of antivaccine messages, respectively; emotional intensity (subjectivity) was associated with fewer retweets of antivaccine messages (OR=0.78, P=.03; IRR=0.80, P=.04). However, these factors had either no or only small effects on the sharing of provaccine tweets. Retweets of provaccine messages were primarily determined by content-unrelated characteristics, such as the numbers of likes (OR=2.55, IRR=2.24, P<.001) and followers (OR=1.31, IRR=1.28, P<.001). Conclusions The dissemination of antivaccine messages is associated with both content-related and content-unrelated characteristics. By contrast, the dissemination of provaccine messages is primarily driven by content-unrelated characteristics. These findings signify the importance of leveraging the peripheral route to promote the dissemination of provaccine messages. Because antivaccine tweets with positive emotions, objective content, and concrete words are more likely to be disseminated, policymakers should pay attention to antivaccine messages with such characteristics.
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Affiliation(s)
- Vipin Saini
- Department of Information Management College of Management National Sun Yet-sen University Kaohsiung Taiwan
| | - Li-Lin Liang
- Institute of Public Health College of Medicine National Yang Ming Chiao Tung University Taipei Taiwan.,Department of Business Management College of Management National Sun Yat-sen University Kaohsiung Taiwan.,Research Center for Epidemic Prevention National Yang Ming Chiao Tung University Taipei Taiwan.,Health Innovation Center National Yang Ming Chiao Tung University Taipei Taiwan
| | - Yu-Chen Yang
- Department of Information Management College of Management National Sun Yet-sen University Kaohsiung Taiwan
| | - Huong Mai Le
- Department of Business Management College of Management National Sun Yat-sen University Kaohsiung Taiwan
| | - Chun-Ying Wu
- Research Center for Epidemic Prevention National Yang Ming Chiao Tung University Taipei Taiwan.,Health Innovation Center National Yang Ming Chiao Tung University Taipei Taiwan.,Institute of Biomedical Informatics College of Medicine National Yang Ming Chiao Tung University Taipei Taiwan
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Niu Q, Liu J, Kato M, Shinohara Y, Matsumura N, Aoyama T, Nagai-Tanima M. Public Opinion and Sentiment Before and at the Beginning of COVID-19 Vaccinations in Japan: Twitter Analysis. JMIR INFODEMIOLOGY 2022; 2:e32335. [PMID: 35578643 PMCID: PMC9092950 DOI: 10.2196/32335] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 12/25/2021] [Accepted: 04/19/2022] [Indexed: 02/06/2023]
Abstract
Background COVID-19 vaccines are considered one of the most effective ways for containing the COVID-19 pandemic, but Japan lagged behind other countries in vaccination in the early stages. A deeper understanding of the slow progress of vaccination in Japan can be instructive for COVID-19 booster vaccination and vaccinations during future pandemics. Objective This retrospective study aims to analyze the slow progress of early-stage vaccination in Japan by exploring opinions and sentiment toward the COVID-19 vaccine in Japanese tweets before and at the beginning of vaccination. Methods We collected 144,101 Japanese tweets containing COVID-19 vaccine-related keywords between August 1, 2020, and June 30, 2021. We visualized the trend of the tweets and sentiments and identified the critical events that may have triggered the surges. Correlations between sentiments and the daily infection, death, and vaccination cases were calculated. The latent dirichlet allocation model was applied to identify topics of negative tweets from the beginning of vaccination. We also conducted an analysis of vaccine brands (Pfizer, Moderna, AstraZeneca) approved in Japan. Results The daily number of tweets continued with accelerating growth after the start of large-scale vaccinations in Japan. The sentiments of around 85% of the tweets were neutral, and negative sentiment overwhelmed the positive sentiment in the other tweets. We identified 6 public-concerned topics related to the negative sentiment at the beginning of the vaccination process. Among the vaccines from the 3 manufacturers, the attitude toward Moderna was the most positive, and the attitude toward AstraZeneca was the most negative. Conclusions Negative sentiment toward vaccines dominated positive sentiment in Japan, and the concerns about side effects might have outweighed fears of infection at the beginning of the vaccination process. Topic modeling on negative tweets indicated that the government and policy makers should take prompt actions in building a safe and convenient vaccine reservation and rollout system, which requires both flexibility of the medical care system and the acceleration of digitalization in Japan. The public showed different attitudes toward vaccine brands. Policy makers should provide more evidence about the effectiveness and safety of vaccines and rebut fake news to build vaccine confidence.
