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Chinyanga E, Britwum K, Gustafson CR, Bernard JC. Did COVID-19 influence fruit and vegetable consumption? Explaining and comparing pandemic peak and post-peak periods. Appetite 2024; 201:107574. [PMID: 38909696 DOI: 10.1016/j.appet.2024.107574] [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: 10/06/2023] [Revised: 03/28/2024] [Accepted: 06/19/2024] [Indexed: 06/25/2024]
Abstract
The COVID-19 pandemic, one of the worst global health crises in the last century, impacted nearly every aspect of people's lives, including their dietary choices and food consumption patterns. It arrived during a long shift in American diets featuring increasingly large portions of processed foods as well as fruit and vegetable consumption that is well below recommended levels. Improving the latter has been a key part of policymakers' efforts to improve consumers' diets. This study surveyed individuals in the US South to determine the factors influencing their consumption of fruit and vegetables during the pandemic peak and how these have changed post-peak. During the peak, food venue, demographics, and concerns about diet and the seriousness of the virus heavily affected consumption. Greater amounts of fresh fruit and vegetables were consumed post-peak pandemic. Changes post-peak were predicted by food venue. Cooking meals at home was the main positive predictor for consumption. US policymakers should try and leverage the changes since the peak to promote greater consumption of fruit and vegetables.
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Affiliation(s)
- Eckton Chinyanga
- The Labry School of Science, Technology, and Business, Cumberland University, Lebanon, TN, USA.
| | - Kofi Britwum
- Department of Applied Economics and Statistics, University of Delaware, Newark, DE, USA.
| | | | - John C Bernard
- Department of Applied Economics and Statistics, University of Delaware, Newark, DE, USA.
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2
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Tang M, Heung Y, Fellman B, Bruera E. Frequency of vaccine hesitancy among patients with advanced cancer. Palliat Support Care 2024; 22:289-295. [PMID: 37525556 DOI: 10.1017/s147895152300113x] [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] [Indexed: 08/02/2023]
Abstract
BACKGROUND Vaccine hesitancy has become prevalent in society. Vulnerable populations, such as those with cancer, are susceptible to increased morbidity and mortality from diseases that may have been prevented through vaccination. OBJECTIVES Our objective was to determine patient perception of vaccine efficacy and safety and sources of information that influence decisions. METHODS This study was a prospective cross-sectional survey trial conducted from March 10, 2022, to November 1, 2022, at a Supportive Care Clinic. Patients completed the survey with a research assistant or from a survey link. Vaccine hesitancy was defined as a response of 2 or more on the Parent Attitudes About Childhood Vaccines (PACV-4). Perception on vaccine safety and efficacy along with the importance of sources of information were determined by a questionnaire. RESULTS Of the 72 patients who completed the PACV-4, 30 were considered vaccine-hesitant (42%). Of those who completed the survey alone (35), 23 (66%) were vaccine-hesitant; and of those who completed the survey with the help of a study coordinator (37), 7 (19%) were vaccine-hesitant. The most important source for decision-making was their doctor (82%, 95% CI 73-89), followed by family (42%, 95% CI 32-52), news/media (31%, 95% CI 22-41), and social media (9%, 95% CI 4-16). Clinical and demographic factors including age, gender, race/ethnicity, education level, and location of residence were not associated with vaccine hesitancy. SIGNIFICANCE OF RESULTS Vaccine hesitancy is present among patients with advanced cancer. The high value given to the doctor's recommendation suggests that universal precautions regarding vaccine recommendation may be an effective intervention.
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Affiliation(s)
- Michael Tang
- Department of Palliative, Rehabilitation and Integrative Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yvonne Heung
- Department of Palliative, Rehabilitation and Integrative Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Bryan Fellman
- Department of Biostatistics, MD Anderson Cancer Center, Houston, TX, USA
| | - Eduardo Bruera
- Department of Palliative, Rehabilitation and Integrative Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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3
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Gyftopoulos S, Drosatos G, Fico G, Pecchia L, Kaldoudi E. Analysis of Pharmaceutical Companies' Social Media Activity during the COVID-19 Pandemic and Its Impact on the Public. Behav Sci (Basel) 2024; 14:128. [PMID: 38392481 PMCID: PMC10886074 DOI: 10.3390/bs14020128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 01/30/2024] [Accepted: 02/06/2024] [Indexed: 02/24/2024] Open
Abstract
The COVID-19 pandemic, a period of great turmoil, was coupled with the emergence of an "infodemic", a state when the public was bombarded with vast amounts of unverified information from dubious sources that led to a chaotic information landscape. The excessive flow of messages to citizens, combined with the justified fear and uncertainty imposed by the unknown virus, cast a shadow on the credibility of even well-intentioned sources and affected the emotional state of the public. Several studies highlighted the mental toll this environment took on citizens by analyzing their discourse on online social networks (OSNs). In this study, we focus on the activity of prominent pharmaceutical companies on Twitter, currently known as X, as well as the public's response during the COVID-19 pandemic. Communication between companies and users is examined and compared in two discrete channels, the COVID-19 and the non-COVID-19 channel, based on the content of the posts circulated in them in the period between March 2020 and September 2022, while the emotional profile of the content is outlined through a state-of-the-art emotion analysis model. Our findings indicate significantly increased activity in the COVID-19 channel compared to the non-COVID-19 channel while the predominant emotion in both channels is joy. However, the COVID-19 channel exhibited an upward trend in the circulation of fear by the public. The quotes and replies produced by the users, with a stark presence of negative charge and diffusion indicators, reveal the public's preference for promoting tweets conveying an emotional charge, such as fear, surprise, and joy. The findings of this research study can inform the development of communication strategies based on emotion-aware messages in future crises.
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Affiliation(s)
- Sotirios Gyftopoulos
- European Alliance for Medical and Biological Engineering and Science, 3001 Leuven, Belgium
- Institute for Language and Speech Processing, Athena Research Center, 67100 Xanthi, Greece
| | - George Drosatos
- European Alliance for Medical and Biological Engineering and Science, 3001 Leuven, Belgium
- Institute for Language and Speech Processing, Athena Research Center, 67100 Xanthi, Greece
| | - Giuseppe Fico
- European Alliance for Medical and Biological Engineering and Science, 3001 Leuven, Belgium
- Life Supporting Technologies, Universidad Politécnica de Madrid, 28040 Madrid, Spain
| | - Leandro Pecchia
- European Alliance for Medical and Biological Engineering and Science, 3001 Leuven, Belgium
- School of Engineering, University of Warwick, Coventry CV4 7AL, UK
- Department of Engineering, Università Campus Bio-Medico di Roma, 00128 Rome, Italy
| | - Eleni Kaldoudi
- European Alliance for Medical and Biological Engineering and Science, 3001 Leuven, Belgium
- Institute for Language and Speech Processing, Athena Research Center, 67100 Xanthi, Greece
- School of Medicine, Democritus University of Thrace, 68100 Alexandroupoli, Greece
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4
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Guo F, Liu Z, Lu Q, Ji S, Zhang C. Public Opinion About COVID-19 on a Microblog Platform in China: Topic Modeling and Multidimensional Sentiment Analysis of Social Media. J Med Internet Res 2024; 26:e47508. [PMID: 38294856 PMCID: PMC10833090 DOI: 10.2196/47508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 09/09/2023] [Accepted: 12/20/2023] [Indexed: 02/01/2024] Open
Abstract
BACKGROUND The COVID-19 pandemic raised wide concern from all walks of life globally. Social media platforms became an important channel for information dissemination and an effective medium for public sentiment transmission during the COVID-19 pandemic. OBJECTIVE Mining and analyzing social media text information can not only reflect the changes in public sentiment characteristics during the COVID-19 pandemic but also help the government understand the trends in public opinion and reasonably control public opinion. METHODS First, this study collected microblog comments related to the COVID-19 pandemic as a data set. Second, sentiment analysis was carried out based on the topic modeling method combining latent Dirichlet allocation (LDA) and Bidirectional Encoder Representations from Transformers (BERT). Finally, a machine learning linear regression (ML-LR) model combined with a sparse matrix was proposed to explore the evolutionary trend in public opinion on social media and verify the high accuracy of the model. RESULTS The experimental results show that, in different stages, the characteristics of public emotion are different, and the overall trend is from negative to positive. CONCLUSIONS The proposed method can effectively reflect the characteristics of the different times and space of public opinion. The results provide theoretical support and practical reference in response to public health and safety events.
