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Rouhani S, Mozaffari F. Comprehensive analytics of COVID-19 vaccine research: From topic modeling to topic classification. Artif Intell Med 2024; 157:102980. [PMID: 39332065 DOI: 10.1016/j.artmed.2024.102980] [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: 02/19/2023] [Revised: 06/10/2024] [Accepted: 09/09/2024] [Indexed: 09/29/2024]
Abstract
COVID-19 vaccine research has played a vital role in successfully controlling the pandemic, and the research surrounding the coronavirus vaccine is ever-evolving and accruing. These enormous efforts in knowledge production necessitate a structured analysis as secondary research to extract useful insights. In this study, comprehensive analytics was performed to extract these insights, which has moved the boundaries of data analytics in secondary research in the vaccine field by utilizing topic modeling, sentiment analysis, and topic classification based on the abstracts of related publications indexed in Scopus and PubMed. By applying topic modeling to 4803 abstracts filtered by this study criterion, 8 research arenas were identified by merging related topics. The extracted research areas were entitled "Reporting," "Acceptance," "Reaction," "Surveyed Opinions," "Pregnancy," "Titer of Variants," "Categorized Surveys," and "International Approaches." Moreover, the investigation of topics sentiments variations over time led to identifying researchers' attitudes and focus in various years from 2020 to 2022. Finally, a CNN-LSTM classification model was developed to predict the dominant topics and sentiments of new documents based on the 25 pre-determined topics with 75 % accuracy. The findings of this study can be utilized for future research design in this area by quickly grasping the structure of the current research on the COVID-19 vaccine. Through the findings of current research, a classification model was developed to classify the topic of a new article as one of the identified topics. Also, vaccine manufacturing firms will achieve a niche market by having a schema to invest in the gap of fields that have yet to be concentrated in extracted topics.
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Affiliation(s)
- Saeed Rouhani
- Department of IT Management, College of Management, University of Tehran, Tehran, Iran.
| | - Fatemeh Mozaffari
- Department of IT Management, College of Management, University of Tehran, Tehran, Iran.
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Sasse K, Mahabir R, Gkountouna O, Crooks A, Croitoru A. Understanding the determinants of vaccine hesitancy in the United States: A comparison of social surveys and social media. PLoS One 2024; 19:e0301488. [PMID: 38843170 PMCID: PMC11156396 DOI: 10.1371/journal.pone.0301488] [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: 10/15/2023] [Accepted: 03/12/2024] [Indexed: 06/09/2024] Open
Abstract
The COVID-19 pandemic prompted governments worldwide to implement a range of containment measures, including mass gathering restrictions, social distancing, and school closures. Despite these efforts, vaccines continue to be the safest and most effective means of combating such viruses. Yet, vaccine hesitancy persists, posing a significant public health concern, particularly with the emergence of new COVID-19 variants. To effectively address this issue, timely data is crucial for understanding the various factors contributing to vaccine hesitancy. While previous research has largely relied on traditional surveys for this information, recent sources of data, such as social media, have gained attention. However, the potential of social media data as a reliable proxy for information on population hesitancy, especially when compared with survey data, remains underexplored. This paper aims to bridge this gap. Our approach uses social, demographic, and economic data to predict vaccine hesitancy levels in the ten most populous US metropolitan areas. We employ machine learning algorithms to compare a set of baseline models that contain only these variables with models that incorporate survey data and social media data separately. Our results show that XGBoost algorithm consistently outperforms Random Forest and Linear Regression, with marginal differences between Random Forest and XGBoost. This was especially the case with models that incorporate survey or social media data, thus highlighting the promise of the latter data as a complementary information source. Results also reveal variations in influential variables across the five hesitancy classes, such as age, ethnicity, occupation, and political inclination. Further, the application of models to different MSAs yields mixed results, emphasizing the uniqueness of communities and the need for complementary data approaches. In summary, this study underscores social media data's potential for understanding vaccine hesitancy, emphasizes the importance of tailoring interventions to specific communities, and suggests the value of combining different data sources.
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Affiliation(s)
- Kuleen Sasse
- Department of Computer Science, The Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Ron Mahabir
- Geographic Data Science Lab, Department of Geography and Planning, University of Liverpool, Liverpool, United Kingdom
| | - Olga Gkountouna
- Geographic Data Science Lab, Department of Geography and Planning, University of Liverpool, Liverpool, United Kingdom
| | - Andrew Crooks
- Department of Geography, University at Buffalo, Buffalo, New York, United States of America
| | - Arie Croitoru
- Department of Computational and Data Sciences, George Mason University, Fairfax, Virginia, United States of America
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Jordan A, Park A. Understanding the Long Haulers of COVID-19: Mixed Methods Analysis of YouTube Content. JMIR AI 2024; 3:e54501. [PMID: 38875666 PMCID: PMC11184269 DOI: 10.2196/54501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 04/02/2024] [Accepted: 04/06/2024] [Indexed: 06/16/2024]
Abstract
BACKGROUND The COVID-19 pandemic had a devastating global impact. In the United States, there were >98 million COVID-19 cases and >1 million resulting deaths. One consequence of COVID-19 infection has been post-COVID-19 condition (PCC). People with this syndrome, colloquially called long haulers, experience symptoms that impact their quality of life. The root cause of PCC and effective treatments remains unknown. Many long haulers have turned to social media for support and guidance. OBJECTIVE In this study, we sought to gain a better understanding of the long hauler experience by investigating what has been discussed and how information about long haulers is perceived on social media. We specifically investigated the following: (1) the range of symptoms that are discussed, (2) the ways in which information about long haulers is perceived, (3) informational and emotional support that is available to long haulers, and (4) discourse between viewers and creators. We selected YouTube as our data source due to its popularity and wide range of audience. METHODS We systematically gathered data from 3 different types of content creators: medical sources, news sources, and long haulers. To computationally understand the video content and viewers' reactions, we used Biterm, a topic modeling algorithm created specifically for short texts, to analyze snippets of video transcripts and all top-level comments from the comment section. To triangulate our findings about viewers' reactions, we used the Valence Aware Dictionary and Sentiment Reasoner to conduct sentiment analysis on comments from each type of content creator. We grouped the comments into positive and negative categories and generated topics for these groups using Biterm. We then manually grouped resulting topics into broader themes for the purpose of analysis. RESULTS We organized the resulting topics into 28 themes across all sources. Examples of medical source transcript themes were Explanations in layman's terms and Biological explanations. Examples of news source transcript themes were Negative experiences and handling the long haul. The 2 long hauler transcript themes were Taking treatments into own hands and Changes to daily life. News sources received a greater share of negative comments. A few themes of these negative comments included Misinformation and disinformation and Issues with the health care system. Similarly, negative long hauler comments were organized into several themes, including Disillusionment with the health care system and Requiring more visibility. In contrast, positive medical source comments captured themes such as Appreciation of helpful content and Exchange of helpful information. In addition to this theme, one positive theme found in long hauler comments was Community building. CONCLUSIONS The results of this study could help public health agencies, policy makers, organizations, and health researchers understand symptomatology and experiences related to PCC. They could also help these agencies develop their communication strategy concerning PCC.
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Affiliation(s)
- Alexis Jordan
- Department of Software and Information Systems, UNC Charlotte, Charlotte, NC, United States
| | - Albert Park
- Department of Software and Information Systems, UNC Charlotte, Charlotte, NC, United States
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Thom-Jones S, Melgaard I, Gordon CS. Autistic Women's Experience of Motherhood: A Qualitative Analysis of Reddit. J Autism Dev Disord 2024:10.1007/s10803-024-06312-7. [PMID: 38668893 DOI: 10.1007/s10803-024-06312-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/23/2024] [Indexed: 07/25/2024]
Abstract
Autistic mothers remain under-represented in parental and autism research despite the associated physical and psychosocial challenges that accompany the transition to motherhood. Extant literature suggests autistic mothers experience sensory difficulties, communication challenges, stigma, and comorbidities as difficulties, but these studies have focused on autistic women in the perinatal period. The aim of this study was to examine reflections on motherhood from a Reddit community for autistic parents. Identified themes were Autistic Mothering is Different, Autistic Mothers Need Autistic Mothers, Autistic Mothers Experience Stigma, and Learnings from Lockdown. Findings extend existing research by offering insight into the ways autism impacts mothers beyond the perinatal period and have important implications for the future design and delivery of support services for autistic mothers.
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Affiliation(s)
- Sandra Thom-Jones
- Australian Catholic University Limited, Melbourne, VIC, 3777, Australia.
| | - Imogen Melgaard
- School of Behavioural & Health Sciences, Australian Catholic University, Fitzroy, VIC, Australia
| | - Chloe S Gordon
- Institute for Positive Psychology and Education, Australian Catholic University, Fitzroy, VIC, Australia
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Lossio-Ventura JA, Weger R, Lee AY, Guinee EP, Chung J, Atlas L, Linos E, Pereira F. A Comparison of ChatGPT and Fine-Tuned Open Pre-Trained Transformers (OPT) Against Widely Used Sentiment Analysis Tools: Sentiment Analysis of COVID-19 Survey Data. JMIR Ment Health 2024; 11:e50150. [PMID: 38271138 PMCID: PMC10813836 DOI: 10.2196/50150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 11/16/2023] [Accepted: 11/17/2023] [Indexed: 01/27/2024] Open
Abstract
BACKGROUND Health care providers and health-related researchers face significant challenges when applying sentiment analysis tools to health-related free-text survey data. Most state-of-the-art applications were developed in domains such as social media, and their performance in the health care context remains relatively unknown. Moreover, existing studies indicate that these tools often lack accuracy and produce inconsistent results. OBJECTIVE This study aims to address the lack of comparative analysis on sentiment analysis tools applied to health-related free-text survey data in the context of COVID-19. The objective was to automatically predict sentence sentiment for 2 independent COVID-19 survey data sets from the National Institutes of Health and Stanford University. METHODS Gold standard labels were created for a subset of each data set using a panel of human raters. We compared 8 state-of-the-art sentiment analysis tools on both data sets to evaluate variability and disagreement across tools. In addition, few-shot learning was explored by fine-tuning Open Pre-Trained Transformers (OPT; a large language model [LLM] with publicly available weights) using a small annotated subset and zero-shot learning using ChatGPT (an LLM without available weights). RESULTS The comparison of sentiment analysis tools revealed high variability and disagreement across the evaluated tools when applied to health-related survey data. OPT and ChatGPT demonstrated superior performance, outperforming all other sentiment analysis tools. Moreover, ChatGPT outperformed OPT, exhibited higher accuracy by 6% and higher F-measure by 4% to 7%. CONCLUSIONS This study demonstrates the effectiveness of LLMs, particularly the few-shot learning and zero-shot learning approaches, in the sentiment analysis of health-related survey data. These results have implications for saving human labor and improving efficiency in sentiment analysis tasks, contributing to advancements in the field of automated sentiment analysis.
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Affiliation(s)
| | - Rachel Weger
- School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Angela Y Lee
- Department of Communication, Stanford University, Stanford, CA, United States
| | - Emily P Guinee
- National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Joyce Chung
- National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Lauren Atlas
- National Center For Complementary and Alternative Medicine, National Institutes of Health, Bethesda, MD, United States
| | - Eleni Linos
- School of Medicine, Stanford University, Stanford, CA, United States
| | - Francisco Pereira
- National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
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Xin Y, Ni C, Song Q, Yin Z. Fatigue, Pain, and Medication: Mining Online Posts Regarding Rheumatoid Arthritis From Reddit. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2024; 2023:754-763. [PMID: 38222419 PMCID: PMC10785940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/16/2024]
Abstract
Rheumatoid arthritis (RA), a chronic and systemic autoimmune disease that primarily attacks the joints around the body, is affecting a large number of people worldwide through severe symptoms and complications. Therefore, it is crucial to understand these patients' problems and support needs such that effective strategies or solutions can be made to improve their long-term treatment experience. In this paper, we present an in-depth study that is based on the structural topic model to uncover the themes and concerns in online RA posts from Reddit, an American social news aggregation, content rating, and discussion website. In addition, we compared the topic prevalence differences before and after the COVID-19 pandemic to understand the impact of the pandemic on these online users. This study demonstrates the potential of using text-mining techniques on social media data to learn the treatment experiments of RA patients.