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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
| | - Yuki Shinohara
- Department of Human Health Sciences Graduate School of Medicine Kyoto University Kyoto Japan
| | - Natsuki Matsumura
- 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
| | - Momoko Nagai-Tanima
- Department of Human Health Sciences Graduate School of Medicine Kyoto University Kyoto Japan
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62
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Gao H, Zhao Q, Ning C, Guo D, Wu J, Li L. Does the COVID-19 Vaccine Still Work That "Most of the Confirmed Cases Had Been Vaccinated"? A Content Analysis of Vaccine Effectiveness Discussion on Sina Weibo during the Outbreak of COVID-19 in Nanjing. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 19:241. [PMID: 35010501 PMCID: PMC8750531 DOI: 10.3390/ijerph19010241] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 12/22/2021] [Accepted: 12/24/2021] [Indexed: 01/19/2023]
Abstract
In July 2021, breakthrough cases were reported in the outbreak of COVID-19 in Nanjing, sparking concern and discussion about the vaccine's effectiveness and becoming a trending topic on Sina Weibo. In order to explore public attitudes towards the COVID-19 vaccine and their emotional orientations, we collected 1542 posts under the trending topic through data mining. We set up four categories of attitudes towards COVID-19 vaccines, and used a big data analysis tool to code and manually checked the coding results to complete the content analysis. The results showed that 45.14% of the Weibo posts (n = 1542) supported the COVID-19 vaccine, 12.97% were neutral, and 7.26% were doubtful, which indicated that the public did not question the vaccine's effectiveness due to the breakthrough cases in Nanjing. There were 66.47% posts that reflected significant negative emotions. Among these, 50.44% of posts with negative emotions were directed towards the media, 25.07% towards the posting users, and 11.51% towards the public, which indicated that the negative emotions were not directed towards the COVID-19 vaccine. External sources outside the vaccine might cause vaccine hesitancy. Public opinions expressed in online media reflect the public's cognition and attitude towards vaccines and their core needs in terms of information. Therefore, online public opinion monitoring could be an essential way to understand the opinions and attitudes towards public health issues.
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Affiliation(s)
- Hao Gao
- School of Journalism and Communication, Nanjing Normal University, Nanjing 210097, China; (H.G.); (Q.Z.); (D.G.)
| | - Qingting Zhao
- School of Journalism and Communication, Nanjing Normal University, Nanjing 210097, China; (H.G.); (Q.Z.); (D.G.)
| | - Chuanlin Ning
- School of Media and Communication, Shanghai Jiao Tong University, Shanghai 200240, China;
| | - Difan Guo
- School of Journalism and Communication, Nanjing Normal University, Nanjing 210097, China; (H.G.); (Q.Z.); (D.G.)
| | - Jing Wu
- Faculty of Social Sciences, University of Ljubljana, 1000 Ljubljana, Slovenia;
| | - Lina Li
- Film-Television and Communication College, Shanghai Normal University, Shanghai 200234, China
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63
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Fazel S, Zhang L, Javid B, Brikell I, Chang Z. Harnessing Twitter data to survey public attention and attitudes towards COVID-19 vaccines in the UK. Sci Rep 2021; 11:23402. [PMID: 34907201 PMCID: PMC8671421 DOI: 10.1038/s41598-021-02710-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 11/16/2021] [Indexed: 11/08/2022] Open
Abstract
Attitudes to COVID-19 vaccination vary considerably within and between countries. Although the contribution of socio-demographic factors to these attitudes has been studied, the role of social media and how it interacts with news about vaccine development and efficacy is uncertain. We examined around 2 million tweets from 522,893 persons in the UK from November 2020 to January 2021 to evaluate links between Twitter content about vaccines and major scientific news announcements about vaccines. The proportion of tweets with negative vaccine content varied, with reductions of 20-24% on the same day as major news announcement. However, the proportion of negative tweets reverted back to an average of around 40% within a few days. Engagement rates were higher for negative tweets. Public health messaging could consider the dynamics of Twitter-related traffic and the potential contribution of more targeted social media campaigns to address vaccine hesitancy.
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Affiliation(s)
- Seena Fazel
- Warneford Hospital, Department of Psychiatry, University of Oxford, Oxford, UK.
| | - Le Zhang
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Babak Javid
- Division of Experimental Medicine, University of California San Francisco, San Francisco, USA
| | - Isabell Brikell
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- National Centre for Register-based Research, Department of Economics and Business Economics, Aarhus BSS, Aarhus University, Aarhus, Denmark
| | - Zheng Chang
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
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64
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Zhang J, Wang Y, Shi M, Wang X. Factors Driving the Popularity and Virality of COVID-19 Vaccine Discourse on Twitter: Text Mining and Data Visualization Study. JMIR Public Health Surveill 2021; 7:e32814. [PMID: 34665761 PMCID: PMC8647971 DOI: 10.2196/32814] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 10/12/2021] [Accepted: 10/13/2021] [Indexed: 01/13/2023] Open
Abstract
BACKGROUND COVID-19 vaccination is considered a critical prevention measure to help end the pandemic. Social media platforms such as Twitter have played an important role in the public discussion about COVID-19 vaccines. OBJECTIVE The aim of this study was to investigate message-level drivers of the popularity and virality of tweets about COVID-19 vaccines using machine-based text-mining techniques. We further aimed to examine the topic communities of the most liked and most retweeted tweets using network analysis and visualization. METHODS We collected US-based English-language public tweets about COVID-19 vaccines from January 1, 2020, to April 30, 2021 (N=501,531). Topic modeling and sentiment analysis were used to identify latent topics and valence, which together with autoextracted information about media presence, linguistic features, and account verification were used in regression models to predict likes and retweets. Among the 2500 most liked tweets and 2500 most retweeted tweets, network analysis and visualization were used to detect topic communities and present the relationship between the topics and the tweets. RESULTS Topic modeling yielded 12 topics. The regression analyses showed that 8 topics positively predicted likes and 7 topics positively predicted retweets, among which the topic of vaccine development and people's views and that of vaccine efficacy and rollout had relatively larger effects. Network analysis and visualization revealed that the 2500 most liked and most retweeted retweets clustered around the topics of vaccine access, vaccine efficacy and rollout, vaccine development and people's views, and vaccination status. The overall valence of the tweets was positive. Positive valence increased likes, but valence did not affect retweets. Media (photo, video, gif) presence and account verification increased likes and retweets. Linguistic features had mixed effects on likes and retweets. CONCLUSIONS This study suggests the public interest in and demand for information about vaccine development and people's views, and about vaccine efficacy and rollout. These topics, along with the use of media and verified accounts, have enhanced the popularity and virality of tweets. These topics could be addressed in vaccine campaigns to help the diffusion of content on Twitter.