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Affiliation(s)
- Feipeng Guo
- Modern Business Research Center, Zhejiang Gongshang University, Hangzhou, China
- School of Management and E-Business, Zhejiang Gongshang University, Hangzhou, China
| | - Zixiang Liu
- Modern Business Research Center, Zhejiang Gongshang University, Hangzhou, China
| | - Qibei Lu
- School of International Business, Zhejiang International Studies University, Hangzhou, China
| | - Shaobo Ji
- Sprott School of Business, Carleton University, Ottawa, ON, Canada
| | - Chen Zhang
- General Manager's Office, Hangzhou Gaojin Technology Co, Ltd, Hangzhou, China
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5
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Yin S, Chen S, Ge Y. Dynamic Associations Between Centers for Disease Control and Prevention Social Media Contents and Epidemic Measures During COVID-19: Infoveillance Study. JMIR INFODEMIOLOGY 2024; 4:e49756. [PMID: 38261367 PMCID: PMC10848128 DOI: 10.2196/49756] [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: 06/09/2023] [Revised: 10/02/2023] [Accepted: 10/14/2023] [Indexed: 01/24/2024]
Abstract
BACKGROUND Health agencies have been widely adopting social media to disseminate important information, educate the public on emerging health issues, and understand public opinions. The Centers for Disease Control and Prevention (CDC) widely used social media platforms during the COVID-19 pandemic to communicate with the public and mitigate the disease in the United States. It is crucial to understand the relationships between the CDC's social media communications and the actual epidemic metrics to improve public health agencies' communication strategies during health emergencies. OBJECTIVE This study aimed to identify key topics in tweets posted by the CDC during the pandemic, investigate the temporal dynamics between these key topics and the actual COVID-19 epidemic measures, and make recommendations for the CDC's digital health communication strategies for future health emergencies. METHODS Two types of data were collected: (1) a total of 17,524 COVID-19-related English tweets posted by the CDC between December 7, 2019, and January 15, 2022, and (2) COVID-19 epidemic measures in the United States from the public GitHub repository of Johns Hopkins University from January 2020 to July 2022. Latent Dirichlet allocation topic modeling was applied to identify key topics from all COVID-19-related tweets posted by the CDC, and the final topics were determined by domain experts. Various multivariate time series analysis techniques were applied between each of the identified key topics and actual COVID-19 epidemic measures to quantify the dynamic associations between these 2 types of time series data. RESULTS Four major topics from the CDC's COVID-19 tweets were identified: (1) information on the prevention of health outcomes of COVID-19; (2) pediatric intervention and family safety; (3) updates of the epidemic situation of COVID-19; and (4) research and community engagement to curb COVID-19. Multivariate analyses showed that there were significant variabilities of progression between the CDC's topics and the actual COVID-19 epidemic measures. Some CDC topics showed substantial associations with the COVID-19 measures over different time spans throughout the pandemic, expressing similar temporal dynamics between these 2 types of time series data. CONCLUSIONS Our study is the first to comprehensively investigate the dynamic associations between topics discussed by the CDC on Twitter and the COVID-19 epidemic measures in the United States. We identified 4 major topic themes via topic modeling and explored how each of these topics was associated with each major epidemic measure by performing various multivariate time series analyses. We recommend that it is critical for public health agencies, such as the CDC, to update and disseminate timely and accurate information to the public and align major topics with key epidemic measures over time. We suggest that social media can help public health agencies to inform the public on health emergencies and to mitigate them effectively.
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Affiliation(s)
- Shuhua Yin
- University of North Carolina at Charlotte, Charlotte, NC, United States
| | - Shi Chen
- University of North Carolina at Charlotte, Charlotte, NC, United States
| | - Yaorong Ge
- University of North Carolina at Charlotte, Charlotte, NC, United States
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6
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Luo W, Liu Q, Zhou Y, Ran Y, Liu Z, Hou W, Pei S, Lai S. Spatiotemporal variations of "triple-demic" outbreaks of respiratory infections in the United States in the post-COVID-19 era. BMC Public Health 2023; 23:2452. [PMID: 38062417 PMCID: PMC10704638 DOI: 10.1186/s12889-023-17406-9] [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: 06/15/2023] [Accepted: 12/04/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND The US confronted a "triple-demic" of influenza, respiratory syncytial virus (RSV), and COVID-19 in the winter of 2022, leading to increased respiratory infections and a higher demand for medical supplies. It is urgent to analyze these epidemics and their spatial-temporal co-occurrence, identifying hotspots and informing public health strategies. METHODS We employed retrospective and prospective space-time scan statistics to assess the situations of COVID-19, influenza, and RSV in 51 US states from October 2021 to February 2022, and from October 2022 to February 2023, respectively. This enabled monitoring of spatiotemporal variations for each epidemic individually and collectively. RESULTS Compared to winter 2021, COVID-19 cases decreased while influenza and RSV infections significantly increased in winter 2022. We found a high-risk cluster of influenza and COVID-19 (not all three) in winter 2021. In late November 2022, a large high-risk cluster of triple-demic emerged in the central US. The number of states at high risk for multiple epidemics increased from 15 in October 2022 to 21 in January 2023. CONCLUSIONS Our study offers a novel spatiotemporal approach that combines both univariate and multivariate surveillance, as well as retrospective and prospective analyses. This approach offers a more comprehensive and timely understanding of how the co-occurrence of COVID-19, influenza, and RSV impacts various regions within the United States. Our findings assist in tailor-made strategies to mitigate the effects of these respiratory infections.
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Affiliation(s)
- Wei Luo
- GeoSpatialX Lab, Department of Geography, National University of Singapore, 1 Arts Link, #04-32 Block AS2, Singapore, 117570, Singapore.
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.
| | - Qianhuang Liu
- GeoSpatialX Lab, Department of Geography, National University of Singapore, 1 Arts Link, #04-32 Block AS2, Singapore, 117570, Singapore
| | - Yuxuan Zhou
- Department of Architecture and Civil Engineering, City University of Hong Kong, Kowloon, Hong Kong SAR, China
| | - Yiding Ran
- GeoSpatialX Lab, Department of Geography, National University of Singapore, 1 Arts Link, #04-32 Block AS2, Singapore, 117570, Singapore
| | - Zhaoyin Liu
- GeoSpatialX Lab, Department of Geography, National University of Singapore, 1 Arts Link, #04-32 Block AS2, Singapore, 117570, Singapore
| | - Weitao Hou
- Department Of Biological Sciences, National University of Singapore, Singapore, Singapore
| | - Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, USA
| | - Shengjie Lai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, SO17 1BJ, UK.
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7
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Fears NE, Chatterjee R, Tamplain PM, Miller HL. Harvesting Twitter Data for Studying Motor Behavior in Disabled Populations: An Introduction and Tutorial in Python. JOURNAL OF MOTOR LEARNING AND DEVELOPMENT 2023; 11:555-570. [PMID: 38283882 PMCID: PMC10811446 DOI: 10.1123/jmld.2023-0006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2024]
Abstract
Social media platforms are rich and dynamic spaces where individuals communicate on a person-to-person level and to broader audiences. These platforms provide a wealth of publicly available data that can shed light on the lived experiences of people from numerous clinical populations. Twitter can be used to examine individual expressions and community discussions about specific characteristics (e.g., motor skills, burnout) associated with a diagnostic group. These data are useful for understanding the perspectives of a diverse, international group of self-advocates representing a wide range of clinical populations. Here, we provide a framework for how to harvest data from Twitter through their free, academic researcher application programming interface access using Python, a free, open-source programming language. We also provide a sample data set harvested using this framework and a set of analyses on these data specifically related to motor differences in neurodevelopmental conditions. This framework offers a cost-effective and flexible means of harvesting and analyzing Twitter data. Researchers should utilize these resources to advance our understanding of the lived experiences of clinical populations through social media platforms and to determine the critical questions that are of most importance to improving quality of life.
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Affiliation(s)
- Nicholas E Fears
- School of Kinesiology, University of Michigan, Ann Arbor, MI, USA
- School of Kinesiology, Louisiana State University, Baton Rouge, LA, USA
| | - Riya Chatterjee
- School of Kinesiology, University of Michigan, Ann Arbor, MI, USA
| | - Priscila M Tamplain
- Department of Kinesiology, University of Texas at Arlington, Arlington, TX, USA
| | - Haylie L Miller
- School of Kinesiology, University of Michigan, Ann Arbor, MI, USA
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8
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Lin LY, Lin CJ, Kuan CI, Chiou HY. Potential Determinants Contributing to COVID-19 Vaccine Acceptance and Hesitancy in Taiwan: Rapid Qualitative Mixed Methods Study. JMIR Form Res 2023; 7:e41364. [PMID: 37698904 PMCID: PMC10523213 DOI: 10.2196/41364] [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: 07/23/2022] [Revised: 04/05/2023] [Accepted: 08/27/2023] [Indexed: 09/13/2023] Open
Abstract
BACKGROUND Although vaccination has been shown to be one of the most important interventions, COVID-19 vaccine hesitancy remains one of the top 10 global public health challenges worldwide. OBJECTIVE The objective of this study is to investigate (1) major determinants of vaccine hesitancy, (2) changes in the determinants of vaccine hesitancy at different time periods, and (3) the potential factors affecting vaccine acceptance. METHODS This study applied a mixed methods approach to explore the potential determinants contributing to vaccine hesitancy among the Taiwanese population. The quantitative design of this study involved using Google Trends search query data. We chose the search term "" (vaccine), selected "" (Taiwan) as the location, and selected the period between December 18, 2020, and July 31, 2021. The rising keywords related to vaccine acceptance and hesitancy were collected. Based on the responses obtained from the qualitative study and the rising keywords obtained in Google Trends, the 3 most popular keywords related to vaccine hesitancy were identified and used as search queries in Google Trends between December 18, 2020, and July 31, 2021, to generate relative search volumes (RSVs). Lastly, autoregressive integrated moving average modeling was used to forecast the RSVs for the 3 keywords between May 29 and July 31, 2021. The estimated RSVs were compared to the observed RSVs in Google Trends within the same time frame. RESULTS The 4 prevailing factors responsible for COVID-19 vaccine acceptance and hesitancy were doubts about the government and manufacturers, side effects, deaths associated with vaccination, and efficacy of vaccination. During the vaccine observation period, "political role" was the overarching consideration leading to vaccine hesitancy. During the peak of the pandemic, side effects, death, and vaccine protection were the main factors contributing to vaccine hesitancy. The popularity of the 3 frequently searched keywords "side effects," "vaccine associated deaths," and "vaccine protection" continued to rise throughout the pandemic outbreak. Lastly, the highest Google search queries related to COVID-19 vaccines emerged as "side effects" prior to vaccination, deaths associated with vaccines during the period when single vaccines were available, and "side effects" and "vaccine protection" during the period when multiple vaccines were available. CONCLUSIONS Investigating the key factors influencing COVID-19 vaccine hesitancy appears to be a fundamental task that needs to be undertaken to ensure effective implementation of COVID-19 vaccination. Google Trends may be used as a complementary infoveillance tool by government agencies for future vaccine policy implementation and communication.