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Affiliation(s)
- Yi Xin
- Vanderbilt University, TN, USA
| | | | | | - Zhijun Yin
- Vanderbilt University, TN, USA
- Vanderbilt University Medical Center, TN, USA
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Park B, Jang IS, Kwak D. Sentiment analysis of the COVID-19 vaccine perception. Health Informatics J 2024; 30:14604582241236131. [PMID: 38403926 DOI: 10.1177/14604582241236131] [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] [Indexed: 02/27/2024]
Abstract
The sharp rise in coronavirus cases in the United States, as well as other countries, is driven by variants such as the Omicron substrain, BA4 and BA5. Keeping up to date with COVID-19 vaccination and wearing masks are essential tools for mitigating the pandemic. Social media plays a vital role in sharing and exchanging information, but it also affects perceptions of social phenomena. In this study, we conducted sentiment analysis and topic modeling to investigate vaccine perception using 338,465 COVID-19 vaccine-related comments collected from January 2020 to May 2021 on Reddit. This study stands apart from prior COVID-related research on social media, particularly on Reddit, as it conducted separate analyses for each COVID vaccine and examines public sentiment with various societal events, including vaccine development progress and government responses to COVID. The findings reveal two notable spikes in the number of comments containing the keyword "vaccine". This suggests that discussions about vaccines tend to increase during times of significant social and political events, indicating that people's attention and interest in the topic are influenced by current events. Understanding the public perception of vaccines and identifying factors influencing vaccine perception could help propose appropriate interventions to promote vaccination.
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Affiliation(s)
- Byeonghwa Park
- Department of Management and Marketing, Valdosta State University, Valdosta, GA, USA
| | - In Suk Jang
- Department of Computer Science, Stevens Institute of Technology, Hoboken, NJ, USA
| | - Daehan Kwak
- Department of Computer Science and Technology, Kean University, Union, NJ, USA
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Fedorova E, Ledyaeva S, Kulikova O, Nevredinov A. Governmental anti-pandemic policies, vaccination, population mobility, Twitter narratives, and the spread of COVID-19: Evidence from the European Union countries. RISK ANALYSIS : AN OFFICIAL PUBLICATION OF THE SOCIETY FOR RISK ANALYSIS 2023; 43:1975-2003. [PMID: 36623930 DOI: 10.1111/risa.14088] [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: 07/21/2022] [Revised: 11/24/2022] [Accepted: 12/06/2022] [Indexed: 06/17/2023]
Abstract
We provide large-scale empirical evidence on the effects of multiple governmental regulatory and health policies, vaccination, population mobility, and COVID-19-related Twitter narratives on the spread of a new coronavirus infection. Using multiple-level fixed effects panel data model with weekly data for 27 European Union countries in the period of March 2020-June 2021, we show that governmental response policies were effective both in reducing the number of COVID-19 infection cases and deaths from it, particularly, in the countries with higher level of rule of law. Vaccination expectedly helped to decrease the number of virus cases. Reductions in population mobility in public places and workplaces were also powerful in fighting the pandemic. Next, we identify four core pandemic-related Twitter narratives: governmental response policies, people's sad feelings during the pandemic, vaccination, and pandemic-related international politics. We find that sad feelings' narrative helped to combat the virus spread in EU countries. Our findings also reveal that while in countries with high rule of law international politics' narrative helped to reduce the virus spread, in countries with low rule of law the effect was strictly the opposite. The latter finding suggests that trust in politicians played an important role in confronting the pandemic.
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Affiliation(s)
- Elena Fedorova
- Department of Corporate Finance and Corporate Governance, Financial University, Moscow, Russia
- School of Finance, National Research University Higher School of Economics, Moscow, Russia
| | - Svetlana Ledyaeva
- Department of Finance and Economics, Hanken School of Economics, Helsinki, Finland
| | - Oksana Kulikova
- Department of Economics, Logistics and Quality Management, Siberian State Automobile and Highway University, Omsk, Russia
| | - Alexandr Nevredinov
- Department of Entrepreneurship and International Activity, Bauman Moscow State Technical University, Moscow, Russia
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Kwon S, Park A. Examining thematic and emotional differences across Twitter, Reddit, and YouTube: The case of COVID-19 vaccine side effects. COMPUTERS IN HUMAN BEHAVIOR 2023; 144:107734. [PMID: 36942128 PMCID: PMC10016349 DOI: 10.1016/j.chb.2023.107734] [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: 10/03/2022] [Revised: 01/31/2023] [Accepted: 03/11/2023] [Indexed: 03/17/2023]
Abstract
Social media discourse has become a key data source for understanding the public's perception of, and sentiments during a public health crisis. However, given the different niches which platforms occupy in terms of information exchange, reliance on a single platform would provide an incomplete picture of public opinions. Based on the schema theory, this study suggests a 'social media platform schema' to indicate users' different expectations based on previous usages of platform and argues that a platform's distinct characteristics foster distinct platform schema and, in turn, distinct nature of information. We analyzed COVID-19 vaccine side effect-related discussions from Twitter, Reddit, and YouTube, each of which represents a different type of the platform, and found thematic and emotional differences across platforms. Thematic analysis using k-means clustering algorithm identified seven clusters in each platform. To computationally group and contrast thematic clusters across platforms, we employed modularity analysis using the Louvain algorithm to determine a semantic network structure based on themes. We also observed differences in emotional contexts across platforms. Theoretical and public health implications are then discussed.
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Affiliation(s)
- Soyeon Kwon
- Department of Management Information System, College of Business, Dongguk University, 30, Pildong-ro 1gil, Jung-gu, Seoul, 04620, Republic of Korea
| | - Albert Park
- Department of Software and Information Systems, College of Computing and Informatics, UNC Charlotte, Woodward 310H, 9201 University City Blvd, Charlotte, NC, 28223, USA
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Sirkis T, Maitland S. Monitoring real-time junior doctor sentiment from comments on a public social media platform: a retrospective observational study. Postgrad Med J 2023; 99:423-427. [PMID: 37294728 DOI: 10.1136/pmj-2022-142080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 09/06/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVES To investigate whether sentiment analysis and topic modelling can be used to monitor the sentiment and opinions of junior doctors. DESIGN Retrospective observational study based on comments on a social media website. SETTING Every publicly available comment in r/JuniorDoctorsUK on Reddit from 1 January 2018 to 31 December 2021. PARTICIPANTS 7707 Reddit users who commented in the r/JuniorDoctorsUK subreddit. MAIN OUTCOME MEASURE Sentiment (scored -1 to +1) of comments compared with results of surveys conducted by the General Medical Council. RESULTS Average comment sentiment was positive but varied significantly during the study period. Fourteen topics of discussion were identified, each associated with a different pattern of sentiment. The topic with the highest proportion of negative comments was the role of a doctor (38%), and the topic with the most positive sentiment was hospital reviews (72%). CONCLUSION Some topics discussed in social media are comparable to those queried in traditional questionnaires, whereas other topics are distinctive and offer insight into what themes junior doctors care about. Events during the coronavirus pandemic may explain the sentiment trends in the junior doctor community. Natural language processing shows significant potential in generating insights into junior doctors' opinions and sentiment.
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Affiliation(s)
- Tamir Sirkis
- College of Medicine and Health, University of Exeter, Exeter, UK
| | - Stuart Maitland
- Translational Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
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Vishwakarma A, Chugh M. COVID-19 vaccination perception and outcome: society sentiment analysis on twitter data in India. SOCIAL NETWORK ANALYSIS AND MINING 2023; 13:84. [PMID: 37193096 PMCID: PMC10170045 DOI: 10.1007/s13278-023-01088-7] [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: 03/13/2023] [Revised: 04/24/2023] [Accepted: 04/24/2023] [Indexed: 05/18/2023]
Abstract
This study examines the perceptions and results of COVID-19 immunization using sentiment analysis of Twitter data from India. The tweets were collected from January 2021 to March 2023 using relevant hashtags and keywords. The dataset was pre-processed and cleaned before conducting sentiment analysis using Natural Language Processing techniques. Our results show that the overall sentiment toward COVID-19 vaccination in India has been positive, with a majority of tweets expressing support for vaccination and encouraging others to get vaccinated. However, we also identified some negative sentiments related to vaccine hesitancy, side effects, and mistrust in the government and pharmaceutical companies. We further analyzed the sentiment based on demographic factors such as gender, age, and location. The analysis revealed that the sentiment varied across different demographics, with some groups expressing more positive or negative sentiments than others. This study provides insights into the perception and outcomes of COVID-19 vaccination in India and highlights the need for targeted communication strategies to address vaccine hesitancy and increase vaccine uptake in specific demographics.
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Affiliation(s)
| | - Mitali Chugh
- UPES, Bidholi, Dehradun, Uttarakhand 248001 India
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Olusanya OA, Tomar A, Thomas J, Alonge K, Wigfall LT. Application of the theoretical domains framework to identify factors influencing catch-up HPV vaccinations among male college students in the United States: A review of evidence and recommendations. Vaccine 2023; 41:3564-3576. [PMID: 37164820 DOI: 10.1016/j.vaccine.2023.04.071] [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: 03/14/2022] [Revised: 03/13/2023] [Accepted: 04/26/2023] [Indexed: 05/12/2023]
Abstract
BACKGROUND Genital human papillomavirus (HPV) infection is the most prevalent sexually transmitted infection among young adults ages 15-25 years in the United States (US). Although HPV vaccines are recommended for individuals ages through 26 years, vaccine completion rates remain substantially low. METHODS Accordingly, our study utilized a comprehensive - Theoretical Domains Framework (TDF) of behavior change to systematically identify facilitators and barriers to catch-up HPV vaccinations. Five databases - Medline, Embase, CINAHL, ERIC, and PsycINFO were searched from January 2009 to July 2019 for empirical studies using quantitative and qualitative methods to assess HPV vaccine uptake among males ages 18-26 years within US college and university settings. The TDF analytic process included a content analysis using the mixed deductive-inductive approach to extract, analyze and categorize data into TDF domains/themes and sub-themes. RESULTS Overall, 17 studies were selected for data extraction. We identified eleven key TDF domains that influenced HPV vaccination behavior among college male students: 'knowledge' (82% of included studies), 'environmental context and resources' (53%), 'beliefs about consequences' (53%), 'unrealistic optimism' (50%) and 'pessimism' (6%), 'emotion' (50%), 'social influences' (50%), 'beliefs about capabilities' (41%), 'intention' (24%), 'reinforcement' (18%), 'social professional role and identity'(12%), and 'behavioral regulation' (12%). Barriers influencing HPV vaccine uptake included lack of knowledge and awareness regarding HPV infections, HPV vaccine safety, effectiveness, side effects, and costs; absence of health providers' recommendations; lack of healthcare and health insurance; low levels of perceived susceptibility and severity for HPV infections; HPV vaccine misinformation; as well as social stigma and peer influences regarding HPV vaccinations. Enablers for HPV vaccine uptake included high levels of perceived benefits for HPV vaccines. DISCUSSION Our study theoretically identified factors influencing HPV vaccinations. This could inform the efficient planning, support, and implementation of interventions that facilitate catch-up HPV vaccination practices among high-risk males within college/university settings.