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Affiliation(s)
- Jueman Zhang
- Polk School of Communications, Long Island University, Brooklyn, NY, United States
| | - Yi Wang
- Department of Communication, University of Louisville, Louisville, KY, United States
| | | | - Xiuli Wang
- School of New Media, Peking University, Beijing, China
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65
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Alam KN, Khan MS, Dhruba AR, Khan MM, Al-Amri JF, Masud M, Rawashdeh M. Deep Learning-Based Sentiment Analysis of COVID-19 Vaccination Responses from Twitter Data. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:4321131. [PMID: 34899965 PMCID: PMC8660217 DOI: 10.1155/2021/4321131] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 11/12/2021] [Indexed: 11/17/2022]
Abstract
The COVID-19 pandemic has had a devastating effect on many people, creating severe anxiety, fear, and complicated feelings or emotions. After the initiation of vaccinations against coronavirus, people's feelings have become more diverse and complex. Our aim is to understand and unravel their sentiments in this research using deep learning techniques. Social media is currently the best way to express feelings and emotions, and with the help of Twitter, one can have a better idea of what is trending and going on in people's minds. Our motivation for this research was to understand the diverse sentiments of people regarding the vaccination process. In this research, the timeline of the collected tweets was from December 21 to July21. The tweets contained information about the most common vaccines available recently from across the world. The sentiments of people regarding vaccines of all sorts were assessed using the natural language processing (NLP) tool, Valence Aware Dictionary for sEntiment Reasoner (VADER). Initializing the polarities of the obtained sentiments into three groups (positive, negative, and neutral) helped us visualize the overall scenario; our findings included 33.96% positive, 17.55% negative, and 48.49% neutral responses. In addition, we included our analysis of the timeline of the tweets in this research, as sentiments fluctuated over time. A recurrent neural network- (RNN-) oriented architecture, including long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM), was used to assess the performance of the predictive models, with LSTM achieving an accuracy of 90.59% and Bi-LSTM achieving 90.83%. Other performance metrics such as precision,, F1-score, and a confusion matrix were also used to validate our models and findings more effectively. This study improves understanding of the public's opinion on COVID-19 vaccines and supports the aim of eradicating coronavirus from the world.
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Affiliation(s)
- Kazi Nabiul Alam
- Department of Electrical and Computer Engineering, North South University, Bashundhara, Dhaka 1229, Bangladesh
| | - Md Shakib Khan
- Department of Electrical and Computer Engineering, North South University, Bashundhara, Dhaka 1229, Bangladesh
| | - Abdur Rab Dhruba
- Department of Electrical and Computer Engineering, North South University, Bashundhara, Dhaka 1229, Bangladesh
| | - Mohammad Monirujjaman Khan
- Department of Electrical and Computer Engineering, North South University, Bashundhara, Dhaka 1229, Bangladesh
| | - Jehad F. Al-Amri
- Department of Information Technology, College of Computers and Information Technology, Taif University, P. O. Box 11099, Taif 21944, Saudi Arabia
| | - Mehedi Masud
- Department of Computer Science, College of Computers and Information Technology, Taif University, Taif 21944, Saudi Arabia
| | - Majdi Rawashdeh
- Department of Business Information Technology, Princess Sumaya University for Technology, P. O. Box 1438, Amman 11941, Jordan
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66
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Alamoodi AH, Zaidan BB, Al-Masawa M, Taresh SM, Noman S, Ahmaro IYY, Garfan S, Chen J, Ahmed MA, Zaidan AA, Albahri OS, Aickelin U, Thamir NN, Fadhil JA, Salahaldin A. Multi-perspectives systematic review on the applications of sentiment analysis for vaccine hesitancy. Comput Biol Med 2021; 139:104957. [PMID: 34735945 PMCID: PMC8520445 DOI: 10.1016/j.compbiomed.2021.104957] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 10/15/2021] [Accepted: 10/15/2021] [Indexed: 01/04/2023]
Abstract
A substantial impediment to widespread Coronavirus disease (COVID-19) vaccination is vaccine hesitancy. Many researchers across scientific disciplines have presented countless studies in favor of COVID-19 vaccination, but misinformation on social media could hinder vaccination efforts and increase vaccine hesitancy. Nevertheless, studying people's perceptions on social media to understand their sentiment presents a powerful medium for researchers to identify the causes of vaccine hesitancy and therefore develop appropriate public health messages and interventions. To the best of the authors' knowledge, previous studies have presented vaccine hesitancy in specific cases or within one scientific discipline (i.e., social, medical, and technological). No previous study has presented findings via sentiment analysis for multiple scientific disciplines as follows: (1) social, (2) medical, public health, and (3) technology sciences. Therefore, this research aimed to review and analyze articles related to different vaccine hesitancy cases in the last 11 years and understand the application of sentiment analysis on the most important literature findings. Articles were systematically searched in Web of Science, Scopus, PubMed, IEEEXplore, ScienceDirect, and Ovid from January 1, 2010, to July 2021. A total of 30 articles were selected on the basis of inclusion and exclusion criteria. These articles were formed into a taxonomy of literature, along with challenges, motivations, and recommendations for social, medical, and public health and technology sciences. Significant patterns were identified, and opportunities were promoted towards the understanding of this phenomenon.