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Affiliation(s)
- Li-Yin Lin
- Department of Leisure Industry and Health Promotion, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan
| | - Chun-Ji Lin
- Institute of Population Health Sciences, National Health Research Institutes, Miaoli County, Taiwan
| | - Chen-I Kuan
- Institute of Health Behaviors and Community Sciences, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Hung-Yi Chiou
- Institute of Population Health Sciences, National Health Research Institutes, Miaoli County, Taiwan
- School of Public Health, College of Public Health, Taipei Medical University, Taipei, Taiwan
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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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Rathke BH, Yu H, Huang H. What Remains Now That the Fear Has Passed? Developmental Trajectory Analysis of COVID-19 Pandemic for Co-occurrences of Twitter, Google Trends, and Public Health Data. Disaster Med Public Health Prep 2023; 17:e471. [PMID: 37317615 DOI: 10.1017/dmp.2023.101] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
OBJECTIVE The rapid onset of coronavirus disease 2019 (COVID-19) created a complex virtual collective consciousness. Misinformation and polarization were hallmarks of the pandemic in the United States, highlighting the importance of studying public opinion online. Humans express their thoughts and feelings more openly than ever before on social media; co-occurrence of multiple data sources have become valuable for monitoring and understanding public sentimental preparedness and response to an event within our society. METHODS In this study, Twitter and Google Trends data were used as the co-occurrence data for the understanding of the dynamics of sentiment and interest during the COVID-19 pandemic in the United States from January 2020 to September 2021. Developmental trajectory analysis of Twitter sentiment was conducted using corpus linguistic techniques and word cloud mapping to reveal 8 positive and negative sentiments and emotions. Machine learning algorithms were used to implement the opinion mining how Twitter sentiment was related to Google Trends interest with historical COVID-19 public health data. RESULTS The sentiment analysis went beyond polarity to detect specific feelings and emotions during the pandemic. CONCLUSIONS The discoveries on the behaviors of emotions at each stage of the pandemic were presented from the emotion detection when associated with the historical COVID-19 data and Google Trends data.
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Affiliation(s)
- Benjamin Havis Rathke
- Department of Applied Statistics and Research Methods, University of Northern Colorado, Greeley, Colorado, USA
| | - Han Yu
- Department of Applied Statistics and Research Methods, University of Northern Colorado, Greeley, Colorado, USA
| | - Hong Huang
- School of Information, University of South Florida, Tampa, Florida, USA
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Catelli R, Pelosi S, Comito C, Pizzuti C, Esposito M. Lexicon-based sentiment analysis to detect opinions and attitude towards COVID-19 vaccines on Twitter in Italy. Comput Biol Med 2023; 158:106876. [PMID: 37030266 PMCID: PMC10072979 DOI: 10.1016/j.compbiomed.2023.106876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 02/26/2023] [Accepted: 03/30/2023] [Indexed: 04/08/2023]
Abstract
The paper proposes a methodology based on Natural Language Processing (NLP) and Sentiment Analysis (SA) to get insights into sentiments and opinions toward COVID-19 vaccination in Italy. The studied dataset consists of vaccine-related tweets published in Italy from January 2021 to February 2022. In the considered period, 353,217 tweets have been analyzed, obtained after filtering 1,602,940 tweets with the word "vaccin". A main novelty of the approach is the categorization of opinion holders in four classes, Common users, Media, Medicine, Politics, obtained by applying NLP tools, enhanced with large-scale domain-specific lexicons, on the short bios published by users themselves. Feature-based sentiment analysis is enriched with an Italian sentiment lexicon containing polarized words, expressing semantic orientation, and intensive words which give cues to identify the tone of voice of each user category. The results of the analysis highlighted an overall negative sentiment along all the considered periods, especially for the Common users, and a different attitude of opinion holders towards specific important events, such as deaths after vaccination, occurring in some days of the examined 14 months.
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Affiliation(s)
- Rosario Catelli
- Institute for High Performance Computing and Networking (ICAR), National Research Council (CNR), Italy.
| | - Serena Pelosi
- Institute for High Performance Computing and Networking (ICAR), National Research Council (CNR), Italy.
| | - Carmela Comito
- Institute for High Performance Computing and Networking (ICAR), National Research Council (CNR), Italy.
| | - Clara Pizzuti
- Institute for High Performance Computing and Networking (ICAR), National Research Council (CNR), Italy.
| | - Massimo Esposito
- Institute for High Performance Computing and Networking (ICAR), National Research Council (CNR), Italy.
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12
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Prada E, Langbecker A, Catalan-Matamoros D. Public discourse and debate about vaccines in the midst of the covid-19 pandemic: a qualitative content analysis of Twitter. Vaccine 2023; 41:3196-3203. [PMID: 37080830 PMCID: PMC10070776 DOI: 10.1016/j.vaccine.2023.03.068] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 03/02/2023] [Accepted: 03/30/2023] [Indexed: 04/07/2023]
Abstract
Objectives Characterize the public debate and discourse about vaccines during the covid-19 vaccination programmes. Methods We performed a manual content analysis of a sample of English-written Twitter posts that included the word vaccine and its derivatives. We categorized 7 variables pertaining to the content of the posts, and classified the type of user that published the post and the number of retweets. Then, the patterns of association between these variables were further explored. Results Among the tweets with negative tone towards vaccines, 33% display negationist discourses, 29% protest or defiance discourses, 13% discuss the pandemic management measures and yet another 13% of these tweets display a scientific discourse. Research results, vaccination data and practical information are more associated to positive tone towards vaccines, while news relate to neutral tone. The users that received more retweets were media accounts and journalists, followed by government accounts and scientific organizations related to the government. Tweets displaying preventive messages received more retweets in average. The discourses most associated with objective information are the preventive, institutional, medical-scientific, and those about the different measures to manage the pandemic. On the other hand, the most subjective tweets are those with negationist, antinegationist and protest discourses. Conclusions Although there is a non-negligible proportion of tweets that are directly opposed to vaccines, also an important part of vaccine-negative content takes the form of protest discourses, criticisms towards government actions as well as towards the measures to tackle the pandemic. Therefore, negative discourses during the pandemic included serious vaccine hesitancy cases. Moreover, they were not only fuelled by distrust in science, but also and very importantly they were connected to dissatisfaction towards the public management of the pandemic.
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Affiliation(s)
- Enrique Prada
- UC3M Medialab, Department of Communication and Media Studies, Madrid University Carlos III, Spain
| | - Andrea Langbecker
- UC3M Medialab, Department of Communication and Media Studies, Madrid University Carlos III, Spain.
| | - Daniel Catalan-Matamoros
- UC3M Medialab, Department of Communication and Media Studies, Madrid University Carlos III, Spain
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Zhou Y, Li R, Shen L. Psychological profiles of COVID vaccine-hesitant individuals and implications for vaccine message design strategies. Vaccine X 2023; 13:100279. [PMID: 36910012 PMCID: PMC9987601 DOI: 10.1016/j.jvacx.2023.100279] [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: 05/12/2022] [Revised: 03/02/2023] [Accepted: 03/03/2023] [Indexed: 03/08/2023] Open
Abstract
COVID-19 has caused tremendous consequences in the U.S., and combating the pandemic requires a significant number of Americans to receive COVID-19 vaccines. Guided by prominent health communication theories, this project took a formative evaluation approach and employed a national sample (N = 1041) in the U.S. to explore the potential differences between vaccine-inclined vs. -hesitant individuals and to generate profiles of hesitant individuals as the foundation for audience segmentation and message targeting. Five distinct profiles emerged in the sample. Characteristics of each profile were described, and appropriate messaging strategies were identified to target each group. Theoretical and practical implications were discussed.
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Affiliation(s)
- Yanmengqian Zhou
- Department of Communication Studies, Louisiana State University, 229 Coates Hall, Baton Rouge, LA 70803, United States
| | - Ruobing Li
- School of Communication and Journalism Stony Brook University Frank Melville, Jr. Memorial Library, John S. Toll Drive N-4011, Stony Brook, NY 11794, United States
| | - Lijiang Shen
- Department of Communication Arts & Sciences College of the Liberal Arts, Pennsylvania State University, 221 Sparks Building, University Park, PA 16802, United States
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14
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Davidson PD, Muniandy T, Karmegam D. Perception of COVID-19 vaccination among Indian Twitter users: computational approach. JOURNAL OF COMPUTATIONAL SOCIAL SCIENCE 2023:1-20. [PMID: 37363805 PMCID: PMC10047476 DOI: 10.1007/s42001-023-00203-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 03/01/2023] [Indexed: 06/28/2023]
Abstract
Vaccination has been a hot topic in the present COVID-19 context. The government, public health stakeholders and media are all concerned about how to get the people vaccinated. The study was intended to explore the perception and emotions of the Indians citizens toward COVID-19 vaccine from Twitter messages. The tweets were collected for the period of 6 months, from mid-January to June, 2021 using hash-tags and keywords specific to India. Topics and emotions from the tweets were extracted using Latent Dirichlet Allocation (LDA) method and National Research Council (NRC) Lexicon, respectively. Theme, sentiment and emotion wise engagement and reachability metrics were assessed. Hash-tag frequency of COVID-19 vaccine brands were also identified and evaluated. Information regarding 'Co-WIN app and availability of vaccine' was widely discussed and also received highest engagement and reachability among Twitter users. Among the various emotions, trust was expressed the most, which highlights the acceptance of vaccines among the Indian citizens. The hash-tags frequency of vaccine brands shows that Covishield was popular in the month of March 2021, and Covaxin in April 2021. The results from the study will help stakeholders to efficiently use social media to disseminate COVID-19 vaccine information on popular concerns. This in turn will encourage citizens to be vaccinated and achieve herd immunity. Similar methodology can be adopted in future to understand the perceptions and concerns of people in emergency situations. Supplementary Information The online version contains supplementary material available at 10.1007/s42001-023-00203-0.