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Affiliation(s)
- Olufunto A Olusanya
- University of Tennessee Health Science Center (UTHSC), UTHSC-Oak Ridge National Laboratory Center for Biomedical Informatics, Department of Pediatrics, Le Bonheur Research Center, 50 N Dunlap, Memphis, TN 38103, United States.
| | - Aditi Tomar
- Department of Health and Kinesiology, Texas A&M University, 107 Gilchrist Building (Reception Area), Mail Stop 4243, College Station, TX 77842-4243, United States.
| | - Jonathan Thomas
- Department of Public Health Studies, Texas A&M School of Public Health, 212 Adriance Lab Rd, College Station, TX 77843, United States.
| | - Kemi Alonge
- Marshfield Clinic Health System, Marshfield, WI 54449, United States.
| | - Lisa T Wigfall
- MD Anderson Cancer Center, Cancer Prevention Research Training Program, The University of Texas MD Anderson Cancer Center, 1150 Pressler Street, Cancer Prevention Research Building (CPB7.3556), Houston, TX 77030, United States.
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Anoop V, Sreelakshmi S. Public discourse and sentiment during Mpox outbreak: an analysis using natural language processing. Public Health 2023; 218:114-120. [PMID: 37019026 DOI: 10.1016/j.puhe.2023.02.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 02/01/2023] [Accepted: 02/21/2023] [Indexed: 04/05/2023]
Abstract
OBJECTIVES Mpox has been declared a Public Health Emergency of International Concern by the World Health Organization on July 23, 2022. Since early May 2022, Mpox has been continuously reported in several endemic countries with alarming death rates. This led to several discussions and deliberations on the Mpox virus among the general public through social media and platforms such as health forums. This study proposes natural language processing techniques such as topic modeling to unearth the general public's perspectives and sentiments on growing Mpox cases worldwide. STUDY DESIGN This was a detailed qualitative study using natural language processing on the user-generated comments from social media. METHODS A detailed analysis using topic modeling and sentiment analysis on Reddit comments (n = 289,073) that were posted between June 1 and August 5, 2022, was conducted. While the topic modeling was used to infer major themes related to the health emergency and user concerns, the sentiment analysis was conducted to see how the general public responded to different aspects of the outbreak. RESULTS The results revealed several interesting and useful themes, such as Mpox symptoms, Mpox transmission, international travel, government interventions, and homophobia from the user-generated contents. The results further confirm that there are many stigmas and fear of the unknown nature of the Mpox virus, which is prevalent in almost all topics and themes unearthed. CONCLUSIONS Analyzing public discourse and sentiments toward health emergencies and disease outbreaks is highly important. The insights that could be leveraged from the user-generated comments from public forums such as social media may be important for community health intervention programs and infodemiology researchers. The findings from this study effectively analyzed the public perceptions that may enable quantifying the effectiveness of measures imposed by governmental administrations. The themes unearthed may also benefit health policy researchers and decision-makers to make informed and data-driven decisions.
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Lindelöf G, Aledavood T, Keller B. Dynamics of Negative Discourse toward COVID-19 Vaccines: A Topic Modeling Study and an annotated dataset of Twitter Posts. J Med Internet Res 2023; 25:e41319. [PMID: 36877804 PMCID: PMC10134018 DOI: 10.2196/41319] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 02/27/2023] [Accepted: 02/28/2023] [Indexed: 03/04/2023] Open
Abstract
BACKGROUND Since the onset of the COVID-19 pandemic, vaccines have been an important topic in public discourse. The discussions around vaccines are polarized as some see them as an important measure to end the pandemic, and others are hesitant or find them harmful. A significant portion of these discussions takes place openly on social media platforms. This allows us to closely monitor the opinions of different groups and their changes over time. OBJECTIVE This study investigates posts related to COVID-19 vaccines on Twitter and focuses on those which have negative stances toward vaccines. We look into the evolution of the percentage of negative tweets over time. We also examine the different topics discussed in these tweets in order to understand the concerns and discussion points of those holding a negative stance toward the vaccines. METHODS A dataset of 16,713,238 English tweets related to COVID-19 vaccines was collected covering the period from March 1, 2020, to July 31, 2021. We used the Scikit-learn Python library to apply a support vector machine (SVM) classifier to identify the tweets with a negative stance toward COVID-19 vaccines. A total of 5,163 tweets were used to train the classifier, out of which a subset of 2,484 tweets were manually annotated by us and made publicly available along with this paper. We used the BERTtopic model to extract and investigate the topics discussed within the negative tweets and how they changed over time. RESULTS We show that the negativity with respect to COVID-19 vaccines has decreased over time along with the vaccine roll-outs. We identify 37 topics of discussion and present their respective importance over time. We show that popular topics consist of conspiratorial discussions such as 5G towers and microchips, but also contain legitimate concerns around vaccination safety and side effects as well as concerns about policies. The most prevalent topic among vaccine-hesitant tweets is related to the use of mRNA and fears about speculated negative effects on our DNA. CONCLUSIONS Hesitancy toward vaccines existed prior to COVID-19. However, given the dimension and circumstances surrounding the COVID-19 pandemic, some new areas of hesitancy and negativity toward the COVID-19 vaccines have arisen, for example, whether there has been enough time for them to be properly tested. There is also an unprecedented amount of conspiracy theories associated with them. Our study shows that even unpopular opinions or conspiracy theories can become widespread when paired with a widely popular discussion topic such as COVID-19 vaccines. Understanding the concerns and the discussed topics and how they change over time is essential for policymakers and public health authorities to provide better in-time information and policies, to facilitate vaccination of the population in future similar crises.
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Affiliation(s)
- Gabriel Lindelöf
- Department of Computer Science, Aalto University, P.O. Box 11000 (Otakaari 1B)FI-00076 AALTO, Espoo, FI.,Department of Management and Engineering, Linköping University, Linköping, SE
| | - Talayeh Aledavood
- Department of Computer Science, Aalto University, P.O. Box 11000 (Otakaari 1B)FI-00076 AALTO, Espoo, FI
| | - Barbara Keller
- Department of Computer Science, Aalto University, P.O. Box 11000 (Otakaari 1B)FI-00076 AALTO, Espoo, FI
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15
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Béres F, Michaletzky TV, Csoma R, Benczúr AA. Network embedding aided vaccine skepticism detection. APPLIED NETWORK SCIENCE 2023; 8:11. [PMID: 36811026 PMCID: PMC9933796 DOI: 10.1007/s41109-023-00534-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 01/16/2023] [Indexed: 06/18/2023]
Abstract
We investigate automatic methods to assess COVID vaccination views in Twitter content. Vaccine skepticism has been a controversial topic of long history that has become more important than ever with the COVID-19 pandemic. Our main goal is to demonstrate the importance of network effects in detecting vaccination skeptic content. Towards this end, we collected and manually labeled vaccination-related Twitter content in the first half of 2021. Our experiments confirm that the network carries information that can be exploited to improve the accuracy of classifying attitudes towards vaccination over content classification as baseline. We evaluate a variety of network embedding algorithms, which we combine with text embedding to obtain classifiers for vaccination skeptic content. In our experiments, by using Walklets, we improve the AUC of the best classifier with no network information by. We publicly release our labels, Tweet IDs and source codes on GitHub.
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Affiliation(s)
- Ferenc Béres
- ELKH Institute for Computer Science and Control (SZTAKI), Kende u. 13-17, Budapest, 1111 Hungary
| | | | - Rita Csoma
- Eötvös Loránd University, Pázmány Péter s. 1, Budapest, 1117 Hungary
| | - András A. Benczúr
- ELKH Institute for Computer Science and Control (SZTAKI), Kende u. 13-17, Budapest, 1111 Hungary
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16
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Yurtsever MME, Shiraz M, Ekinci E, Eken S. Comparing COVID-19 vaccine passports attitudes across countries by analysing Reddit comments. J Inf Sci 2023. [DOI: 10.1177/01655515221148356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Topic mining and sentiment polarity analysis together can adequately represent the topics and attitudes of users. The goal of this article is to use Reddit’s location-based subreddits to look at country-level differences in attitudes towards COVID-19 vaccine passports. We used sentiment analysis and latent topic modelling on textual data obtained from 18 Reddit communities concentrating on COVID-19 vaccine passports from 1 January 2021 to 28 February 2022 to study COVID-19 vaccine passports–related discussion on Reddit. To discover changes in sentiment and latent topics, 11,168 comments were aggregated and examined by month. The number of comments on postings from country-specific subreddits was positively proportional to the number of new COVID-19 cases reported each day. The more subjective expressions and positive/negative interpretations occurred after July 2021. Communities indicated more positive sentiment than negative sentiment towards vaccine passports–related topics, according to polarity analysis. Topic modelling found that community members were concerned about a variety of concerns related to their socioeconomic status. Throughout the topic modelling, keywords suggesting people’s privacy concerns and acceptance of various COVID-19 control methods were found. The use of public opinion and topic modelling to analyse vaccine passports could help with important global health informatics concerns associated with their socioeconomic status.
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Affiliation(s)
| | - Muhammad Shiraz
- Department of Information Systems Engineering, Kocaeli University, Turkey
| | - Ekin Ekinci
- Department of Computer Engineering, Sakarya University of Applied Sciences, Turkey
| | - Süleyman Eken
- Department of Information Systems Engineering, Kocaeli University, Turkey
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17
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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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18
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Danek S, Büttner M, Krois J, Schwendicke F. How Do Users Respond to Mass Vaccination Centers? A Cross-Sectional Study Using Natural Language Processing on Online Reviews to Explore User Experience and Satisfaction with COVID-19 Vaccination Centers. Vaccines (Basel) 2023; 11:vaccines11010144. [PMID: 36679989 PMCID: PMC9861127 DOI: 10.3390/vaccines11010144] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 12/21/2022] [Accepted: 12/23/2022] [Indexed: 01/11/2023] Open
Abstract
To reach large groups of vaccine recipients, several high-income countries introduced mass vaccination centers for COVID-19. Understanding user experiences of these novel structures can help optimize their design and increase patient satisfaction and vaccine uptake. This study drew on user online reviews of vaccination centers to assess user experience and identify its key determinants over time, by sentiment, and by interaction. Machine learning methods were used to analyze Google reviews of six COVID-19 mass vaccination centers in Berlin from December 2020 to December 2021. 3647 user online reviews were included in the analysis. Of these, 89% (3261/3647) were positive according to user rating (four to five of five stars). A total of 85% (2740/3647) of all reviews contained text. Topic modeling of the reviews containing text identified five optimally latent topics, and keyword extraction identified 47 salient keywords. The most important themes were organization, friendliness/responsiveness, and patient flow/wait time. Key interactions for users of vaccination centers included waiting, scheduling, transit, and the vaccination itself. Keywords connected to scheduling and efficiency, such as "appointment" and "wait", were most prominent in negative reviews. Over time, the average rating score decreased from 4.7 to 4.1, and waiting and duration became more salient keywords. Overall, mass vaccination centers appear to be positively perceived, yet users became more critical over the one-year period of the pandemic vaccination campaign observed. The study shows that online reviews can provide real-time insights into newly set-up infrastructures, and policymakers should consider their use to monitor the population's response over time.