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Affiliation(s)
- A H Alamoodi
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris (UPSI), Perak, Malaysia.
| | - B B Zaidan
- Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan, ROC
| | - Maimonah Al-Masawa
- Centre for Tissue Engineering and Regenerative Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia Medical Centre, Jalan Yaacob Latif, 56000, Kuala Lumpur, Malaysia
| | - Sahar M Taresh
- Department of Kindergarten Educational Psychology, Taiz University, Yemen
| | - Sarah Noman
- Department of Community Health, Faculty of Medicine & Health Sciences, Universiti Putra Malaysia, Malaysia
| | - Ibraheem Y Y Ahmaro
- Computer Science Department, College of Information Technology, Hebron University, Hebron, Palestine
| | - Salem Garfan
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris (UPSI), Perak, Malaysia
| | - Juliana Chen
- The University of Sydney, Charles Perkins Centre, Discipline of Nutrition and Dietetics, School of Life and Environmental Sciences, Camperdown, New South Wales, Australia; Department of Clinical Medicine, Faculty of Medicine and Health Sciences, Macquarie University, Australia; Healthy Weight Clinic, MQ Health, Macquarie University Hospital, Australia
| | - M A Ahmed
- Computer Science and Mathematics College, Tikrit University, Iraq
| | - A A Zaidan
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris (UPSI), Perak, Malaysia
| | - O S Albahri
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris (UPSI), Perak, Malaysia
| | - Uwe Aickelin
- School of Computing and Information Systems, University of Melbourne, 700 Swanston Street, Victoria, 3010, Australia
| | - Noor N Thamir
- Department of Computer Science, University of Baghdad, Iraq
| | - Julanar Ahmed Fadhil
- Faculty of Computer Science and Information Technology, Universiti Putra Malaysia (UPM), 43400 Serdang, Malaysia
| | - Asmaa Salahaldin
- College of Graduate Studies, Universiti Tenaga Nasional (UNITEN), Kajang, Selangor, Malaysia
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67
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Suppan M, Stuby L, Harbarth S, Fehlmann CA, Achab S, Abbas M, Suppan L. Nationwide Deployment of a Serious Game Designed to Improve COVID-19 Infection Prevention Practices in Switzerland: Prospective Web-Based Study. JMIR Serious Games 2021; 9:e33003. [PMID: 34635472 PMCID: PMC8623323 DOI: 10.2196/33003] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 09/18/2021] [Accepted: 10/12/2021] [Indexed: 12/18/2022] Open
Abstract
Background Lassitude and a rather high degree of mistrust toward the authorities can make regular or overly constraining COVID-19 infection prevention and control campaigns inefficient and even counterproductive. Serious games provide an original, engaging, and potentially effective way of disseminating COVID-19 infection prevention and control guidelines. Escape COVID-19 is a serious game for teaching COVID-19 infection prevention and control practices that has previously been validated in a population of nursing home personnel. Objective We aimed to identify factors learned from playing the serious game Escape COVID-19 that facilitate or impede intentions of changing infection prevention and control behavior in a large and heterogeneous Swiss population. Methods This fully automated, prospective web-based study, compliant with the Checklist for Reporting Results of Internet E-Surveys (CHERRIES), was conducted in all 3 main language regions of Switzerland. After creating an account on the platform, participants were asked to complete a short demographic questionnaire before accessing the serious game. The only incentive given to the potential participants was a course completion certificate, which participants obtained after completing the postgame questionnaire. The primary outcome was the proportion of participants who reported that they were willing to change their infection prevention and control behavior. Secondary outcomes were the infection prevention and control areas affected by this willingness and the presumed evolution in the use of specific personal protective equipment items. The elements associated with intention to change infection prevention and control behavior, or lack thereof, were also assessed. Other secondary outcomes were the subjective perceptions regarding length, difficulty, meaningfulness, and usefulness of the serious game; impression of engagement and boredom while playing the serious game; and willingness to recommend its use to friends or colleagues. Results From March 9 to June 9, 2021, a total of 3227 accounts were created on the platform, and 1104 participants (34.2%) completed the postgame questionnaire. Of the 1104 respondents, 509 respondents (46.1%) answered that they intended to change their infection prevention and control behavior after playing the game. Among the respondents who answered that they did not intend to change their behavior, 86.1% (512/595) answered that they already apply these guidelines. Participants who followed the German version were less likely to intend to change their infection prevention and control behavior (odds ratio [OR] 0.48, 95% CI 0.24-0.96; P=.04) and found the game less engaging (P<.001). Conversely, participants aged 53 years or older had stronger intentions of changing infection prevention and control behavior (OR 2.07, 95% CI 1.44-2.97; P<.001). Conclusions Escape COVID-19 is a useful tool to enhance correct infection prevention and control measures on a national scale, even after 2 COVID-19 pandemic waves; however, the serious game's impact was affected by language, age category, and previous educational training, and the game should be adapted to enhance its impact on specific populations.