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Affiliation(s)
| | | | - Dhivya Karmegam
- School of Public Health, SRM Institute of Science and Technology, Kattankulathur, Chennai, India
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15
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Zhou Y, Li R, Shen L. Targeting COVID-19 vaccine-hesitancy in college students: An audience-centered approach. JOURNAL OF AMERICAN COLLEGE HEALTH : J OF ACH 2023:1-10. [PMID: 36853986 DOI: 10.1080/07448481.2023.2180988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 12/27/2022] [Accepted: 02/10/2023] [Indexed: 06/18/2023]
Abstract
Objective: The study tested potential factors that differentiated the COVID-19 vaccine-hesitant and -inclined college students and, based on these factors, identified subgroups of the vaccine-hesitant students. Participants: Participants were 1,183 U.S. college students attending four-year universities or community colleges recruited through Qualtrics between January 25 and March 3, 2021. Methods: Participants completed an online survey assessing their COVID-19 vaccination intention, perceived risks of COVID-19 and the COVID-19 vaccines, efficacy beliefs regarding COVID-19 and the COVID-19 vaccines, and emotions toward taking the COVID-19 vaccines. Results: Vaccine-hesitant and -inclined college students varied in their emotions, risk perceptions, and efficacy beliefs regarding the virus and the vaccines. Using these factors as indicators, vaccine-hesitant college students were classified into five latent subgroups with distinct characteristics. Conclusions: In identifying subgroups of the vaccine-hesitant college students, the study has important insights to offer regarding the design of vaccine-promotion messaging strategies targeting the college student population.
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Affiliation(s)
- Yanmengqian Zhou
- Department of Communication Studies, Louisiana State University, Baton Rouge, Louisiana, USA
| | - Ruobing Li
- School of Communication & Journalism, Stony Brook University, Stony Brook, New York, USA
| | - Lijiang Shen
- Department of Communication Arts & Sciences, Pennsylvania State University, University Park, Pennsylvania, USA
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16
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Mavragani A, Xie F, An X, Lan X, Liu C, Yan L, Zhang H. Evolution of Public Attitudes and Opinions Regarding COVID-19 Vaccination During the Vaccine Campaign in China: Year-Long Infodemiology Study of Weibo Posts. J Med Internet Res 2023; 25:e42671. [PMID: 36795467 PMCID: PMC9937109 DOI: 10.2196/42671] [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: 09/13/2022] [Revised: 01/20/2023] [Accepted: 01/27/2023] [Indexed: 02/17/2023] Open
Abstract
BACKGROUND Monitoring people's perspectives on the COVID-19 vaccine is crucial for understanding public vaccination hesitancy and developing effective, targeted vaccine promotion strategies. Although this is widely recognized, studies on the evolution of public opinion over the course of an actual vaccination campaign are rare. OBJECTIVE We aimed to track the evolution of public opinion and sentiment toward COVID-19 vaccines in online discussions over an entire vaccination campaign. Moreover, we aimed to reveal the pattern of gender differences in attitudes and perceptions toward vaccination. METHODS We collected COVID-19 vaccine-related posts by the general public that appeared on Sina Weibo from January 1, 2021, to December 31, 2021; this period covered the entire vaccination process in China. We identified popular discussion topics using latent Dirichlet allocation. We further examined changes in public sentiment and topics during the 3 stages of the vaccination timeline. Gender differences in perceptions toward vaccination were also investigated. RESULTS Of 495,229 crawled posts, 96,145 original posts from individual accounts were included. Most posts presented positive sentiments (positive: 65,981/96,145, 68.63%; negative: 23,184/96,145, 24.11%; neutral: 6980/96,145, 7.26%). The average sentiment scores were 0.75 (SD 0.35) for men and 0.67 (SD 0.37) for women. The overall trends in sentiment scores showed a mixed response to the number of new cases and significant events related to vaccine development and important holidays. The sentiment scores showed a weak correlation with new case numbers (R=0.296; P=.03). Significant sentiment score differences were observed between men and women (P<.001). Common and distinguishing characteristics were found among frequently discussed topics during the different stages, with significant differences in topic distribution between men and women (January 1, 2021, to March 31, 2021: χ23=3030.9; April 1, 2021, to September 30, 2021: χ24=8893.8; October 1, 2021, to December 31, 2021: χ25=3019.5; P<.001). Women were more concerned with side effects and vaccine effectiveness. In contrast, men reported broader concerns around the global pandemic, the progress of vaccine development, and economics affected by the pandemic. CONCLUSIONS Understanding public concerns regarding vaccination is essential for reaching vaccine-induced herd immunity. This study tracked the year-long evolution of attitudes and opinions on COVID-19 vaccines according to the different stages of vaccination in China. These findings provide timely information that will enable the government to understand the reasons for low vaccine uptake and promote COVID-19 vaccination nationwide.
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Affiliation(s)
| | - Fang Xie
- Medical Basic Experimental Teaching Center, China Medical University, Shenyang, China
| | - Xinyu An
- School of Health Management, China Medical University, Shenyang, China
| | - Xue Lan
- School of Health Management, China Medical University, Shenyang, China
| | - Chunhe Liu
- School of Health Management, China Medical University, Shenyang, China
| | - Lei Yan
- School of Health Management, China Medical University, Shenyang, China
| | - Han Zhang
- School of Health Management, China Medical University, Shenyang, China
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17
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Zang S, Zhang X, Xing Y, Chen J, Lin L, Hou Z. Applications of Social Media and Digital Technologies in COVID-19 Vaccination: Scoping Review. J Med Internet Res 2023; 25:e40057. [PMID: 36649235 PMCID: PMC9924059 DOI: 10.2196/40057] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 12/18/2022] [Accepted: 01/13/2023] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND Social media and digital technologies have played essential roles in disseminating information and promoting vaccination during the COVID-19 pandemic. There is a need to summarize the applications and analytical techniques of social media and digital technologies in monitoring vaccine attitudes and administering COVID-19 vaccines. OBJECTIVE We aimed to synthesize the global evidence on the applications of social media and digital technologies in COVID-19 vaccination and to explore their avenues to promote COVID-19 vaccination. METHODS We searched 6 databases (PubMed, Scopus, Web of Science, Embase, EBSCO, and IEEE Xplore) for English-language articles from December 2019 to August 2022. The search terms covered keywords relating to social media, digital technology, and COVID-19 vaccines. Articles were included if they provided original descriptions of applications of social media or digital health technologies/solutions in COVID-19 vaccination. Conference abstracts, editorials, letters, commentaries, correspondence articles, study protocols, and reviews were excluded. A modified version of the Appraisal Tool for Cross-Sectional Studies (AXIS tool) was used to evaluate the quality of social media-related studies. The review was undertaken with the guidance of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. RESULTS A total of 178 articles were included in our review, including 114 social media articles and 64 digital technology articles. Social media has been applied for sentiment/emotion analysis, topic analysis, behavioral analysis, dissemination and engagement analysis, and information quality analysis around COVID-19 vaccination. Of these, sentiment analysis and topic analysis were the most common, with social media data being primarily analyzed by lexicon-based and machine learning techniques. The accuracy and reliability of information on social media can seriously affect public attitudes toward COVID-19 vaccines, and misinformation often leads to vaccine hesitancy. Digital technologies have been applied to determine the COVID-19 vaccination strategy, predict the vaccination process, optimize vaccine distribution and delivery, provide safe and transparent vaccination certificates, and perform postvaccination surveillance. The applied digital technologies included algorithms, blockchain, mobile health, the Internet of Things, and other technologies, although with some barriers to their popularization. CONCLUSIONS The applications of social media and digital technologies in addressing COVID-19 vaccination-related issues represent an irreversible trend. Attention should be paid to the ethical issues and health inequities arising from the digital divide while applying and promoting these technologies.
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Affiliation(s)
- Shujie Zang
- School of Public Health, Fudan University, Shanghai, China
- Global Health Institute, Fudan University, Shanghai, China
| | - Xu Zhang
- School of Public Health, Fudan University, Shanghai, China
- Global Health Institute, Fudan University, Shanghai, China
| | - Yuting Xing
- School of Public Health, Fudan University, Shanghai, China
- Global Health Institute, Fudan University, Shanghai, China
| | - Jiaxian Chen
- School of Public Health, Fudan University, Shanghai, China
| | - Leesa Lin
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong, SAR, China
| | - Zhiyuan Hou
- School of Public Health, Fudan University, Shanghai, China
- Global Health Institute, Fudan University, Shanghai, China
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18
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Wang S, Huang X, Hu T, She B, Zhang M, Wang R, Gruebner O, Imran M, Corcoran J, Liu Y, Bao S. A global portrait of expressed mental health signals towards COVID-19 in social media space. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION : ITC JOURNAL 2023; 116:103160. [PMID: 36570490 PMCID: PMC9759272 DOI: 10.1016/j.jag.2022.103160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 11/07/2022] [Accepted: 12/15/2022] [Indexed: 06/17/2023]
Abstract
Globally, the COVID-19 pandemic has induced a mental health crisis. Social media data offer a unique opportunity to track the mental health signals of a given population and quantify their negativity towards COVID-19. To date, however, we know little about how negative sentiments differ across countries and how these relate to the shifting policy landscape experienced through the pandemic. Using 2.1 billion individual-level geotagged tweets posted between 1 February 2020 and 31 March 2021, we track, monitor and map the shifts in negativity across 217 countries and unpack its relationship with COVID-19 policies. Findings reveal that there are important geographic, demographic, and socioeconomic disparities of negativity across continents, different levels of a nation's income, population density, and the level of COVID-19 infection. Countries with more stringent policies were associated with lower levels of negativity, a relationship that weakened in later phases of the pandemic. This study provides the first global and multilingual evaluation of the public's real-time mental health signals to COVID-19 at a large spatial and temporal scale. We offer an empirical framework to monitor mental health signals globally, helping international authorizations, including the United Nations and World Health Organization, to design smart country-specific mental health initiatives in response to the ongoing pandemic and future public emergencies.