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19
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Verma R, Chhabra A, Gupta A. A statistical analysis of tweets on covid-19 vaccine hesitancy utilizing opinion mining: an Indian perspective. SOCIAL NETWORK ANALYSIS AND MINING 2023; 13:12. [PMID: 36591558 PMCID: PMC9793353 DOI: 10.1007/s13278-022-01015-2] [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: 08/14/2022] [Revised: 12/02/2022] [Accepted: 12/05/2022] [Indexed: 12/28/2022]
Abstract
The world witnessed the emergence of a deadly virus in December 2019, later named COVID-19. The virus was found to be highly contagious, and so people across the world were highly prone to be affected by the virus. Being a virus-borne disease, developing a vaccine was one of the most promising remedies. Thus, research organizations across the globe started working on developing the vaccine. However, it was later found by many researchers that a large number of people were hesitant to receive the vaccine. This paper aims to study the acceptance and hesitancy levels of people in India and compares them with the acceptance and hesitancy levels of people from the UK, the USA, and the rest of the world by analyzing their tweets on Twitter. For this study, 2,98,452 tweets were fetched from January 2020 to March 2022 from Twitter, and 1,84,720 tweets from 1,22,960 unique users were selected based on their country of origin. Machine learning based Sentiment analysis is then used to evaluate and analyze the tweets. The paper also proposes an NLP-based algorithm to perform opinion mining on Twitter data. The study found the public sentiment of the Indian population to be 63% positive, 28% neutral, and 9% negative. While the worldwide sentiment distribution is 45% positive, 34% neutral, and 21% negative, the USA has 42% positive, 34% neutral, and 23% negative and the UK has 50% positive, 29% neutral, and 21% negative. Also, sentiment analysis for individual vaccines in Indian context resulted in "Covaxin" with the highest positive sentiment at 43% followed by "Covishield" at 36%. The outcome of this work yields an insight into the public perception of the COVID-19 vaccine and thus can be used to formulate policies for existing and future vaccine campaigns. This study becomes more relevant as it is the consolidated opinion of Indian people, which is versatile in nature.
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Affiliation(s)
- Ravi Verma
- Department of Computer Science and Engineering, Chandigarh College of Engineering and Technology (Degree Wing), Sector 26, Chandigarh, Chandigarh 160019 India
| | - Amit Chhabra
- Department of Computer Science and Engineering, Chandigarh College of Engineering and Technology (Degree Wing), Sector 26, Chandigarh, Chandigarh 160019 India
| | - Ankit Gupta
- Department of Computer Science and Engineering, Chandigarh College of Engineering and Technology (Degree Wing), Sector 26, Chandigarh, Chandigarh 160019 India
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20
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White BM, Melton C, Zareie P, Davis RL, Bednarczyk RA, Shaban-Nejad A. Exploring celebrity influence on public attitude towards the COVID-19 pandemic: social media shared sentiment analysis. BMJ Health Care Inform 2023; 30:e100665. [PMID: 36810135 PMCID: PMC9950585 DOI: 10.1136/bmjhci-2022-100665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 12/26/2022] [Indexed: 02/24/2023] Open
Abstract
OBJECTIVES The COVID-19 pandemic has introduced new opportunities for health communication, including an increase in the public's use of online outlets for health-related emotions. People have turned to social media networks to share sentiments related to the impacts of the COVID-19 pandemic. In this paper, we examine the role of social messaging shared by Persons in the Public Eye (ie, athletes, politicians, news personnel, etc) in determining overall public discourse direction. METHODS We harvested approximately 13 million tweets ranging from 1 January 2020 to 1 March 2022. The sentiment was calculated for each tweet using a fine-tuned DistilRoBERTa model, which was used to compare COVID-19 vaccine-related Twitter posts (tweets) that co-occurred with mentions of People in the Public Eye. RESULTS Our findings suggest the presence of consistent patterns of emotional content co-occurring with messaging shared by Persons in the Public Eye for the first 2 years of the COVID-19 pandemic influenced public opinion and largely stimulated online public discourse. DISCUSSION We demonstrate that as the pandemic progressed, public sentiment shared on social networks was shaped by risk perceptions, political ideologies and health-protective behaviours shared by Persons in the Public Eye, often in a negative light. CONCLUSION We argue that further analysis of public response to various emotions shared by Persons in the Public Eye could provide insight into the role of social media shared sentiment in disease prevention, control and containment for COVID-19 and in response to future disease outbreaks.
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Affiliation(s)
- Brianna M White
- College of Medicine, Department of Pediatrics, The University of Tennessee Health Science Center, Memphis, Tennessee, USA
| | - Chad Melton
- Bredesen Center for Interdisciplinary Research and Graduate Education, The University of Tennessee Knoxville, Knoxville, Tennessee, USA
| | - Parya Zareie
- College of Medicine, Department of Pediatrics, The University of Tennessee Health Science Center, Memphis, Tennessee, USA
| | - Robert L Davis
- College of Medicine, Department of Pediatrics, The University of Tennessee Health Science Center, Memphis, Tennessee, USA
| | - Robert A Bednarczyk
- Hubert Department of Global Health, Rollins School of Public Health, Atlanta, Georgia, USA
| | - Arash Shaban-Nejad
- College of Medicine, Department of Pediatrics, The University of Tennessee Health Science Center, Memphis, Tennessee, USA
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21
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Guerra A, Karakuş O. Sentiment analysis for measuring hope and fear from Reddit posts during the 2022 Russo-Ukrainian conflict. Front Artif Intell 2023; 6:1163577. [PMID: 37091300 PMCID: PMC10113549 DOI: 10.3389/frai.2023.1163577] [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: 02/10/2023] [Accepted: 03/07/2023] [Indexed: 04/25/2023] Open
Abstract
This article proposes a novel lexicon-based unsupervised sentiment analysis method to measure the "hope" and "fear" for the 2022 Ukrainian-Russian Conflict. Reddit.com is utilized as the main source of human reactions to daily events during nearly the first 3 months of the conflict. The top 50 "hot" posts of six different subreddits about Ukraine and news (Ukraine, worldnews, Ukraina, UkrainianConflict, UkraineWarVideoReport, and UkraineWarReports) along with their relative comments are scraped every day between 10th of May and 28th of July, and a novel data set is created. On this corpus, multiple analyzes, such as (1) public interest, (2) Hope/Fear score, and (3) stock price interaction, are employed. We use a dictionary approach, which scores the hopefulness of every submitted user post. The Latent Dirichlet Allocation (LDA) algorithm of topic modeling is also utilized to understand the main issues raised by users and what are the key talking points. Experimental analysis shows that the hope strongly decreases after the symbolic and strategic losses of Azovstal (Mariupol) and Severodonetsk. Spikes in hope/fear, both positives and negatives, are present not only after important battles, but also after some non-military events, such as Eurovision and football games.
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22
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de Carvalho VDH, Nepomuceno TCC, Poleto T, Costa APCS. The COVID-19 Infodemic on Twitter: A Space and Time Topic Analysis of the Brazilian Immunization Program and Public Trust. Trop Med Infect Dis 2022; 7:425. [PMID: 36548680 PMCID: PMC9783210 DOI: 10.3390/tropicalmed7120425] [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: 10/31/2022] [Revised: 11/24/2022] [Accepted: 12/03/2022] [Indexed: 12/14/2022] Open
Abstract
The context of the COVID-19 pandemic has brought to light the infodemic phenomenon and the problem of misinformation. Agencies involved in managing COVID-19 immunization programs are also looking for ways to combat this problem, demanding analytical tools specialized in identifying patterns of misinformation and understanding how they have evolved in time and space to demonstrate their effects on public trust. The aim of this article is to present the results of a study applying topic analysis in space and time with respect to public opinion on the Brazilian COVID-19 immunization program. The analytical process involves applying topic discovery to tweets with geoinformation extracted from the COVID-19 vaccination theme. After extracting the topics, they were submitted to manual annotation, whereby the polarity labels pro, anti, and neutral were applied based on the support and trust in the COVID-19 vaccination. A space and time analysis was carried out using the topic and polarity distributions, making it possible to understand moments during which the most significant quantities of posts occurred and the cities that generated the most tweets. The analytical process describes a framework capable of meeting the needs of agencies for tools, providing indications of how misinformation has evolved and where its dissemination focuses, in addition to defining the granularity of this information according to what managers define as adequate. The following research outcomes can be highlighted. (1) We identified a specific date containing a peak that stands out among the other dates, indicating an event that mobilized public opinion about COVID-19 vaccination. (2) We extracted 23 topics, enabling the manual polarity annotation of each topic and an understanding of which polarities were associated with tweets. (3) Based on the association between polarities, topics, and tweets, it was possible to identify the Brazilian cities that produced the majority of tweets for each polarity and the amount distribution of tweets relative to cities populations.
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Affiliation(s)
| | | | - Thiago Poleto
- Departamento de Administração, Instituto de Ciências Sociais Aplicadas, Universidade Federal do Pará, Belém 66075-110, Brazil
| | - Ana Paula Cabral Seixas Costa
- Departamento de Engenharia de Produção, Centro de Tecnologia e Geociências, Universidade Federal de Pernambuco, Recife 50740-550, Brazil
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23
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Ahmad W, Wang B, Martin P, Xu M, Xu H. Enhanced sentiment analysis regarding COVID-19 news from global channels. JOURNAL OF COMPUTATIONAL SOCIAL SCIENCE 2022; 6:19-57. [PMID: 36465148 PMCID: PMC9702932 DOI: 10.1007/s42001-022-00189-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 11/06/2022] [Indexed: 05/05/2023]
Abstract
For a healthy society to exist, it is crucial for the media to focus on disease-related issues so that more people are widely aware of them and reduce health risks. Recently, deep neural networks have become a popular tool for textual sentiment analysis, which can provide valuable insights and real-time monitoring and analysis regarding health issues. In this paper, as part of an effort to develop an effective model that can elicit public sentiment on COVID-19 news, we propose a novel approach Cov-Att-BiLSTM for sentiment analysis of COVID-19 news headlines using deep neural networks. We integrate attention mechanisms, embedding techniques, and semantic level data labeling into the prediction process to enhance the accuracy. To evaluate the proposed approach, we compared it to several deep and machine learning classifiers using various metrics of categorization efficiency and prediction quality, and the experimental results demonstrate its superiority with 0.931 testing accuracy. Furthermore, 73,138 pandemic-related tweets posted on six global channels were analyzed by the proposed approach, which accurately reflects global coverage of COVID-19 news and vaccination.