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Affiliation(s)
- Melanie Suppan
- Division of Anesthesiology, Department of Anesthesiology, Clinical Pharmacology, Intensive Care, and Emergency Medicine, University of Geneva Hospitals and Faculty of Medicine, Geneva, Switzerland
| | | | - Stephan Harbarth
- Infection Control Programme and WHO Collaborating Centre on Patient Safety, University of Geneva Hospitals and Faculty of Medicine, Geneva, Switzerland
| | - Christophe A Fehlmann
- Division of Emergency Medicine, Department of Anesthesiology, Clinical Pharmacology, Intensive Care, and Emergency Medicine, University of Geneva Hospitals and Faculty of Medicine, Geneva, Switzerland
| | - Sophia Achab
- Specialized Facility in Behavioral Addictions ReConnecte, Geneva University Hospitals, Geneva, Switzerland.,WHO Collaborating Center in Training and Research in Mental Health, University of Geneva, Geneva, Switzerland
| | - Mohamed Abbas
- Infection Control Programme and WHO Collaborating Centre on Patient Safety, University of Geneva Hospitals and Faculty of Medicine, Geneva, Switzerland
| | - Laurent Suppan
- Division of Emergency Medicine, Department of Anesthesiology, Clinical Pharmacology, Intensive Care, and Emergency Medicine, University of Geneva Hospitals and Faculty of Medicine, Geneva, Switzerland
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68
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Wang AW, Lan JY, Wang MH, Yu C. The Evolution of Rumors on a Closed Social Networking Platform During COVID-19: Algorithm Development and Content Study. JMIR Med Inform 2021; 9:e30467. [PMID: 34623954 PMCID: PMC8612313 DOI: 10.2196/30467] [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: 05/21/2021] [Revised: 06/29/2021] [Accepted: 09/10/2021] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND In 2020, the COVID-19 pandemic put the world in a crisis regarding both physical and psychological health. Simultaneously, a myriad of unverified information flowed on social media and online outlets. The situation was so severe that the World Health Organization identified it as an infodemic in February 2020. OBJECTIVE The aim of this study was to examine the propagation patterns and textual transformation of COVID-19-related rumors on a closed social media platform. METHODS We obtained a data set of suspicious text messages collected on Taiwan's most popular instant messaging platform, LINE, between January and July 2020. We proposed a classification-based clustering algorithm that could efficiently cluster messages into groups, with each group representing a rumor. For ease of understanding, a group is referred to as a "rumor group." Messages in a rumor group could be identical or could have limited textual differences between them. Therefore, each message in a rumor group is a form of the rumor. RESULTS A total of 936 rumor groups with at least 10 messages each were discovered among 114,124 text messages collected from LINE. Among 936 rumors, 396 (42.3%) were related to COVID-19. Of the 396 COVID-19-related rumors, 134 (33.8%) had been fact-checked by the International Fact-Checking Network-certified agencies in Taiwan and determined to be false or misleading. By studying the prevalence of simplified Chinese characters or phrases in the messages that originated in China, we found that COVID-19-related messages, compared to non-COVID-19-related messages, were more likely to have been written by non-Taiwanese users. The association was statistically significant, with P<.001, as determined by the chi-square independence test. The qualitative investigations of the three most popular COVID-19 rumors revealed that key authoritative figures, mostly medical personnel, were often misquoted in the messages. In addition, these rumors resurfaced multiple times after being fact-checked, usually preceded by major societal events or textual transformations. CONCLUSIONS To fight the infodemic, it is crucial that we first understand why and how a rumor becomes popular. While social media has given rise to an unprecedented number of unverified rumors, it also provides a unique opportunity for us to study the propagation of rumors and their interactions with society. Therefore, we must put more effort into these areas.