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Affiliation(s)
- Siqin Wang
- School of Earth and Environmental Sciences, University of Queensland, Brisbane, Queensland, Australia
- Spatial Data Lab, Centre for Geographic Analysis, Harvard University, MA, USA
| | - Xiao Huang
- Department of Geosciences, University of Arkansas, AR, USA
- Spatial Data Lab, Centre for Geographic Analysis, Harvard University, MA, USA
| | - Tao Hu
- Department of Geography, Oklahoma State University, OK, USA
- Spatial Data Lab, Centre for Geographic Analysis, Harvard University, MA, USA
| | - Bing She
- Institute for Social Research, University of Michigan, MI, USA
| | - Mengxi Zhang
- Department of Nutrition and Health Science, Ball State University, IN, USA
- Spatial Data Lab, Centre for Geographic Analysis, Harvard University, MA, USA
| | - Ruomei Wang
- School of Earth and Environmental Sciences, University of Queensland, Brisbane, Queensland, Australia
| | - Oliver Gruebner
- Department of Geography, University of Zurich, Switzerland
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Switzerland
| | - Muhammad Imran
- Qatar Computing Research Institute, Hamad Bin Khalifa University, Qatar
| | - Jonathan Corcoran
- School of Earth and Environmental Sciences, University of Queensland, Brisbane, Queensland, Australia
| | - Yan Liu
- School of Earth and Environmental Sciences, University of Queensland, Brisbane, Queensland, Australia
| | - Shuming Bao
- China Data Institute and Future Data Lab, MI, USA
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19
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Mavragani A, Suh YK. A Comprehensive Analysis of COVID-19 Vaccine Discourse by Vaccine Brand on Twitter in Korea: Topic and Sentiment Analysis. J Med Internet Res 2023; 25:e42623. [PMID: 36603153 PMCID: PMC9891356 DOI: 10.2196/42623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 10/28/2022] [Accepted: 01/05/2023] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND The unprecedented speed of COVID-19 vaccine development and approval has raised public concern about its safety. However, studies on public discourses and opinions on social media focusing on adverse events (AEs) related to COVID-19 vaccine are rare. OBJECTIVE This study aimed to analyze Korean tweets about COVID-19 vaccines (Pfizer, Moderna, AstraZeneca, Janssen, and Novavax) after the vaccine rollout, explore the topics and sentiments of tweets regarding COVID-19 vaccines, and examine their changes over time. We also analyzed topics and sentiments focused on AEs related to vaccination using only tweets with terms about AEs. METHODS We devised a sophisticated methodology consisting of 5 steps: keyword search on Twitter, data collection, data preprocessing, data analysis, and result visualization. We used the Twitter Representational State Transfer application programming interface for data collection. A total of 1,659,158 tweets were collected from February 1, 2021, to March 31, 2022. Finally, 165,984 data points were analyzed after excluding retweets, news, official announcements, advertisements, duplicates, and tweets with <2 words. We applied a variety of preprocessing techniques that are suitable for the Korean language. We ran a suite of analyses using various Python packages, such as latent Dirichlet allocation, hierarchical latent Dirichlet allocation, and sentiment analysis. RESULTS The topics related to COVID-19 vaccines have a very large spectrum, including vaccine-related AEs, emotional reactions to vaccination, vaccine development and supply, and government vaccination policies. Among them, the top major topic was AEs related to COVID-19 vaccination. The AEs ranged from the adverse reactions listed in the safety profile (eg, myalgia, fever, fatigue, injection site pain, myocarditis or pericarditis, and thrombosis) to unlisted reactions (eg, irregular menstruation, changes in appetite and sleep, leukemia, and deaths). Our results showed a notable difference in the topics for each vaccine brand. The topics pertaining to the Pfizer vaccine mainly mentioned AEs. Negative public opinion has prevailed since the early stages of vaccination. In the sentiment analysis based on vaccine brand, the topics related to the Pfizer vaccine expressed the strongest negative sentiment. CONCLUSIONS Considering the discrepancy between academic evidence and public opinions related to COVID-19 vaccination, the government should provide accurate information and education. Furthermore, our study suggests the need for management to correct the misinformation related to vaccine-related AEs, especially those affecting negative sentiments. This study provides valuable insights into the public discourses and opinions regarding COVID-19 vaccination.
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Affiliation(s)
| | - Young-Kyoon Suh
- School of Computer Science and Engineering, Kyungpook National University, Daegu, Republic of Korea.,Department of Data Convergence Computing, Kyungpook National University, Daegu, Republic of Korea
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20
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Jing F, Li Z, Qiao S, Zhang J, Olatosi B, Li X. Using geospatial social media data for infectious disease studies: a systematic review. INTERNATIONAL JOURNAL OF DIGITAL EARTH 2023; 16:130-157. [PMID: 37997607 PMCID: PMC10664840 DOI: 10.1080/17538947.2022.2161652] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Accepted: 12/17/2022] [Indexed: 11/25/2023]
Abstract
Geospatial social media (GSM) data has been increasingly used in public health due to its rich, timely, and accessible spatial information, particularly in infectious disease research. This review synthesized 86 research articles that use GSM data in infectious diseases published between December 2013 and March 2022. These articles cover 12 infectious disease types ranging from respiratory infectious diseases to sexually transmitted diseases with spatial levels varying from the neighborhood, county, state, and country. We categorized these studies into three major infectious disease research domains: surveillance, explanation, and prediction. With the assistance of advanced statistical and spatial methods, GSM data has been widely and deeply applied to these domains, particularly in surveillance and explanation domains. We further identified four knowledge gaps in terms of contextual information use, application scopes, spatiotemporal dimension, and data limitations and proposed innovation opportunities for future research. Our findings will contribute to a better understanding of using GSM data in infectious diseases studies and provide insights into strategies for using GSM data more effectively in future research.
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Affiliation(s)
- Fengrui Jing
- Geoinformation and Big Data Research Laboratory, Department of Geography, University of South Carolina, Columbia, SC, USA
- Big Data Health Science Center, University of South Carolina, Columbia, SC, USA
| | - Zhenlong Li
- Geoinformation and Big Data Research Laboratory, Department of Geography, University of South Carolina, Columbia, SC, USA
- Big Data Health Science Center, University of South Carolina, Columbia, SC, USA
| | - Shan Qiao
- Big Data Health Science Center, University of South Carolina, Columbia, SC, USA
- Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
| | - Jiajia Zhang
- Big Data Health Science Center, University of South Carolina, Columbia, SC, USA
- Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
| | - Banky Olatosi
- Big Data Health Science Center, University of South Carolina, Columbia, SC, USA
- Department of Health Services Policy and Management, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
| | - Xiaoming Li
- Big Data Health Science Center, University of South Carolina, Columbia, SC, USA
- Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
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21
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Yin F, Crooks A, Yin L. Information propagation on cyber, relational and physical spaces about covid-19 vaccine: Using social media and splatial framework. COMPUTERS, ENVIRONMENT AND URBAN SYSTEMS 2022; 98:101887. [PMID: 36124092 PMCID: PMC9472797 DOI: 10.1016/j.compenvurbsys.2022.101887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 07/30/2022] [Accepted: 09/06/2022] [Indexed: 06/15/2023]
Abstract
With the advent of social media, human dynamics studied in purely physical space have been extended to that of a cyber and relational context. However, connections and interactions between these hybrid spaces have not been sufficiently investigated. The "space-place (Splatial)" framework proposed in recent years allows capturing human activities in the hybrid of spaces. This study applies the Splatial framework to examine the information propagation between cyber, relational, and physical spaces through a case study of Covid-19 vaccine debates in New York State (NYS). Whereby the physical space represents the regional boundaries and locations of social media (i.e., Twitter) users in NYS, the relational space indicates the social networks of these NYS users, and the cyber space captures the larger conversational context of the vaccination debate. Our results suggest that the Covid-19 vaccine debate is not polarized across all three spaces as compared to that of other vaccines. However, the rate of users with a pro-vaccine stance decreases from physical to relational and cyber spaces. We also found that while users from different spaces interact with each other, they also engage in local communications with users from the same region or same space, and distance-based and boundary-confined clusters exist in cyber and relational space communities. These results based on the Splatial framework not only shed light on the vaccination debates but also help to define and elucidate the relationships between the three spaces. The intense interactions between spaces suggest incorporating people's relational network and cyber presence in physical place-making.
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Affiliation(s)
- Fuzhen Yin
- Department of Urban and Regional Planning, University at Buffalo, Buffalo, NY, USA
| | - Andrew Crooks
- Department of Geography, University at Buffalo, Buffalo, NY, USA
| | - Li Yin
- Department of Urban and Regional Planning, University at Buffalo, Buffalo, NY, USA
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22
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Arabic Twitter Conversation Dataset about the COVID-19 Vaccine. DATA 2022. [DOI: 10.3390/data7110152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
The development and rollout of COVID-19 vaccination around the world offers hope for controlling the pandemic. People turned to social media such as Twitter seeking information or to voice their opinion. Therefore, mining such conversation can provide a rich source of data for different applications related to the COVID-19 vaccine. In this data article, we developed an Arabic Twitter dataset of 1.1 M Arabic posts regarding the COVID-19 vaccine. The dataset was streamed over one year, covering the period from January to December 2021. We considered a set of crawling keywords in the Arabic language related to the conversation about the vaccine. The dataset consists of seven databases that can be analyzed separately or merged for further analysis. The initial analysis depicts the embedded features within the posts, including hashtags, media, and the dynamic of replies and retweets. Further, the textual analysis reveals the most frequent words that can capture the trends of the discussions. The dataset was designed to facilitate research across different fields, such as social network analysis, information retrieval, health informatics, and social science.