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Affiliation(s)
- Waseem Ahmad
- School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China
| | - Bang Wang
- School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China
| | - Philecia Martin
- School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China
| | - Minghua Xu
- School of Journalism and Information Communication, Huazhong University of Science and Technology, Wuhan, China
| | - Han Xu
- School of Journalism and Information Communication, Huazhong University of Science and Technology, Wuhan, China
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24
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Olusanya OA, White B, Malik F, Hester KA, Davis RL, Bednarczyk RA, Shaban-Nejad A. Healthcare professionals' perceptions and recommendations regarding adolescent vaccinations in Georgia and Tennessee during the COVID-19 pandemic: A qualitative research. PLoS One 2022; 17:e0277748. [PMID: 36399477 PMCID: PMC9674128 DOI: 10.1371/journal.pone.0277748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 11/02/2022] [Indexed: 11/19/2022] Open
Abstract
INTRODUCTION Despite its benefits, HPV vaccine uptake has been historically lower than other recommended adolescent vaccines in the United States (US). While hesitancy and misinformation have threatened vaccinations for many years, the adverse impacts from COVID-19 pandemic on preventive services have been far-reaching. OBJECTIVES To explore the perceptions and experiences of adolescent healthcare providers regarding routine vaccination services during the COVID-19 pandemic. METHODOLOGY Between December 2020 and May 2021, in-depth qualitative interviews were conducted via Zoom video conferencing among a purposively selected, diverse group of adolescent healthcare providers (n = 16) within 5 healthcare practices in the US southeastern states of Georgia and Tennessee. Audio recordings were transcribed verbatim and analyzed using a rapid qualitative analysis framework. Our analysis was guided by the grounded theory and inductive approach. RESULTS Participants reported that patient-provider communications; effective use of presumptive languaging; provider's continuing education/training; periodic reminders/recall messages; provider's personal conviction on vaccine safety/efficacy; early initiation of HPV vaccination series at 9 years; community partnerships with community health navigators/vaccine champions/vaccine advocates; use of standardized forms/prewritten scripts/standard operating protocols for patient-provider interactions; and vaccine promotion through social media, brochures/posters/pamphlets as well as outreaches to schools and churches served as facilitators to adolescent HPV vaccine uptake. Preventive adolescent services were adversely impacted by the COVID-19 pandemic at all practices. Participants highlighted an initial decrease in patients due to the pandemic, while some practices avoided the distribution of vaccine informational materials due to sanitary concerns. CONCLUSION As part of a larger study, we provided contextual information to refine an intervention package currently being developed to improve adolescent preventive care provision in healthcare practices. Our results could inform the implementation of comprehensive intervention strategies that improve HPV vaccination rates. Additionally, lessons learned (e.g. optimizing patient- provider interactions) could be adopted to expand COVID-19 vaccine acceptance on a sizable scale.
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Affiliation(s)
- Olufunto A. Olusanya
- Department of Pediatrics, Center for Biomedical Informatics, College of Medicine, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America
- * E-mail: (OAO); (AS-N)
| | - Brianna White
- Department of Pediatrics, Center for Biomedical Informatics, College of Medicine, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America
| | - Fauzia Malik
- Department of Health Policy and Management, Yale School of Public Health, Yale University, New Haven, Connecticut, United States of America
| | - Kyra A. Hester
- Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Robert L. Davis
- Department of Pediatrics, Center for Biomedical Informatics, College of Medicine, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America
| | - Robert A. Bednarczyk
- Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Arash Shaban-Nejad
- Department of Pediatrics, Center for Biomedical Informatics, College of Medicine, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America
- * E-mail: (OAO); (AS-N)
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25
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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. J Med Internet Res 2022; 24:e42261. [PMID: 36301673 PMCID: PMC9671489 DOI: 10.2196/42261] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [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
Abstract
BACKGROUND Since the first COVID-19 vaccine appeared, there has been a growing tendency to automatically determine public attitudes toward it. In particular, it was important to find the reasons for vaccine hesitancy, since it was directly correlated with pandemic protraction. Natural language processing (NLP) and public health researchers have turned to social media (eg, Twitter, Reddit, and Facebook) for user-created content from which they can gauge public opinion on vaccination. To automatically process such content, they use a number of NLP techniques, most notably topic modeling. Topic modeling enables the automatic uncovering and grouping of hidden topics in the text. When applied to content that expresses a negative sentiment toward vaccination, it can give direct insight into the reasons for vaccine hesitancy. OBJECTIVE This study applies NLP methods to classify vaccination-related tweets by sentiment polarity and uncover the reasons for vaccine hesitancy among the negative tweets in the Serbian language. METHODS To study the attitudes and beliefs behind vaccine hesitancy, we collected 2 batches of tweets that mention some aspects of COVID-19 vaccination. The first batch of 8817 tweets was manually annotated as either relevant or irrelevant regarding the COVID-19 vaccination sentiment, and then the relevant tweets were annotated as positive, negative, or neutral. We used the annotated tweets to train a sequential bidirectional encoder representations from transformers (BERT)-based classifier for 2 tweet classification tasks to augment this initial data set. The first classifier distinguished between relevant and irrelevant tweets. The second classifier used the relevant tweets and classified them as negative, positive, or neutral. This sequential classifier was used to annotate the second batch of tweets. The combined data sets resulted in 3286 tweets with a negative sentiment: 1770 (53.9%) from the manually annotated data set and 1516 (46.1%) as a result of automatic classification. Topic modeling methods (latent Dirichlet allocation [LDA] and nonnegative matrix factorization [NMF]) were applied using the 3286 preprocessed tweets to detect the reasons for vaccine hesitancy. RESULTS The relevance classifier achieved an F-score of 0.91 and 0.96 for relevant and irrelevant tweets, respectively. The sentiment polarity classifier achieved an F-score of 0.87, 0.85, and 0.85 for negative, neutral, and positive sentiments, respectively. By summarizing the topics obtained in both models, we extracted 5 main groups of reasons for vaccine hesitancy: concern over vaccine side effects, concern over vaccine effectiveness, concern over insufficiently tested vaccines, mistrust of authorities, and conspiracy theories. CONCLUSIONS This paper presents a combination of NLP methods applied to find the reasons for vaccine hesitancy in Serbia. Given these reasons, it is now possible to better understand the concerns of people regarding the vaccination process.
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Affiliation(s)
- Adela Ljajić
- The Institute for Artificial Intelligence Research and Development of Serbia, Novi Sad, Serbia
| | - Nikola Prodanović
- The Institute for Artificial Intelligence Research and Development of Serbia, Novi Sad, Serbia
| | - Darija Medvecki
- The Institute for Artificial Intelligence Research and Development of Serbia, Novi Sad, Serbia
| | - Bojana Bašaragin
- The Institute for Artificial Intelligence Research and Development of Serbia, Novi Sad, Serbia
| | - Jelena Mitrović
- The Institute for Artificial Intelligence Research and Development of Serbia, Novi Sad, Serbia
- Faculty of Computer Science and Mathematics, University of Passau, Passau, Germany
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Lee JY, Lim J, Choi JH, Lee BH. Can a wonder material be a popular item? A hype cycle of shifts in the sentiment of the interested public about graphene. TECHNOLOGY ANALYSIS & STRATEGIC MANAGEMENT 2022. [DOI: 10.1080/09537325.2022.2136068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
Affiliation(s)
- Ji Yeon Lee
- Department of Science and Technology Management Policy, University of Science and Technology, Daejeon, Korea
- NTIS Center, Korea Institute of Science and Technology Information, Daejeon, Korea
| | - Jeongsub Lim
- School of Media, Arts, and Science, Sogang University, Seoul, Korea
| | - Jae-Hak Choi
- Department of Polymer Science and Engineering, Chungnam National University, Daejeon, Korea
| | - Byeong-Hee Lee
- Department of Science and Technology Management Policy, University of Science and Technology, Daejeon, Korea
- NTIS Center, Korea Institute of Science and Technology Information, Daejeon, Korea
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27
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Melton CA, White BM, Davis RL, Bednarczyk RA, Shaban-Nejad A. Fine-tuned Sentiment Analysis of COVID-19 Vaccine-Related Social Media Data: Comparative Study. J Med Internet Res 2022; 24:e40408. [PMID: 36174192 PMCID: PMC9578521 DOI: 10.2196/40408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 08/18/2022] [Accepted: 09/15/2022] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND The emergence of the novel coronavirus (COVID-19) and the necessary separation of populations have led to an unprecedented number of new social media users seeking information related to the pandemic. Currently, with an estimated 4.5 billion users worldwide, social media data offer an opportunity for near real-time analysis of large bodies of text related to disease outbreaks and vaccination. These analyses can be used by officials to develop appropriate public health messaging, digital interventions, educational materials, and policies. OBJECTIVE Our study investigated and compared public sentiment related to COVID-19 vaccines expressed on 2 popular social media platforms-Reddit and Twitter-harvested from January 1, 2020, to March 1, 2022. METHODS To accomplish this task, we created a fine-tuned DistilRoBERTa model to predict the sentiments of approximately 9.5 million tweets and 70 thousand Reddit comments. To fine-tune our model, our team manually labeled the sentiment of 3600 tweets and then augmented our data set through back-translation. Text sentiment for each social media platform was then classified with our fine-tuned model using Python programming language and the Hugging Face sentiment analysis pipeline. RESULTS Our results determined that the average sentiment expressed on Twitter was more negative (5,215,830/9,518,270, 54.8%) than positive, and the sentiment expressed on Reddit was more positive (42,316/67,962, 62.3%) than negative. Although the average sentiment was found to vary between these social media platforms, both platforms displayed similar behavior related to the sentiment shared at key vaccine-related developments during the pandemic. CONCLUSIONS Considering this similar trend in shared sentiment demonstrated across social media platforms, Twitter and Reddit continue to be valuable data sources that public health officials can use to strengthen vaccine confidence and combat misinformation. As the spread of misinformation poses a range of psychological and psychosocial risks (anxiety and fear, etc), there is an urgency in understanding the public perspective and attitude toward shared falsities. Comprehensive educational delivery systems tailored to a population's expressed sentiments that facilitate digital literacy, health information-seeking behavior, and precision health promotion could aid in clarifying such misinformation.
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Affiliation(s)
- Chad A Melton
- Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee at Knoxville, Knoxville, TN, United States
- Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, University of Tennessee Health Science Center, Memphis, TN, United States
| | - Brianna M White
- Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, University of Tennessee Health Science Center, Memphis, TN, United States
| | - Robert L Davis
- Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, University of Tennessee Health Science Center, Memphis, TN, United States
| | - Robert A Bednarczyk
- Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA, United States
| | - Arash Shaban-Nejad
- Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, University of Tennessee Health Science Center, Memphis, TN, United States
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28
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Li M, Hua Y, Liao Y, Zhou L, Li X, Wang L, Yang J. Tracking the Impact of COVID-19 and Lockdown Policies on Public Mental Health Using Social Media: Infoveillance Study. J Med Internet Res 2022; 24:e39676. [PMID: 36191167 PMCID: PMC9566822 DOI: 10.2196/39676] [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: 05/18/2022] [Revised: 07/21/2022] [Accepted: 09/30/2022] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND The COVID-19 pandemic and its corresponding preventive and control measures have increased the mental burden on the public. Understanding and tracking changes in public mental status can facilitate optimizing public mental health intervention and control strategies. OBJECTIVE This study aimed to build a social media-based pipeline that tracks public mental changes and use it to understand public mental health status regarding the pandemic. METHODS This study used COVID-19-related tweets posted from February 2020 to April 2022. The tweets were downloaded using unique identifiers through the Twitter application programming interface. We created a lexicon of 4 mental health problems (depression, anxiety, insomnia, and addiction) to identify mental health-related tweets and developed a dictionary for identifying health care workers. We analyzed temporal and geographic distributions of public mental health status during the pandemic and further compared distributions among health care workers versus the general public, supplemented by topic modeling on their underlying foci. Finally, we used interrupted time series analysis to examine the statewide impact of a lockdown policy on public mental health in 12 states. RESULTS We extracted 4,213,005 tweets related to mental health and COVID-19 from 2,316,817 users. Of these tweets, 2,161,357 (51.3%) were related to "depression," whereas 1,923,635 (45.66%), 225,205 (5.35%), and 150,006 (3.56%) were related to "anxiety," "insomnia," and "addiction," respectively. Compared to the general public, health care workers had higher risks of all 4 types of problems (all P<.001), and they were more concerned about clinical topics than everyday issues (eg, "students' pressure," "panic buying," and "fuel problems") than the general public. Finally, the lockdown policy had significant associations with public mental health in 4 out of the 12 states we studied, among which Pennsylvania showed a positive association, whereas Michigan, North Carolina, and Ohio showed the opposite (all P<.05). CONCLUSIONS The impact of COVID-19 and the corresponding control measures on the public's mental status is dynamic and shows variability among different cohorts regarding disease types, occupations, and regional groups. Health agencies and policy makers should primarily focus on depression (reported by 51.3% of the tweets) and insomnia (which has had an ever-increasing trend since the beginning of the pandemic), especially among health care workers. Our pipeline timely tracks and analyzes public mental health changes, especially when primary studies and large-scale surveys are difficult to conduct.