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Affiliation(s)
- Andrea W Wang
- Information Operations Research Group, Taipei, Taiwan
| | - Jo-Yu Lan
- Department of Information Engineering and Computer Science, Feng Chia University, Taichung, Taiwan
| | - Ming-Hung Wang
- Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, Taiwan
| | - Chihhao Yu
- Information Operations Research Group, Taipei, Taiwan
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69
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Benis A, Chatsubi A, Levner E, Ashkenazi S. Change in Threads on Twitter Regarding Influenza, Vaccines, and Vaccination During the COVID-19 Pandemic: Artificial Intelligence-Based Infodemiology Study. ACTA ACUST UNITED AC 2021; 1:e31983. [PMID: 34693212 PMCID: PMC8521455 DOI: 10.2196/31983] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 08/05/2021] [Accepted: 09/18/2021] [Indexed: 12/14/2022]
Abstract
Background Discussions of health issues on social media are a crucial information source reflecting real-world responses regarding events and opinions. They are often important in public health care, since these are influencing pathways that affect vaccination decision-making by hesitant individuals. Artificial intelligence methodologies based on internet search engine queries have been suggested to detect disease outbreaks and population behavior. Among social media, Twitter is a common platform of choice to search and share opinions and (mis)information about health care issues, including vaccination and vaccines. Objective Our primary objective was to support the design and implementation of future eHealth strategies and interventions on social media to increase the quality of targeted communication campaigns and therefore increase influenza vaccination rates. Our goal was to define an artificial intelligence–based approach to elucidate how threads in Twitter on influenza vaccination changed during the COVID-19 pandemic. Such findings may support adapted vaccination campaigns and could be generalized to other health-related mass communications. Methods The study comprised the following 5 stages: (1) collecting tweets from Twitter related to influenza, vaccines, and vaccination in the United States; (2) data cleansing and storage using machine learning techniques; (3) identifying terms, hashtags, and topics related to influenza, vaccines, and vaccination; (4) building a dynamic folksonomy of the previously defined vocabulary (terms and topics) to support the understanding of its trends; and (5) labeling and evaluating the folksonomy. Results We collected and analyzed 2,782,720 tweets of 420,617 unique users between December 30, 2019, and April 30, 2021. These tweets were in English, were from the United States, and included at least one of the following terms: “flu,” “influenza,” “vaccination,” “vaccine,” and “vaxx.” We noticed that the prevalence of the terms vaccine and vaccination increased over 2020, and that “flu” and “covid” occurrences were inversely correlated as “flu” disappeared over time from the tweets. By combining word embedding and clustering, we then identified a folksonomy built around the following 3 topics dominating the content of the collected tweets: “health and medicine (biological and clinical aspects),” “protection and responsibility,” and “politics.” By analyzing terms frequently appearing together, we noticed that the tweets were related mainly to COVID-19 pandemic events. Conclusions This study focused initially on vaccination against influenza and moved to vaccination against COVID-19. Infoveillance supported by machine learning on Twitter and other social media about topics related to vaccines and vaccination against communicable diseases and their trends can lead to the design of personalized messages encouraging targeted subpopulations’ engagement in vaccination. A greater likelihood that a targeted population receives a personalized message is associated with higher response, engagement, and proactiveness of the target population for the vaccination process.
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Affiliation(s)
- Arriel Benis
- Faculty of Industrial Engineering and Technology Management Holon Institute of Technology Holon Israel.,Faculty of Digital Technologies in Medicine Holon Institute of Technology Holon Israel
| | - Anat Chatsubi
- Faculty of Industrial Engineering and Technology Management Holon Institute of Technology Holon Israel
| | - Eugene Levner
- Faculty of Sciences Holon Institute of Technology Holon Israel
| | - Shai Ashkenazi
- Adelson School of Medicine Ariel University Ariel Israel
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70
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Public perception of COVID-19 vaccines from the digital footprints left on Twitter: analyzing positive, neutral and negative sentiments of Twitterati. LIBRARY HI TECH 2021. [DOI: 10.1108/lht-08-2021-0261] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
PurposeTwitter is gaining popularity as a microblogging and social networking service to discuss various social issues. Coronavirus disease 2019 (COVID-19) has become a global pandemic and is discussed worldwide. Social media is an instant platform to deliberate various dimensions of COVID-19. The purpose of the study is to explore and analyze the public sentiments related to COVID-19 vaccines across the Twitter messages (positive, neutral, and negative) and the impact tweets make across digital social circles.Design/methodology/approachTo fetch the vaccine-related posts, a manual examination of randomly selected 500 tweets was carried out to identify the popular hashtags relevant to the vaccine conversation. It was found that the hashtags “covid19vaccine” and “coronavirusvaccine” were the two popular hashtags used to discuss the communications related to COVID-19 vaccines. 23,575 global tweets available in public domain were retrieved through “Twitter Application Programming Interface” (API), using “Orange Software”, an open-source machine learning, data visualization and data mining toolkit. The study was confined to the tweets posted in English language only. The default data cleaning and preprocessing techniques available in the “Orange Software” were applied to the dataset, which include “transformation”, “tokenization” and “filtering”. The “Valence Aware Dictionary for sEntiment Reasoning” (VADER) tool was used for classification of tweets to determine the tweet sentiments (positive, neutral and negative) as well as the degree of sentiments (compound score also known as sentiment score). To assess the influence/impact of tweets account wise (verified and unverified) and sentiment wise (positive, neutral, and negative), the retweets and likes, which offer a sort of reward or acknowledgment of tweets, were used.FindingsA gradual decline in the number of tweets over the time is observed. Majority (11,205; 47.52%) of tweets express positive sentiments, followed by neutral (7,948; 33.71%) and negative sentiments (4,422; 18.75%), respectively. The study also signifies a substantial difference between the impact of tweets tweeted by verified and unverified users. The tweets related to verified users have a higher impact both in terms of retweets (65.91%) and likes (84.62%) compared to the tweets tweeted by unverified users. Tweets expressing positive sentiments have the highest impact both in terms of likes (mean = 10.48) and retweets (mean = 3.07) compared to those that express neutral or negative sentiments.Research limitations/implicationsThe main limitation of the study is that the sentiments of the people expressed over one single social platform, that is, Twitter have been studied which cannot generalize the global public perceptions. There can be a variation in the results when the datasets from other social media platforms will be studied.Practical implicationsThe study will help to know the people's sentiments and beliefs toward the COVID-19 vaccines. Sentiments that people hold about the COVID-19 vaccines are studied, which will help health policymakers understand the polarity (positive, negative, and neutral) of the tweets and thus see the public reaction and reflect the types of information people are exposed to about vaccines. The study can aid the health sectors to intensify positive messages and eliminate negative messages for an enhanced vaccination uptake. The research can also help design more operative vaccine-advocating communication by customizing messages using the obtained knowledge from the sentiments and opinions about the vaccines.Originality/valueThe paper focuses on an essential aspect of COVID-19 vaccines and how people express themselves (positively, neutrally and negatively) on Twitter.