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23
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Liu L, Tu Y, Zhou X. How local outbreak of COVID-19 affect the risk of internet public opinion: A Chinese social media case study. TECHNOLOGY IN SOCIETY 2022; 71:102113. [PMID: 36105882 PMCID: PMC9463078 DOI: 10.1016/j.techsoc.2022.102113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 09/03/2022] [Accepted: 09/03/2022] [Indexed: 06/15/2023]
Abstract
Motivated by the realistic demand of controlling the Internet public opinion risk caused by the local outbreak of COVID-19, this paper creatively proposes a COVID-19 local outbreak Internet public opinion risk grading research framework. The SMAA-FAHPSort II method combining Analytic Hierarchy Process Sort II (AHPSort II) method with Stochastic Multicriteria Acceptability Analysis (SMAA-2) method is introduced into this framework, to evaluate the Internet public opinion risk level of social media during the local outbreak of COVID-19. In addition, this framework is applied to a case of Internet public opinion risk evaluation on Microblog platform of China. According to the number of new cases per day in mainland China, this paper divides the period from May 7, 2020 to September 3, 2021 into seven stages. A total of more than 10,000 Microblog hot topics were collected, after screening and preprocessing, 5422 related topics are remained to help complete the Internet public opinion risk evaluation. The case study analysis results show that the number of days classified as moderate risk and above has reached more than 280. This proves that the local outbreak of COVID-19 will indeed increase the risk of Internet public opinion, and correlation analysis confirms that the level of public opinion risk is positively correlated with the severity of the epidemic in the real world. Furthermore, the effectiveness and advantages of the proposed method are verified by comparative analysis and sensitivity analysis. Finally, some effective public opinion management suggestions have been put forward. This paper can provide reference for the government to formulate or improve relevant strategies, and also has great significance for reducing the risk of Internet public opinion in social media.
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Affiliation(s)
- Liyi Liu
- School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan, 430070, China
| | - Yan Tu
- School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan, 430070, China
| | - Xiaoyang Zhou
- The School of Management, Xi'an Jiaotong University, Xi'an, 710049, China
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China
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24
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Zhu J, Weng F, Zhuang M, Lu X, Tan X, Lin S, Zhang R. Revealing Public Opinion towards the COVID-19 Vaccine with Weibo Data in China: BertFDA-Based Model. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph192013248. [PMID: 36293828 PMCID: PMC9602858 DOI: 10.3390/ijerph192013248] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 10/09/2022] [Accepted: 10/11/2022] [Indexed: 05/27/2023]
Abstract
The COVID-19 pandemic has created unprecedented burdens on people's health and subjective well-being. While countries around the world have established models to track and predict the affective states of COVID-19, identifying the topics of public discussion and sentiment evolution of the vaccine, particularly the differences in topics of concern between vaccine-support and vaccine-hesitant groups, remains scarce. Using social media data from the two years following the outbreak of COVID-19 (23 January 2020 to 23 January 2022), coupled with state-of-the-art natural language processing (NLP) techniques, we developed a public opinion analysis framework (BertFDA). First, using dynamic topic clustering on Weibo through the latent Dirichlet allocation (LDA) model, a total of 118 topics were generated in 24 months using 2,211,806 microblog posts. Second, by building an improved Bert pre-training model for sentiment classification, we provide evidence that public negative sentiment continued to decline in the early stages of COVID-19 vaccination. Third, by modeling and analyzing the microblog posts from the vaccine-support group and the vaccine-hesitant group, we discover that the vaccine-support group was more concerned about vaccine effectiveness and the reporting of news, reflecting greater group cohesion, whereas the vaccine-hesitant group was particularly concerned about the spread of coronavirus variants and vaccine side effects. Finally, we deployed different machine learning models to predict public opinion. Moreover, functional data analysis (FDA) is developed to build the functional sentiment curve, which can effectively capture the dynamic changes with the explicit function. This study can aid governments in developing effective interventions and education campaigns to boost vaccination rates.
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Affiliation(s)
- Jianping Zhu
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China
- Data Mining Research Center, Xiamen University, Xiamen 361005, China
- School of Management, Xiamen University, Xiamen 361005, China
| | - Futian Weng
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China
- Data Mining Research Center, Xiamen University, Xiamen 361005, China
- School of Medicine, Xiamen University, Xiamen 361005, China
| | - Muni Zhuang
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China
- Data Mining Research Center, Xiamen University, Xiamen 361005, China
- School of Medicine, Xiamen University, Xiamen 361005, China
| | - Xin Lu
- College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
| | - Xu Tan
- Career-Oriented Multidisciplinary Education Center, Shenzhen Institiute of Information Technology, Shenzhen 518172, China
| | - Songjie Lin
- Career-Oriented Multidisciplinary Education Center, Shenzhen Institiute of Information Technology, Shenzhen 518172, China
| | - Ruoyi Zhang
- Columbia College of Art and Science, George Washington University, Washington, DC 20052, USA
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Mir AA, Sevukan R. Sentiment analysis of Indian Tweets about Covid-19 vaccines. J Inf Sci 2022. [PMCID: PMC9482880 DOI: 10.1177/01655515221118049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
People are becoming more reliant on social media networks to express their opinions about various topics and obtain health information. The study is intended to explore and analyse the sentiments of Indian people related to Covid-19 vaccines as well as to visualise the top most frequently occurring terms individuals have used to communicate their ideas on Twitter about Covid-19 vaccines in India. The Tweet Archiver was used to retrieve the Tweets against ‘Covid19vaccine’ and ‘Coronavirusvaccine’ hashtags for the period of 2 months 18 days (4 January 2021–22 March 2021). After collecting data, the Orange software and VOSviewer were used for further analysis. The Tweets were posted across the country, with an immense contribution from Maharashtra (223, 15.58%), followed by Delhi (220, 15.37%) and Tamil Nadu (73, 5.10%). The majority (639, 44.65%) of the Tweets reflect positive sentiments, followed by neutral (521, 38.50%) and negative (241, 16.84%) sentiments, respectively. This signifies that most Twitter users have a favourable opinion towards Covid vaccines in India. Based on the relevance score of the words, the words ‘Delhi heart’, ‘Lung institute’, ‘Gift’, ‘Unite2fightcorona’, and ‘Covid-19 Vaccine’ are the leading words appearing in Tweets. The study illustrates the sentiments of the Indian people towards ‘Covid-19 vaccines’, gains some insights into overall public communication about the topic and complements the existing literature. It can assist health policymakers and administrators in better understanding the polarity (positive, negative, and neutral) of Tweets about Covid-19 vaccines on Twitter to raise public awareness about health concerns and misinformation about the vaccine.
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Affiliation(s)
- Aasif Ahmad Mir
- Department of Library and Information Science, Pondicherry University, India
| | - Rathinam Sevukan
- Department of Library and Information Science, Pondicherry University, India
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Huang X, Wang S, Zhang M, Hu T, Hohl A, She B, Gong X, Li J, Liu X, Gruebner O, Liu R, Li X, Liu Z, Ye X, Li Z. Social media mining under the COVID-19 context: Progress, challenges, and opportunities. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION : ITC JOURNAL 2022; 113:102967. [PMID: 36035895 PMCID: PMC9391053 DOI: 10.1016/j.jag.2022.102967] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Revised: 06/17/2022] [Accepted: 08/05/2022] [Indexed: 05/21/2023]
Abstract
Social media platforms allow users worldwide to create and share information, forging vast sensing networks that allow information on certain topics to be collected, stored, mined, and analyzed in a rapid manner. During the COVID-19 pandemic, extensive social media mining efforts have been undertaken to tackle COVID-19 challenges from various perspectives. This review summarizes the progress of social media data mining studies in the COVID-19 contexts and categorizes them into six major domains, including early warning and detection, human mobility monitoring, communication and information conveying, public attitudes and emotions, infodemic and misinformation, and hatred and violence. We further document essential features of publicly available COVID-19 related social media data archives that will benefit research communities in conducting replicable and reproducible studies. In addition, we discuss seven challenges in social media analytics associated with their potential impacts on derived COVID-19 findings, followed by our visions for the possible paths forward in regard to social media-based COVID-19 investigations. This review serves as a valuable reference that recaps social media mining efforts in COVID-19 related studies and provides future directions along which the information harnessed from social media can be used to address public health emergencies.