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Affiliation(s)
- Minghui Li
- Department of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, China
| | - Yining Hua
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
- Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA, United States
| | - Yanhui Liao
- Department of Psychiatry, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Li Zhou
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
- Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA, United States
| | - Xue Li
- Department of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, China
| | - Ling Wang
- Florence Nightingale Faculty of Nursing, Midwifery & Palliative Care, King's College London, London, United Kingdom
| | - Jie Yang
- Department of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, China
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Mostafa MM. A one-hundred-year structural topic modeling analysis of the knowledge structure of international management research. QUALITY & QUANTITY 2022; 57:1-31. [PMID: 36249708 PMCID: PMC9549032 DOI: 10.1007/s11135-022-01548-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Accepted: 09/26/2022] [Indexed: 12/02/2022]
Abstract
International Management is a vast and multidisciplinary research domain that is heavily influenced by several other disciplines, such as Economics, Organizational Theory and Strategic Management. Based on 28,973 research articles, this study aims to analyze the knowledge structure of the international management domain from 1920 to 2019. Using computational text-based topic modeling analysis, we trace the evolution of international management knowledge by examining the major academic topics/latent themes discussed in the field. The study also diachronically visualizes the variations in topic prevalence over time. Our methodology is akin to "inductive mapping" as it is neither biased by our position nor it is guided by assumptions related to the topics we expect to find. Results indicate the existence of a wide variety of important research foci in the domain of international management. These include, among others, strategic alliances formation, international entry modes, corporate social responsibility, cross-cultural consumer behavior, technological innovation and entrepreneurship. Results also show that some topics such as "financial risk and return on investment" and "corporate social responsibility" show a declining time trend, indicating that academic research focusing on such topics was more likely to be published early on and less so recently. On the other hand, other topics such as "Emerging (East) Asian nations" and "global mergers and acquisitions" show an increasing trend, indicating that more papers were published recently. Taken together, although our findings might reflect the breadth and depth of research in international management, they might also suggest that the bounds of this field are not well defined.
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Baxi MK, Philip J, Mago V. Resilience of political leaders and healthcare organizations during COVID-19. PeerJ Comput Sci 2022; 8:e1121. [PMID: 36262139 PMCID: PMC9575867 DOI: 10.7717/peerj-cs.1121] [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: 05/03/2022] [Accepted: 09/07/2022] [Indexed: 06/16/2023]
Abstract
This study assesses the online societal association of leaders and healthcare organizations from the top-10 COVID-19 resilient nations through public engagement, sentiment strength, and inclusivity and diversity strength. After analyzing 173,071 Tweets authored by the leaders and health organizations, our findings indicate that United Arab Emirate's Prime Minister had the highest online societal association (normalized online societal association: 1.000) followed by the leaders of Canada and Turkey (normalized online societal association: 0.068 and 0.033, respectively); and among the healthcare organizations, the Public Health Agency of Canada was the most impactful (normalized online societal association: 1.000) followed by the healthcare agencies of Turkey and Spain (normalized online societal association: 0.632 and 0.094 respectively). In comparison to healthcare organizations, the leaders displayed a strong awareness of individual factors and generalized their Tweets to a broader audience. The findings also suggest that users prefer accessing social media platforms for information during health emergencies and that leaders and healthcare institutions should realize the potential to use them effectively.
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Affiliation(s)
- Manmeet Kaur Baxi
- Department of Computer Science, Lakehead University, Thunder Bay, Ontario, Canada
| | - Joshua Philip
- Superior Collegiate and Vocational Institute, Thunder Bay, Ontario, Canada
| | - Vijay Mago
- Department of Computer Science, Lakehead University, Thunder Bay, Ontario, Canada
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31
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Mining Public Opinions on COVID-19 Vaccination: A Temporal Analysis to Support Combating Misinformation. Trop Med Infect Dis 2022; 7:tropicalmed7100256. [DOI: 10.3390/tropicalmed7100256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Revised: 09/17/2022] [Accepted: 09/19/2022] [Indexed: 11/16/2022] Open
Abstract
This article presents a study that applied opinion analysis about COVID-19 immunization in Brazil. An initial set of 143,615 tweets was collected containing 49,477 pro- and 44,643 anti-vaccination and 49,495 neutral posts. Supervised classifiers (multinomial naïve Bayes, logistic regression, linear support vector machines, random forests, adaptative boosting, and multilayer perceptron) were tested, and multinomial naïve Bayes, which had the best trade-off between overfitting and correctness, was selected to classify a second set containing 221,884 unclassified tweets. A timeline with the classified tweets was constructed, helping to identify dates with peaks in each polarity and search for events that may have caused the peaks, providing methodological assistance in combating sources of misinformation linked to the spread of anti-vaccination opinion.
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32
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Xia L, Chen B, Hunt K, Zhuang J, Song C. Food Safety Awareness and Opinions in China: A Social Network Analysis Approach. Foods 2022; 11:foods11182909. [PMID: 36141035 PMCID: PMC9498558 DOI: 10.3390/foods11182909] [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: 08/14/2022] [Revised: 09/13/2022] [Accepted: 09/15/2022] [Indexed: 11/16/2022] Open
Abstract
Over recent years, food safety has garnered widespread attention and concern from society. Concurrently, social media sites and online forums have become popular platforms to disseminate news, share opinions, and connect with one’s social network. In this research, we focus on the intersection of food safety and online social networking by utilizing natural language processing techniques and social network analysis to study public opinions related to food safety. Using real data collected from a popular Chinese question-and-answer platform, we first identify hot topics related to food safety, and then analyze the emotional state of users in each community (i.e., users communicating about the same topic) to understand the public’s sentiment related to different food safety topics. We proceed by forming semantic networks to analyze the characteristics of food safety opinion networks. Our results show that Internet users form modular communities, each with differences in topics of concern and emotional states of community users. Users focus on a wide range of topics, showing that overall, food safety awareness is increasing. This paper provides novel insights that can help interested stakeholders monitor the discussions and opinions related to food safety.
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Affiliation(s)
- Lei Xia
- State-Owned Assets Management Department, Nanjing Forestry University, Nanjing 210037, China
| | - Bo Chen
- School of Economics and Management, China University of Petroleum, Beijing 102200, China
| | - Kyle Hunt
- Department of Management Science and Systems, University at Buffalo, Buffalo, NY 14260, USA
- Department of Industrial and Systems Engineering, University at Buffalo, Buffalo, NY 14260, USA
| | - Jun Zhuang
- Department of Industrial and Systems Engineering, University at Buffalo, Buffalo, NY 14260, USA
| | - Cen Song
- School of Economics and Management, China University of Petroleum, Beijing 102200, China
- Correspondence:
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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
| | - 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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Karas B, Qu S, Xu Y, Zhu Q. Experiments with LDA and Top2Vec for embedded topic discovery on social media data—A case study of cystic fibrosis. Front Artif Intell 2022; 5:948313. [PMID: 36062265 PMCID: PMC9433987 DOI: 10.3389/frai.2022.948313] [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: 05/19/2022] [Accepted: 07/25/2022] [Indexed: 11/18/2022] Open
Abstract
Social media has become an important resource for discussing, sharing, and seeking information pertinent to rare diseases by patients and their families, given the low prevalence in the extraordinarily sparse populations. In our previous study, we identified prevalent topics from Reddit via topic modeling for cystic fibrosis (CF). While we were able to derive/access concerns/needs/questions of patients with CF, we observed challenges and issues with the traditional techniques of topic modeling, e.g., Latent Dirichlet Allocation (LDA), for fulfilling the task of topic extraction. Thus, here we present our experiments to extend the previous study with an aim of improving the performance of topic modeling, by experimenting with LDA model optimization and examination of the Top2Vec model with different embedding models. With the demonstrated results with higher coherence and qualitatively higher human readability of derived topics, we implemented the Top2Vec model with doc2vec as the embedding model as our final model to extract topics from a subreddit of CF (“r/CysticFibrosis”) and proposed to expand its use with other types of social media data for other rare diseases for better assessing patients' needs with social media data.
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Affiliation(s)
- Bradley Karas
- Division of Rare Diseases Research Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Bethesda, MD, United States
| | - Sue Qu
- Division of Rare Diseases Research Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Bethesda, MD, United States
| | - Yanji Xu
- Division of Rare Diseases Research Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Bethesda, MD, United States
- *Correspondence: Yanji Xu
| | - Qian Zhu
- Division of Pre-Clinical Innovation, National Center for Advancing Translational Sciences, (NCATS), National Institutes of Health (NIH), Rockville, MD, United States
- Qian Zhu
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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: 78] [Impact Index Per Article: 39.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [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, Halden, Norway
- Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Anders Nordahl-Hansen
- Department of Education, ICT, and Learning, Østfold University College, Halden, Norway
| | - Daniel S Quintana
- Department of Psychology, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
- Norwegian Centre for Mental Disorders Research (NORMENT), University of Oslo, Oslo, Norway
- NevSom, Department of Rare Disorders & Disabilities, Oslo University Hospital, Oslo, Norway
| | - Rolf Wynn
- Department of Clinical Medicine, The Artic University of Norway, Tromsø, Norway
- Division of Mental Health and Substance Use, University Hospital of North Norway, Tromsø, Norway
| | - Elia Gabarron
- Department of Education, ICT, and Learning, Østfold University College, Halden, Norway
- Norwegian Centre for E-health Research, University Hospital of North Norway, Tromsø, Norway
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He L, Yin T, Zheng K. They May not Work! An Evaluation of Eleven Sentiment Analysis Tools on Seven Social Media Datasets. J Biomed Inform 2022; 132:104142. [PMID: 35835437 DOI: 10.1016/j.jbi.2022.104142] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 07/05/2022] [Accepted: 07/07/2022] [Indexed: 11/29/2022]
Abstract
OBJECTIVE Sentiment analysis is an important method for understanding emotions and opinions expressed through social media exchanges. Little work has been done to evaluate the performance of existing sentiment analysis tools on social media datasets, particularly those related to health, healthcare, or public health. This study aims to address the gap. MATERIAL AND METHODS We evaluated 11 commonly used sentiment analysis tools on five health-related social media datasets curated in previously published studies. These datasets include Human Papillomavirus Vaccine, Health Care Reform, COVID-19 Masking, Vitals.com Physician Reviews, and the Breast Cancer Forum from MedHelp.org. For comparison, we also analyzed two non-health datasets based on movie reviews and generic tweets. We conducted a qualitative error analysis on the social media posts that were incorrectly classified by all tools. RESULTS The existing sentiment analysis tools performed poorly with an average weighted F1 score below 0.6. The inter-tool agreement was also low; the average Fleiss Kappa score is 0.066. The qualitative error analysis identified two major causes for misclassification: (1) correct sentiment but on wrong subject(s) and (2) failure to properly interpret inexplicit/indirect sentiment expressions. DISCUSSION and Conclusion: The performance of the existing sentiment analysis tools is insufficient to generate accurate sentiment classification results. The low inter-tool agreement suggests that the conclusion of a study could be entirely driven by the idiosyncrasies of the tool selected, rather than by the data. This is very concerning especially if the results may be used to inform important policy decisions such as mask or vaccination mandates.