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COVID-19 Vaccine and Social Media in the U.S.: Exploring Emotions and Discussions on Twitter. Vaccines (Basel) 2021; 9:vaccines9101059. [PMID: 34696167 PMCID: PMC8540945 DOI: 10.3390/vaccines9101059] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 09/06/2021] [Accepted: 09/16/2021] [Indexed: 01/18/2023] Open
Abstract
The understanding of the public response to COVID-19 vaccines is the key success factor to control the COVID-19 pandemic. To understand the public response, there is a need to explore public opinion. Traditional surveys are expensive and time-consuming, address limited health topics, and obtain small-scale data. Twitter can provide a great opportunity to understand public opinion regarding COVID-19 vaccines. The current study proposes an approach using computational and human coding methods to collect and analyze a large number of tweets to provide a wider perspective on the COVID-19 vaccine. This study identifies the sentiment of tweets using a machine learning rule-based approach, discovers major topics, explores temporal trend and compares topics of negative and non-negative tweets using statistical tests, and discloses top topics of tweets having negative and non-negative sentiment. Our findings show that the negative sentiment regarding the COVID-19 vaccine had a decreasing trend between November 2020 and February 2021. We found Twitter users have discussed a wide range of topics from vaccination sites to the 2020 U.S. election between November 2020 and February 2021. The findings show that there was a significant difference between tweets having negative and non-negative sentiment regarding the weight of most topics. Our results also indicate that the negative and non-negative tweets had different topic priorities and focuses. This research illustrates that Twitter data can be used to explore public opinion regarding the COVID-19 vaccine.
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Hu T, Wang S, Luo W, Zhang M, Huang X, Yan Y, Liu R, Ly K, Kacker V, She B, Li Z. Revealing Public Opinion Towards COVID-19 Vaccines With Twitter Data in the United States: Spatiotemporal Perspective. J Med Internet Res 2021; 23:e30854. [PMID: 34346888 PMCID: PMC8437406 DOI: 10.2196/30854] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 07/12/2021] [Accepted: 07/26/2021] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND The COVID-19 pandemic has imposed a large, initially uncontrollable, public health crisis both in the United States and across the world, with experts looking to vaccines as the ultimate mechanism of defense. The development and deployment of COVID-19 vaccines have been rapidly advancing via global efforts. Hence, it is crucial for governments, public health officials, and policy makers to understand public attitudes and opinions towards vaccines, such that effective interventions and educational campaigns can be designed to promote vaccine acceptance. OBJECTIVE The aim of this study was to investigate public opinion and perception on COVID-19 vaccines in the United States. We investigated the spatiotemporal trends of public sentiment and emotion towards COVID-19 vaccines and analyzed how such trends relate to popular topics found on Twitter. METHODS We collected over 300,000 geotagged tweets in the United States from March 1, 2020 to February 28, 2021. We examined the spatiotemporal patterns of public sentiment and emotion over time at both national and state scales and identified 3 phases along the pandemic timeline with sharp changes in public sentiment and emotion. Using sentiment analysis, emotion analysis (with cloud mapping of keywords), and topic modeling, we further identified 11 key events and major topics as the potential drivers to such changes. RESULTS An increasing trend in positive sentiment in conjunction with a decrease in negative sentiment were generally observed in most states, reflecting the rising confidence and anticipation of the public towards vaccines. The overall tendency of the 8 types of emotion implies that the public trusts and anticipates the vaccine. This is accompanied by a mixture of fear, sadness, and anger. Critical social or international events or announcements by political leaders and authorities may have potential impacts on public opinion towards vaccines. These factors help identify underlying themes and validate insights from the analysis. CONCLUSIONS The analyses of near real-time social media big data benefit public health authorities by enabling them to monitor public attitudes and opinions towards vaccine-related information in a geo-aware manner, address the concerns of vaccine skeptics, and promote the confidence that individuals within a certain region or community have towards vaccines.