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Affiliation(s)
- Xiao Huang
- Department of Geosciences, University of Arkansas, Fayetteville, AR 72701, USA
| | - Siqin Wang
- School of Earth Environmental Sciences, University of Queensland, Brisbane, Queensland 4076, Australia
| | - Mengxi Zhang
- Department of Nutrition and Health Science, Ball State University, Muncie, IN 47304, USA
| | - Tao Hu
- Department of Geography, Oklahoma State University, Stillwater, OK 74078, USA
| | - Alexander Hohl
- Department of Geography, The University of Utah, Salt Lake City, UT 84112, USA
| | - Bing She
- Institute for social research, University of Michigan, Ann Arbor, MI 48109, USA
| | - Xi Gong
- Department of Geography & Environmental Studies, University of New Mexico, Albuquerque, NM 87131, USA
| | - Jianxin Li
- School of Information Technology, Deakin University, Geelong, Victoria 3220, Australia
| | - Xiao Liu
- School of Information Technology, Deakin University, Geelong, Victoria 3220, Australia
| | - Oliver Gruebner
- Department of Geography, University of Zurich, Zürich CH-8006, Switzerland
| | - Regina Liu
- Department of Biology, Mercer University, Macon, GA 31207, USA
| | - Xiao Li
- Texas A&M Transportation Institute, Bryan, TX 77807, USA
| | - Zhewei Liu
- Department of Land Surveying and Geo-informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China
| | - Xinyue Ye
- Department of Landscape Architecture and Urban Planning, Texas A&M University, College Station, TX 77840, USA
| | - Zhenlong Li
- Geoinformation and Big Data Research Lab, Department of Geography, University of South Carolina, Columbia, SC 29208, USA
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Ljajić A, Prodanović N, Medvecki D, Bašaragin B, Mitrović J. Uncovering the Reasons behind COVID-19 Vaccine Hesitancy in Serbia: Sentiment-Based Topic Modeling (Preprint). J Med Internet Res 2022; 24:e42261. [DOI: 10.2196/42261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 09/29/2022] [Accepted: 09/29/2022] [Indexed: 11/13/2022] Open
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Choi YJ, Lee J, Paek SY. Public Awareness and Sentiment toward COVID-19 Vaccination in South Korea: Findings from Big Data Analytics. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19169914. [PMID: 36011550 PMCID: PMC9407697 DOI: 10.3390/ijerph19169914] [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: 07/16/2022] [Revised: 08/05/2022] [Accepted: 08/10/2022] [Indexed: 05/17/2023]
Abstract
Despite a worldwide campaign to promote vaccination, South Korea is facing difficulties in increasing its vaccination rate due to negative perceptions of the vaccines and vaccination policies. This study investigated South Koreans' awareness of and sentiments toward vaccination. Particularly, this study explored how public opinions have developed over time, and compared them to those of other nations. We used Pfizer, Moderna, Janssen, and AstraZeneca as keywords on Naver, Daum, Google, and Twitter to collect data on public awareness and sentiments toward the vaccines and the government's vaccination policies. The results showed that South Koreans' sentiments on vaccination changed from neutral to negative to positive over the past two years. In particular, public sentiments turned positive due to South Koreans' hopeful expectations and a high vaccination rate. Overall, the attitudes and sentiments toward vaccination in South Korea were similar to those of other nations. The conspiracy theories surrounding the vaccines had a significant effect on the negative opinions in other nations, but had little impact on South Korea.
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Affiliation(s)
- Yeon-Jun Choi
- Department of Aviation Security Protection, Kwangju Women’s University, Gwangju 62396, Korea
| | - Julak Lee
- Department of Industrial Security, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 06911, Korea
- Correspondence:
| | - Seung Yeop Paek
- Department of Criminal Justice, California State University, East Bay, SF-428, Hayward, CA 94542, USA
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Alhuzali H, Zhang T, Ananiadou S. A comparative geolocation and text mining analysis of emotions and topics during the COVID-19 Pandemic in the UK. J Med Internet Res 2022; 24:e40323. [PMID: 36150046 PMCID: PMC9536769 DOI: 10.2196/40323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 08/06/2022] [Accepted: 08/10/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND In recent years, the COVID-19 pandemic has brought great changes to public health, society and the economy. Social media provides a platform for people to discuss health concerns, living conditions and policies during the epidemic, which allows policy makers to use its contents to analyse the public emotions and attitudes for decision making. OBJECTIVE In this study, we aim to use deep learning-based methods to understand public emotions on topics related to the COVID-19 pandemic in the UK through a comparative geolocation and text mining analysis on Twitter. METHODS Over 500,000 tweets related to COVID-19 from 48 different cities in the UK were extracted, and the data cover the period of the last 2 years (from February 2020 to November 2021). We leveraged three advanced deep learning-based models: SenticNet 6 for sentiment analysis, SpanEmo for emotion recognition, and Combined Topic Modelling (CTM) for topic modelling to geospatially analyse the sentiment, emotion and topics of tweets in the UK. RESULTS According to the analysis, we observed a significant change in the number of tweets as the epidemiological situation and vaccination these two years. There was a sharp increase in the number of tweets from January 2020 to February 2020 due to the outbreak of COVID-19 in the UK. Then, the number of tweets gradually declined from February 2020. Moreover, with the identification of the COVID-19 Omicron variant in the UK in November 2021, the number of tweets grew. Our findings reveal people's attitudes and emotions towards topics related to COVID-19. For sentiment, about 60% of tweets are positive, 20% neutral and 20% are negative. For emotion, people tend to express highly positive emotions in the beginning of 2020, while expressing highly negative emotions as the time changes towards the end of 2021. The topics are also changing during the pandemic. CONCLUSIONS Through large scale text mining of Twitter, our study found that there were meaningful differences in public emotions and topics regarding the COVID-19 pandemic among different UK cities. Furthermore, efficient location-based and time-based comparative analysis can be used to track people's thoughts, feelings and understand their behaviours. Based on our analysis, positive attitudes were common during the pandemic; optimism and anticipation were the dominant emotions. With the outbreak epidemiological change, the government developed control measures, vaccination policies and the topics also shifted over time. Overall, the proportion and expressions of emojis, sentiments, emotions and topics varied geographically and temporally. Therefore, our approach of exploring public emotions and topics on the pandemic from Twitter can potentially lead to informing how public policies are received in a particular geographical area.
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Affiliation(s)
- Hassan Alhuzali
- College of Computers and Information Systems, Umm Al-Qura University, SA., Makkah, SA
| | - Tianlin Zhang
- Department of Computer Science, The University of Manchester, National Centre for Text Mining, Manchester, UK, National Centre for Text Mining Manchester Institute of Biotechnology 131 Princess Street Manchester M1 7DN UK, Manchester, GB
| | - Sophia Ananiadou
- Department of Computer Science, The University of Manchester, National Centre for Text Mining, Manchester, UK, National Centre for Text Mining Manchester Institute of Biotechnology 131 Princess Street Manchester M1 7DN UK, Manchester, GB
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Hsu JTH, Tsai RTH. Increased Online Aggression During COVID-19 Lockdowns: Two-Stage Study of Deep Text Mining and Difference-in-Differences Analysis. J Med Internet Res 2022; 24:e38776. [PMID: 35943771 PMCID: PMC9364970 DOI: 10.2196/38776] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Revised: 06/19/2022] [Accepted: 06/23/2022] [Indexed: 01/14/2023] Open
Abstract
BACKGROUND The COVID-19 pandemic caused a critical public health crisis worldwide, and policymakers are using lockdowns to control the virus. However, there has been a noticeable increase in aggressive social behaviors that threaten social stability. Lockdown measures might negatively affect mental health and lead to an increase in aggressive emotions. Discovering the relationship between lockdown and increased aggression is crucial for formulating appropriate policies that address these adverse societal effects. We applied natural language processing (NLP) technology to internet data, so as to investigate the social and emotional impacts of lockdowns. OBJECTIVE This research aimed to understand the relationship between lockdown and increased aggression using NLP technology to analyze the following 3 kinds of aggressive emotions: anger, offensive language, and hate speech, in spatiotemporal ranges of tweets in the United States. METHODS We conducted a longitudinal internet study of 11,455 Twitter users by analyzing aggressive emotions in 1,281,362 tweets they posted from 2019 to 2020. We selected 3 common aggressive emotions (anger, offensive language, and hate speech) on the internet as the subject of analysis. To detect the emotions in the tweets, we trained a Bidirectional Encoder Representations from Transformers (BERT) model to analyze the percentage of aggressive tweets in every state and every week. Then, we used the difference-in-differences estimation to measure the impact of lockdown status on increasing aggressive tweets. Since most other independent factors that might affect the results, such as seasonal and regional factors, have been ruled out by time and state fixed effects, a significant result in this difference-in-differences analysis can not only indicate a concrete positive correlation but also point to a causal relationship. RESULTS In the first 6 months of lockdown in 2020, aggression levels in all users increased compared to the same period in 2019. Notably, users under lockdown demonstrated greater levels of aggression than those not under lockdown. Our difference-in-differences estimation discovered a statistically significant positive correlation between lockdown and increased aggression (anger: P=.002, offensive language: P<.001, hate speech: P=.005). It can be inferred from such results that there exist causal relations. CONCLUSIONS Understanding the relationship between lockdown and aggression can help policymakers address the personal and societal impacts of lockdown. Applying NLP technology and using big data on social media can provide crucial and timely information for this effort.
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Affiliation(s)
- Jerome Tze-Hou Hsu
- Center for Geographic Information Science, Research Center for Humanities and Social Sciences, Academia Sinica, Taipei, Taiwan.,Taipei Municipal Jianguo High School, Taipei, Taiwan
| | - Richard Tzong-Han Tsai
- Center for Geographic Information Science, Research Center for Humanities and Social Sciences, Academia Sinica, Taipei, Taiwan.,Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan
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31
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Hu S, Xiong C, Li Q, Wang Z, Jiang Y. COVID-19 vaccine hesitancy cannot fully explain disparities in vaccination coverage across the contiguous United States. Vaccine 2022; 40:5471-5482. [PMID: 35953322 PMCID: PMC9359480 DOI: 10.1016/j.vaccine.2022.07.051] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Revised: 07/25/2022] [Accepted: 07/27/2022] [Indexed: 11/27/2022]
Abstract
Vaccine hesitancy has been identified as a major obstacle preventing comprehensive coverage against the COVID-19 pandemic. However, few studies have analyzed the association between ex-ante vaccine hesitancy and ex-post vaccination coverage. This study leveraged one-year county-level data across the contiguous United States to examine whether the prospective vaccine hesitancy eventually translates into differential vaccination rates, and whether vaccine hesitancy can explain socioeconomic, racial, and partisan disparities in vaccine uptake. A set of structural equation modeling was fitted with vaccine hesitancy and vaccination rate as endogenous variables, controlling for various potential confounders. The results demonstrated a significant negative link between vaccine hesitancy and vaccination rate, with the difference between the two continuously widening over time. Counties with higher socioeconomic statuses, more Asian and Hispanic populations, more elderly residents, greater health insurance coverage, and more Democrats presented lower vaccine hesitancy and higher vaccination rates. However, underlying determinants of vaccination coverage and vaccine hesitancy were divergent regarding their different associations with exogenous variables. Mediation analysis further demonstrated that indirect effects from exogenous variables to vaccination coverage via vaccine hesitancy only partially explained corresponding total effects, challenging the popular narrative that portrays vaccine hesitancy as a root cause of disparities in vaccination. Our study highlights the need of well-funded, targeted, and ongoing initiatives to reduce persisting vaccination inequities.