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Affiliation(s)
- Lu He
- Department of Informatics, Donald Bren School of Information and Computer Science, University of California, Irvine, Irvine, California, United States
| | - Tingjue Yin
- Department of Informatics, Donald Bren School of Information and Computer Science, University of California, Irvine, Irvine, California, United States
| | - Kai Zheng
- Department of Informatics, Donald Bren School of Information and Computer Science, University of California, Irvine, Irvine, California, United States; Department of Emergency Medicine, School of Medicine, University of California, Irvine, Irvine, California, United States.
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Yin JDC. Media Data and Vaccine Hesitancy: Scoping Review. JMIR INFODEMIOLOGY 2022; 2:e37300. [PMID: 37113443 PMCID: PMC9987198 DOI: 10.2196/37300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 06/16/2022] [Accepted: 07/14/2022] [Indexed: 04/29/2023]
Abstract
Background Media studies are important for vaccine hesitancy research, as they analyze how the media shapes risk perceptions and vaccine uptake. Despite the growth in studies in this field owing to advances in computing and language processing and an expanding social media landscape, no study has consolidated the methodological approaches used to study vaccine hesitancy. Synthesizing this information can better structure and set a precedent for this growing subfield of digital epidemiology. Objective This review aimed to identify and illustrate the media platforms and methods used to study vaccine hesitancy and how they build or contribute to the study of the media's influence on vaccine hesitancy and public health. Methods This study followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. A search was conducted on PubMed and Scopus for any studies that used media data (social media or traditional media), had an outcome related to vaccine sentiment (opinion, uptake, hesitancy, acceptance, or stance), were written in English, and were published after 2010. Studies were screened by only 1 reviewer and extracted for media platform, analysis method, the theoretical models used, and outcomes. Results In total, 125 studies were included, of which 71 (56.8%) used traditional research methods and 54 (43.2%) used computational methods. Of the traditional methods, most used content analysis (43/71, 61%) and sentiment analysis (21/71, 30%) to analyze the texts. The most common platforms were newspapers, print media, and web-based news. The computational methods mostly used sentiment analysis (31/54, 57%), topic modeling (18/54, 33%), and network analysis (17/54, 31%). Fewer studies used projections (2/54, 4%) and feature extraction (1/54, 2%). The most common platforms were Twitter and Facebook. Theoretically, most studies were weak. The following five major categories of studies arose: antivaccination themes centered on the distrust of institutions, civil liberties, misinformation, conspiracy theories, and vaccine-specific concerns; provaccination themes centered on ensuring vaccine safety using scientific literature; framing being important and health professionals and personal stories having the largest impact on shaping vaccine opinion; the coverage of vaccination-related data mostly identifying negative vaccine content and revealing deeply fractured vaccine communities and echo chambers; and the public reacting to and focusing on certain signals-in particular cases, deaths, and scandals-which suggests a more volatile period for the spread of information. Conclusions The heterogeneity in the use of media to study vaccines can be better consolidated through theoretical grounding. Areas of suggested research include understanding how trust in institutions is associated with vaccine uptake, how misinformation and information signaling influence vaccine uptake, and the evaluation of government communications on vaccine rollouts and vaccine-related events. The review ends with a statement that media data analyses, though groundbreaking in approach, should supplement-not supplant-current practices in public health research.
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Affiliation(s)
- Jason Dean-Chen Yin
- School of Public Health Li Ka Shing Faculty of Medicine The University of Hong Kong Hong Kong China (Hong Kong)
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Olusanya OA, White B, Melton CA, Shaban-Nejad A. Examining the Implementation of Digital Health to Strengthen the COVID-19 Pandemic Response and Recovery and Scale up Equitable Vaccine Access in African Countries. ARXIV 2022:arXiv:2206.03286v1. [PMID: 35677423 PMCID: PMC9176651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
The COVID-19 pandemic has profoundly impacted the world, having taken the lives of over 6 million individuals. Accordingly, this pandemic has caused a shift in conversations surrounding the burden of diseases worldwide, welcoming insights from multidisciplinary fields including digital health and artificial intelligence. Africa faces a heavy disease burden that exacerbates the current COVID-19 pandemic and limits the scope of public health preparedness, response, containment, and case management. Herein, we examined the potential impact of transformative digital health technologies in mitigating the global health crisis with reference to African countries. Furthermore, we proposed recommendations for scaling up digital health technologies and artificial intelligence-based platforms to tackle the transmission of the SARS-CoV-2 and enable equitable vaccine access. Challenges related to the pandemic are numerous. Rapid response and management strategies-that is, contract tracing, case surveillance, diagnostic testing intensity, and most recently vaccine distribution mapping-can overwhelm the health care delivery system that is fragile. Although challenges are vast, digital health technologies can play an essential role in achieving sustainable resilient recovery and building back better. It is plausible that African nations are better equipped to rapidly identify, diagnose, and manage infected individuals for COVID-19, other diseases, future outbreaks, and pandemics.
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Affiliation(s)
- Olufunto A Olusanya
- Department of Pediatrics, The University of Tennessee Health Science Center, Memphis, TN, United States
| | - Brianna White
- Department of Pediatrics, The University of Tennessee Health Science Center, Memphis, TN, United States
| | - Chad A Melton
- Bredesen Center for Interdisciplinary Research and Graduate Education, The University of Tennessee, Knoxville, TN, United States
| | - Arash Shaban-Nejad
- Department of Pediatrics, The University of Tennessee Health Science Center, Memphis, TN, United States
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Olusanya OA, White B, Melton CA, Shaban-Nejad A. Examining the Implementation of Digital Health to Strengthen the COVID-19 Pandemic Response and Recovery and Scale up Equitable Vaccine Access in African Countries. JMIR Form Res 2022; 6:e34363. [PMID: 35512271 PMCID: PMC9116456 DOI: 10.2196/34363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 03/08/2022] [Accepted: 04/21/2022] [Indexed: 12/01/2022] Open
Abstract
The COVID-19 pandemic has profoundly impacted the world, having taken the lives of over 6 million individuals. Accordingly, this pandemic has caused a shift in conversations surrounding the burden of diseases worldwide, welcoming insights from multidisciplinary fields including digital health and artificial intelligence. Africa faces a heavy disease burden that exacerbates the current COVID-19 pandemic and limits the scope of public health preparedness, response, containment, and case management. Herein, we examined the potential impact of transformative digital health technologies in mitigating the global health crisis with reference to African countries. Furthermore, we proposed recommendations for scaling up digital health technologies and artificial intelligence-based platforms to tackle the transmission of the SARS-CoV-2 and enable equitable vaccine access. Challenges related to the pandemic are numerous. Rapid response and management strategies-that is, contract tracing, case surveillance, diagnostic testing intensity, and most recently vaccine distribution mapping-can overwhelm the health care delivery system that is fragile. Although challenges are vast, digital health technologies can play an essential role in achieving sustainable resilient recovery and building back better. It is plausible that African nations are better equipped to rapidly identify, diagnose, and manage infected individuals for COVID-19, other diseases, future outbreaks, and pandemics.
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Affiliation(s)
- Olufunto A Olusanya
- Department of Pediatrics, The University of Tennessee Health Science Center, Memphis, TN, United States
| | - Brianna White
- Department of Pediatrics, The University of Tennessee Health Science Center, Memphis, TN, United States
| | - Chad A Melton
- Bredesen Center for Interdisciplinary Research and Graduate Education, The University of Tennessee, Knoxville, TN, United States
| | - Arash Shaban-Nejad
- Department of Pediatrics, The University of Tennessee Health Science Center, Memphis, TN, United States
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40
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Could Social Bots’ Sentiment Engagement Shape Humans’ Sentiment on COVID-19 Vaccine Discussion on Twitter? SUSTAINABILITY 2022. [DOI: 10.3390/su14095566] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
During the COVID-19 pandemic, social media has become an emerging platform for the public to find information, share opinions, and seek coping strategies. Vaccination, one of the most effective public health interventions to control the COVID-19 pandemic, has become the focus of public online discussions. Several studies have demonstrated that social bots actively involved in topic discussions on social media and expressed their sentiments and emotions, which affected human users. However, it is unclear whether social bots’ sentiments affect human users’ sentiments of COVID-19 vaccines. This study seeks to scrutinize whether the sentiments of social bots affect human users’ sentiments of COVID-19 vaccines. The work identified social bots and built an innovative computational framework, i.e., the BERT-CNN sentiment analysis framework, to classify tweet sentiments at the three most discussed stages of COVID-19 vaccines on Twitter from December 2020 to August 2021, thus exploring the impacts of social bots on online vaccine sentiments of humans. Then, the Granger causality test was used to analyze whether there was a time-series causality between the sentiments of social bots and humans. The findings revealed that social bots can influence human sentiments about COVID-19 vaccines. Their ability to transmit the sentiments on social media, whether in the spread of positive or negative tweets, will have a corresponding impact on human sentiments.