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Affiliation(s)
- Tao Hu
- Department of Geography, Oklahoma State University, Stillwater, OK, United States
- Center for Geographic Analysis, Harvard University, Cambridge, MA, United States
| | - Siqin Wang
- School of Earth and Environmental Sciences, University of Queensland, Brisbane, Australia
| | - Wei Luo
- Department of Geography, National University of Singapore, Singapore, Singapore
| | - Mengxi Zhang
- Department of Nutrition and Health Science, Ball State University, Muncie, IN, United States
| | - Xiao Huang
- Department of Geosciences, University of Arkansas, Fayetteville, AR, United States
| | - Yingwei Yan
- Department of Geography, National University of Singapore, Singapore, Singapore
| | - Regina Liu
- Department of Biology, Mercer University, Macon, GA, United States
| | - Kelly Ly
- Department of Computer Science, University of Massachusetts Lowell, Lowell, MA, United States
| | - Viraj Kacker
- College of Computing, Georgia Institute of Technology, Atlanta, GA, United States
| | - Bing She
- Institute for Social Research, University of Michigan, Ann Arbor, MI, United States
| | - Zhenlong Li
- Geoinformation and Big Data Research Laboratory, Department of Geography, University of South Carolina, Columbia, SC, United States
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Yousefinaghani S, Dara R, Mubareka S, Papadopoulos A, Sharif S. An analysis of COVID-19 vaccine sentiments and opinions on Twitter. Int J Infect Dis 2021; 108:256-262. [PMID: 34052407 PMCID: PMC8157498 DOI: 10.1016/j.ijid.2021.05.059] [Citation(s) in RCA: 76] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 05/12/2021] [Accepted: 05/22/2021] [Indexed: 12/17/2022] Open
Abstract
OBJECTIVE We identified public sentiments and opinions toward the COVID-19 vaccines based on the content of Twitter. MATERIALS AND METHODS We retrieved 4,552,652 publicly available tweets posted within the timeline of January 2020 to January 2021. Following extraction, we identified vaccine sentiments and opinions of tweets and compared their progression by time, geographical distribution, main themes, keywords, posts engagement metrics and accounts characteristics. RESULTS We found a slight difference in the prevalence of positive and negative sentiments, with positive being the dominant polarity and having higher engagements. The amount of discussion on vaccine rejection and hesitancy was more than interest in vaccines during the course of the study, but the pattern was different in various countries. We found the accounts producing vaccine opposition content were partly Twitter bots or political activists while well-known individuals and organizations generated the content in favour of vaccination. CONCLUSION Understanding sentiments and opinions toward vaccination using Twitter may help public health agencies to increase positive messaging and eliminate opposing messages in order to enhance vaccine uptake.
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Affiliation(s)
| | - Rozita Dara
- School of Computer Science, University of Guelph, Guelph, Ontario, Canada.
| | | | - Andrew Papadopoulos
- Department of Population Medicine, University of Guelph, Guelph, Ontario, Canada
| | - Shayan Sharif
- Department of Pathobiology, University of Guelph, Guelph, Ontario, Canada
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Jiang LC, Chu TH, Sun M. Characterization of Vaccine Tweets During the Early Stage of the COVID-19 Outbreak in the United States: Topic Modeling Analysis. JMIR INFODEMIOLOGY 2021; 1:e25636. [PMID: 34604707 PMCID: PMC8448459 DOI: 10.2196/25636] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 12/30/2020] [Accepted: 06/21/2021] [Indexed: 04/28/2023]
Abstract
BACKGROUND During the early stages of the COVID-19 pandemic, developing safe and effective coronavirus vaccines was considered critical to arresting the spread of the disease. News and social media discussions have extensively covered the issue of coronavirus vaccines, with a mixture of vaccine advocacies, concerns, and oppositions. OBJECTIVE This study aimed to uncover the emerging themes in Twitter users' perceptions and attitudes toward vaccines during the early stages of the COVID-19 outbreak. METHODS This study employed topic modeling to analyze tweets related to coronavirus vaccines at the start of the COVID-19 outbreak in the United States (February 21 to March 20, 2020). We created a predefined query (eg, "COVID" AND "vaccine") to extract the tweet text and metadata (number of followers of the Twitter account and engagement metrics based on likes, comments, and retweeting) from the Meltwater database. After preprocessing the data, we tested Latent Dirichlet Allocation models to identify topics associated with these tweets. The model specifying 20 topics provided the best overall coherence, and each topic was interpreted based on its top associated terms. RESULTS In total, we analyzed 100,209 tweets containing keywords related to coronavirus and vaccines. The 20 topics were further collapsed based on shared similarities, thereby generating 7 major themes. Our analysis characterized 26.3% (26,234/100,209) of the tweets as News Related to Coronavirus and Vaccine Development, 25.4% (25,425/100,209) as General Discussion and Seeking of Information on Coronavirus, 12.9% (12,882/100,209) as Financial Concerns, 12.7% (12,696/100,209) as Venting Negative Emotions, 9.9% (9908/100,209) as Prayers and Calls for Positivity, 8.1% (8155/100,209) as Efficacy of Vaccine and Treatment, and 4.9% (4909/100,209) as Conspiracies about Coronavirus and Its Vaccines. Different themes demonstrated some changes over time, mostly in close association with news or events related to vaccine developments. Twitter users who discussed conspiracy theories, the efficacy of vaccines and treatments, and financial concerns had more followers than those focused on other vaccine themes. The engagement level-the extent to which a tweet being retweeted, quoted, liked, or replied by other users-was similar among different themes, but tweets venting negative emotions yielded the lowest engagement. CONCLUSIONS This study enriches our understanding of public concerns over new vaccines or vaccine development at early stages of the outbreak, bearing implications for influencing vaccine attitudes and guiding public health efforts to cope with infectious disease outbreaks in the future. This study concluded that public concerns centered on general policy issues related to coronavirus vaccines and that the discussions were considerably mixed with political views when vaccines were not made available. Only a small proportion of tweets focused on conspiracy theories, but these tweets demonstrated high engagement levels and were often contributed by Twitter users with more influence.
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Affiliation(s)
- Li Crystal Jiang
- Department of Media and Communication City University of Hong Kong Hong Kong Hong Kong
| | - Tsz Hang Chu
- Department of Media and Communication City University of Hong Kong Hong Kong Hong Kong
| | - Mengru Sun
- College of Media and International Culture Zhejiang University Hangzhou China
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