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Lotto M, Santana Jorge O, Sá Menezes T, Ramalho AM, Marchini Oliveira T, Bevilacqua F, Cruvinel T. Psychophysiological reactions of Internet users exposed to fluoride information and disinformation: Protocol for a randomized controlled trial (Preprint). JMIR Res Protoc 2022; 11:e39133. [PMID: 35708767 PMCID: PMC9247811 DOI: 10.2196/39133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 05/11/2022] [Accepted: 05/18/2022] [Indexed: 11/13/2022] Open
Abstract
Background False messages on the internet continually propagate possible adverse effects of fluoridated oral care products and water, despite their essential role in preventing and controlling dental caries. Objective This study aims to evaluate the patterns of psychophysiological reactions of adults after the consumption of internet-based fluoride-related information and disinformation. Methods A 2-armed, single-blinded, parallel, and randomized controlled trial will be conducted with 58 parents or caregivers of children who attend the Clinics of Pediatric Dentistry at the Bauru School of Dentistry, considering an attrition of 10% and a significance level of 5%. The participants will be randomized into test and intervention groups, being respectively exposed to fluoride-related information and disinformation presented on a computer with simultaneous monitoring of their psychophysiological reactions, including analysis of their heart rates (HRs) and 7 facial features (mouth outer, mouth corner, eye area, eyebrow activity, face area, face motion, and facial center of mass). Then, participants will respond to questions about the utility and truthfulness of content, their emotional state after the experiment, eHealth literacy, oral health knowledge, and socioeconomic characteristics. The Shapiro-Wilk and Levene tests will be used to determine the normality and homogeneity of the data, which could lead to further statistical analyses for elucidating significant differences between groups, using parametric (Student t test) or nonparametric (Mann-Whitney U test) analyses. Moreover, multiple logistic regression models will be developed to evaluate the association of distinct variables with the psychophysiological aspects. Only factors with significant Wald statistics in the simple analysis will be included in the multiple models (P<.2). Furthermore, receiver operating characteristic curve analysis will be performed to determine the accuracy of the remote HR with respect to the measured HR. For all analyses, P<.05 will be considered significant. Results From June 2022, parents and caregivers who frequent the Clinics of Pediatric Dentistry at the Bauru School of Dentistry will be invited to participate in the study and will be randomized into 1 of the 2 groups (control or intervention). Data collection is expected to be completed in December 2023. Subsequently, the authors will analyze the data and publish the findings of the clinical trial by June 2024. Conclusions This randomized controlled trial aims to elucidate differences between psychophysiological patterns of adults exposed to true or false oral health content. This evidence may support the development of further studies and digital strategies, such as neural network models to automatically detect disinformation available on the internet. Trial Registration Brazilian Clinical Trials Registry (RBR-7q4ymr2) U1111-1263-8227; https://tinyurl.com/2kf73t3d International Registered Report Identifier (IRRID) PRR1-10.2196/39133
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Affiliation(s)
- Matheus Lotto
- Department of Pediatric Dentistry, Orthodontics and Public Health, Bauru School of Dentistry, University of São Paulo, Bauru, Brazil
| | - Olivia Santana Jorge
- Department of Pediatric Dentistry, Orthodontics and Public Health, Bauru School of Dentistry, University of São Paulo, Bauru, Brazil
| | - Tamires Sá Menezes
- Department of Pediatric Dentistry, Orthodontics and Public Health, Bauru School of Dentistry, University of São Paulo, Bauru, Brazil
| | - Ana Maria Ramalho
- Department of Pediatric Dentistry, Orthodontics and Public Health, Bauru School of Dentistry, University of São Paulo, Bauru, Brazil
| | - Thais Marchini Oliveira
- Department of Pediatric Dentistry, Orthodontics and Public Health, Bauru School of Dentistry, University of São Paulo, Bauru, Brazil
| | - Fernando Bevilacqua
- Department of Computer Science, Federal University of Fronteira Sul, Chapecó, Brazil
| | - Thiago Cruvinel
- Department of Pediatric Dentistry, Orthodontics and Public Health, Bauru School of Dentistry, University of São Paulo, Bauru, Brazil
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Kumar N, Corpus I, Hans M, Harle N, Yang N, McDonald C, Sakai SN, Janmohamed K, Chen K, Altice FL, Tang W, Schwartz JL, Jones-Jang SM, Saha K, Memon SA, Bauch CT, Choudhury MD, Papakyriakopoulos O, Tucker JD, Goyal A, Tyagi A, Khoshnood K, Omer S. COVID-19 vaccine perceptions in the initial phases of US vaccine roll-out: an observational study on reddit. BMC Public Health 2022; 22:446. [PMID: 35255881 PMCID: PMC8899002 DOI: 10.1186/s12889-022-12824-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 02/21/2022] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND Open online forums like Reddit provide an opportunity to quantitatively examine COVID-19 vaccine perceptions early in the vaccine timeline. We examine COVID-19 misinformation on Reddit following vaccine scientific announcements, in the initial phases of the vaccine timeline. METHODS We collected all posts on Reddit (reddit.com) from January 1 2020 - December 14 2020 (n=266,840) that contained both COVID-19 and vaccine-related keywords. We used topic modeling to understand changes in word prevalence within topics after the release of vaccine trial data. Social network analysis was also conducted to determine the relationship between Reddit communities (subreddits) that shared COVID-19 vaccine posts, and the movement of posts between subreddits. RESULTS There was an association between a Pfizer press release reporting 90% efficacy and increased discussion on vaccine misinformation. We observed an association between Johnson and Johnson temporarily halting its vaccine trials and reduced misinformation. We found that information skeptical of vaccination was first posted in a subreddit (r/Coronavirus) which favored accurate information and then reposted in subreddits associated with antivaccine beliefs and conspiracy theories (e.g. conspiracy, NoNewNormal). CONCLUSIONS Our findings can inform the development of interventions where individuals determine the accuracy of vaccine information, and communications campaigns to improve COVID-19 vaccine perceptions, early in the vaccine timeline. Such efforts can increase individual- and population-level awareness of accurate and scientifically sound information regarding vaccines and thereby improve attitudes about vaccines, especially in the early phases of vaccine roll-out. Further research is needed to understand how social media can contribute to COVID-19 vaccination services.
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Affiliation(s)
- Navin Kumar
- Section of Infectious Diseases, Yale School of Medicine, New Haven, CT USA
| | | | | | | | - Nan Yang
- Department of Biostatistics, Yale School of Public Health, New Haven, CT USA
| | - Curtis McDonald
- Department of Statistics, Yale University, New Haven, CT USA
| | | | | | - Keyu Chen
- Section of Infectious Diseases, Yale School of Medicine, New Haven, CT USA
| | - Frederick L. Altice
- Section of Infectious Diseases, Yale School of Medicine, New Haven, CT USA
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT USA
| | - Weiming Tang
- University of North Carolina Project-China, Guangzhou, China
- Social Entrepreneurship to Spur Health (SESH) Global, Guangzhou, China
- University of North Carolina at Chapel Hill, Chapel Hill, NC USA
| | - Jason L. Schwartz
- Department of Health Policy and Management, Yale School of Public Health, New Haven, CT USA
| | | | | | | | - Chris T. Bauch
- Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario Canada
| | | | | | - Joseph D. Tucker
- University of North Carolina Project-China, Guangzhou, China
- School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
- Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, USA
| | - Abhay Goyal
- Department of Computer Science, Stony Brook University, New York, NY USA
| | - Aman Tyagi
- Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, PA USA
| | - Kaveh Khoshnood
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT USA
| | - Saad Omer
- Yale Institute for Global Health, New Haven, CT USA
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Yang Y, Zhang Y, Zhang X, Cao Y, Zhang J. Spatial evolution patterns of public panic on Chinese social networks amidst the COVID-19 pandemic. INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION : IJDRR 2022; 70:102762. [PMID: 35004139 PMCID: PMC8721919 DOI: 10.1016/j.ijdrr.2021.102762] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 12/03/2021] [Accepted: 12/28/2021] [Indexed: 05/25/2023]
Abstract
Novel coronavirus pneumonia has had a significant impact on people's lives and psychological health. We developed a stage model to analyse the spatial and temporal distribution of public panic during the two waves of the coronavirus disease 2019 (COVID-19) pandemic. We used tweets with geographic location data from the popular hashtag 'Lockdown Diary' recorded from 23 January to April 8, 2020, and 'Nanjing Outbreak' recorded from 21 July to 1 September 2021 on Weibo. Combining the lexicon-based sentiment analysis and the grounded theory approach, this panic model could explain people's panic and behavioural responses in different areas at different stages of the pandemic. Next, we used the latent Dirichlet allocation topic model to reconfirm the panic model. The results showed that public sentiments fluctuated strongly in the early stages; in this case, panic and prayers were the dominant sentiments. In terms of spatial distribution, public panic showed hierarchical and neighbourhood diffusion, with highly assertive expressions of sentiment at the outbreak sites, economically developed areas, and areas surrounding the outbreak. Most importantly, we considered that public panic was affected by the 17 specific topics extracted based on the perceived and actual distance of the pandemic, thus stimulating the process of panic from minimal, acute, and mild panic to perceived rationality. Consequently, the public's behavioural responses shifted from delayed, negative, and positive, to rational behavioural responses. This study presents a novel approach to explore public panic from both a time and space perspective and provides some suggestions in response to future pandemics.
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Affiliation(s)
- Yixin Yang
- School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing, 210023, China
| | - Yingying Zhang
- School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing, 210023, China
| | - Xiaowan Zhang
- School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing, 210023, China
- School of Business, Anhui University, Hefei, 230039, China
| | - Yihan Cao
- School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing, 210023, China
| | - Jie Zhang
- School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing, 210023, China
- Joint College of Ningbo University and Angre University, Ningbo, 315201, China
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Gulf Countries’ Citizens’ Acceptance of COVID-19 Vaccines—A Machine Learning Approach. MATHEMATICS 2022. [DOI: 10.3390/math10030467] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
The COVID-19 pandemic created a global emergency in many sectors. The spread of the disease can be subdued through timely vaccination. The COVID-19 vaccination process in various countries is ongoing and is slowing down due to multiple factors. Many studies on European countries and the USA have been conducted and have highlighted the public’s concern that over-vaccination results in slowing the vaccination rate. Similarly, we analyzed a collection of data from the gulf countries’ citizens’ COVID-19 vaccine-related discourse shared on social media websites, mainly via Twitter. The people’s feedback regarding different types of vaccines needs to be considered to increase the vaccination process. In this paper, the concerns of Gulf countries’ people are highlighted to lessen the vaccine hesitancy. The proposed approach emphasizes the Gulf region-specific concerns related to COVID-19 vaccination accurately using machine learning (ML)-based methods. The collected data were filtered and tokenized to analyze the sentiments extracted using three different methods: Ratio, TextBlob, and VADER methods. The sentiment-scored data were classified into positive and negative tweeted data using a proposed LSTM method. Subsequently, to obtain more confidence in classification, the in-depth features from the proposed LSTM were extracted and given to four different ML classifiers. The ratio, TextBlob, and VADER sentiment scores were separately provided to LSTM and four machine learning classifiers. The VADER sentiment scores had the best classification results using fine-KNN and Ensemble boost with 94.01% classification accuracy. Given the improved accuracy, the proposed scheme is robust and confident in classifying and determining sentiments in Twitter discourse.
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Visual Sentiment Analysis Using Deep Learning Models with Social Media Data. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12031030] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Analyzing the sentiments of people from social media content through text, speech, and images is becoming vital in a variety of applications. Many existing research studies on sentiment analysis rely on textual data, and similar to the sharing of text, users of social media share more photographs and videos. Compared to text, images are said to exhibit the sentiments in a much better way. So, there is an urge to build a sentiment analysis model based on images from social media. In our work, we employed different transfer learning models, including the VGG-19, ResNet50V2, and DenseNet-121 models, to perform sentiment analysis based on images. They were fine-tuned by freezing and unfreezing some of the layers, and their performance was boosted by applying regularization techniques. We used the Twitter-based images available in the Crowdflower dataset, which contains URLs of images with their sentiment polarities. Our work also presents a comparative analysis of these pre-trained models in the prediction of image sentiments on our dataset. The accuracies of our fine-tuned transfer learning models involving VGG-19, ResNet50V2, and DenseNet-121 are 0.73, 0.75, and 0.89, respectively. When compared to previous attempts at visual sentiment analysis, which used a variety of machine and deep learning techniques, our model had an improved accuracy by about 5% to 10%. According to the findings, the fine-tuned DenseNet-121 model outperformed the VGG-19 and ResNet50V2 models in image sentiment prediction.
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Bokaee Nezhad Z, Deihimi MA. Twitter sentiment analysis from Iran about COVID 19 vaccine. Diabetes Metab Syndr 2022; 16:102367. [PMID: 34933273 PMCID: PMC8667351 DOI: 10.1016/j.dsx.2021.102367] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 12/07/2021] [Accepted: 12/09/2021] [Indexed: 11/29/2022]
Abstract
BACKGROUND AND AIMS The development of vaccines against COVID-19 has been a global purpose since the World Health Organization declared the pandemic. People usually use social media, especially Twitter, to transfer knowledge and beliefs on global concerns like COVID-19-vaccination, hence, Twitter is a good source for investigating public opinions. The present study aimed to assess Persian tweets to (1) analyze Iranian people's view toward COVID-19 vaccination. (2) Compare Iranian views toward a homegrown and imported COVID-19-vaccines. METHODS First, a total of 803278 Persian tweets were retrieved from Twitter, mentioning COVIran Barekat (the homegrown vaccine), Pfizer/BioNTech, AstraZeneca/Oxford, Moderna, and Sinopharm (imported vaccines) between April 1, 2021 and September 30, 2021. Then, we identified sentiments of retrieved tweets using a deep learning sentiment analysis model based on CNN-LSTM architecture. Finally, we investigated Iranian views toward COVID-19-vaccination. RESULTS (1) We found a subtle difference in the number of positive sentiments toward the homegrown and foreign vaccines, and the latter had the dominant positive polarity. (2) The negative sentiment regarding homegrown and imported vaccines seems to be increasing in some months. (3) We also observed no significant differences between the percentage of overall positive and negative opinions toward vaccination amongst Iranian people. CONCLUSIONS It is worrisome that the negative sentiment toward homegrown and imported vaccines increases in Iran in some months. Since public healthcare agencies aim to increase the uptake of COVID-19 vaccines to end the pandemic, they can focus on social media such as Twitter to promote positive messaging and decrease opposing views.
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