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Affiliation(s)
- Songhua Hu
- Maryland Transportation Institute (MTI), Department of Civil and Environmental Engineering, University of Maryland, College Park, MD 20742, United States
| | - Chenfeng Xiong
- Department of Civil and Environmental Engineering, Villanova University, PA 19085, USA.
| | - Qingchen Li
- Department of Civil and Environmental Engineering, Villanova University, PA 19085, USA
| | - Zitong Wang
- Department of Civil and Environmental Engineering, Villanova University, PA 19085, USA
| | - Yuan Jiang
- Department of Planning, Chengdu Institute of Planning & Design, Chengdu, China
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32
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Gao Z, Wang S, Gu J, Gu C, Liu R. A community-level study on COVID-19 transmission and policy interventions in Wuhan, China. CITIES (LONDON, ENGLAND) 2022; 127:103745. [PMID: 35582597 PMCID: PMC9098919 DOI: 10.1016/j.cities.2022.103745] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2020] [Revised: 04/28/2022] [Accepted: 05/08/2022] [Indexed: 05/14/2023]
Abstract
The specific factors and response strategies that affect COVID-19 transmission in local communities remain under-explored in the current literature due to a lack of data. Based on primary COVID-19 data collected at the community level in Wuhan, China, our study contributes a community-level investigation on COVID-19 transmission and response strategies by addressing two research questions: 1) What community factors are associated with viral transmission? and 2) What are the key mechanisms behind policy interventions towards controlling viral transmission within local communities? We conducted two sets of analyses to address these two questions-quantitative analyses of the relationship between community factors and viral transmission and qualitative analyses of policy interventions on community transmission. Our findings show that the viral spread in local communities is irrelevant to the built environment of a community and its socioeconomic position but is related to its demographic composition. Specifically, groups under the age of 18 play an important role in viral transmission. Moreover, a series of community shutdown management initiatives (e.g., group buying, delivering supplies, and self-reporting of health conditions) play an important role in curbing viral transmission at the local level that can be applied to other geographic contexts.
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Affiliation(s)
- Zhe Gao
- Hubei Provincial Key Laboratory for Geographical Process Analysis & Simulation, Central China Normal University, Wuhan, Hubei Province 430079, China
| | - Siqin Wang
- School of Earth and Environmental Sciences, University of Queensland, Brisbane 4067, Australia
| | - Jiang Gu
- Hubei Provincial Key Laboratory for Geographical Process Analysis & Simulation, Central China Normal University, Wuhan, Hubei Province 430079, China
| | - Chaolin Gu
- School of Architecture, Tsinghua University, Beijing 100084, China
| | - Regina Liu
- Department of Biology, Mercer University, Macon, GA, USA
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Vaccine discourse during the onset of the COVID-19 pandemic: Topical structure and source patterns informing efforts to combat vaccine hesitancy. PLoS One 2022; 17:e0271394. [PMID: 35895626 PMCID: PMC9328525 DOI: 10.1371/journal.pone.0271394] [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: 10/04/2021] [Accepted: 06/29/2022] [Indexed: 11/19/2022] Open
Abstract
Background Understanding public discourse about a COVID-19 vaccine in the early phase of the COVID-19 pandemic may provide key insights concerning vaccine hesitancy. However, few studies have investigated the communicative patterns in which Twitter users participate discursively in vaccine discussions. Objectives This study aims to investigate 1) the major topics that emerged from public conversation on Twitter concerning vaccines for COVID-19, 2) the topics that were emphasized in tweets with either positive or negative sentiment toward a COVID-19 vaccine, and 3) the type of online accounts in which tweets with either positive or negative sentiment were more likely to circulate. Methods We randomly extracted a total of 349,979 COVID-19 vaccine-related tweets from the initial period of the pandemic. Out of 64,216 unique tweets, a total of 23,133 (36.03%) tweets were classified as positive and 14,051 (21.88%) as negative toward a COVID-19 vaccine. We conducted Structural Topic Modeling and Network Analysis to reveal the distinct topical structure and connection patterns that characterize positive and negative discourse toward a COVID-19 vaccine. Results Our STM analysis revealed the most prominent topic emerged on Twitter of a COVID-19 vaccine was “other infectious diseases”, followed by “vaccine safety concerns”, and “conspiracy theory.” While the positive discourse demonstrated a broad range of topics such as “vaccine development”, “vaccine effectiveness”, and “safety test”, negative discourse was more narrowly focused on topics such as “conspiracy theory” and “safety concerns.” Beyond topical differences, positive discourse was more likely to interact with verified sources such as scientists/medical sources and the media/journalists, whereas negative discourse tended to interact with politicians and online influencers. Conclusions Positive and negative discourse was not only structured around distinct topics but also circulated within different networks. Public health communicators need to address specific topics of public concern in varying information hubs based on audience segmentation, potentially increasing COVID-19 vaccine uptake.
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Song C, Yin H, Shi X, Xie M, Yang S, Zhou J, Wang X, Tang Z, Yang Y, Pan J. Spatiotemporal disparities in regional public risk perception of COVID-19 using Bayesian Spatiotemporally Varying Coefficients (STVC) series models across Chinese cities. INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION : IJDRR 2022; 77:103078. [PMID: 35664453 PMCID: PMC9148270 DOI: 10.1016/j.ijdrr.2022.103078] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 05/24/2022] [Accepted: 05/24/2022] [Indexed: 05/11/2023]
Abstract
Regional public attention has been critical during the COVID-19 pandemic, impacting the effectiveness of sub-national non-pharmaceutical interventions. While studies have focused on public attention at the national level, sub-national public attention has not been well investigated. Understanding sub-national public attention can aid local governments in designing regional scientific guidelines, especially in large countries with substantial spatiotemporal disparities in the spread of infections. Here, we evaluated the online public attention to the COVID-19 pandemic using internet search data and developed a regional public risk perception index (PRPI) that depicts heterogeneous associations between local pandemic risk and public attention across 366 Chinese cities. We used the Bayesian Spatiotemporally Varying Coefficients (STVC) model, a full-map local regression for estimating spatiotemporal heterogeneous relationships of variables, and improved it to the Bayesian Spatiotemporally Interacting Varying Coefficients (STIVC) model to incorporate space-time interaction non-stationarity at spatial or temporal stratified scales. COVID-19 daily cases (median contribution 82.6%) was the most critical factor affecting public attention, followed by urban socioeconomic conditions (16.7%) and daily population mobility (0.7%). After adjusting national and provincial impacts, city-level influence factors accounted for 89.4% and 58.6% in spatiotemporal variations of public attention. Spatiotemporal disparities were substantial among cities and provinces, suggesting that observing national-level public dynamics alone was insufficient. Multi-period PRPI maps revealed clusters and outlier cities with potential public panic and low health literacy. Bayesian STVC series models are systematically proposed and provide a multi-level spatiotemporal heterogeneous analytical framework for understanding collective human responses to major public health emergencies and disasters.
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Affiliation(s)
- Chao Song
- HEOA Group, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, 610044, China
- Department of Geography, Dartmouth College, Hanover, NH, 03755, USA
- Institute for Healthy Cities and West China Research Centre for Rural Health Development, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Hao Yin
- Department of Economics, University of Southern California, CA, 90089, USA
- School of Population and Public Health, University of British Columbia, BC, V6T 1Z3, Canada
| | - Xun Shi
- Department of Geography, Dartmouth College, Hanover, NH, 03755, USA
| | - Mingyu Xie
- Institute for Healthy Cities and West China Research Centre for Rural Health Development, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Shujuan Yang
- HEOA Group, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, 610044, China
| | - Junmin Zhou
- HEOA Group, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, 610044, China
| | - Xiuli Wang
- HEOA Group, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, 610044, China
- Institute for Healthy Cities and West China Research Centre for Rural Health Development, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Zhangying Tang
- State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, School of Geoscience and Technology, Southwest Petroleum University, Chengdu, Sichuan, 610500, China
| | - Yili Yang
- Institute for Healthy Cities and West China Research Centre for Rural Health Development, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Jay Pan
- HEOA Group, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, 610044, China
- Institute for Healthy Cities and West China Research Centre for Rural Health Development, Sichuan University, Chengdu, Sichuan, 610041, China
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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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Chen Q, Crooks A. Analyzing the vaccination debate in social media data Pre- and Post-COVID-19 pandemic. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION : ITC JOURNAL 2022; 110:102783. [PMID: 35528967 PMCID: PMC9069236 DOI: 10.1016/j.jag.2022.102783] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 04/02/2022] [Accepted: 04/09/2022] [Indexed: 06/14/2023]
Abstract
The COVID-19 virus has caused and continues to cause unprecedented impacts on the life trajectories of millions of people globally. Recently, to combat the transmission of the virus, vaccination campaigns around the world have become prevalent. However, while many see such campaigns as positive (e.g., protecting lives), others see them as negative (e.g., the side effects that are not fully understood scientifically), resulting in diverse sentiments towards vaccination campaigns. In addition, the diverse sentiments have seldom been systematically quantified let alone their dynamic changes over space and time. To shed light on this issue, we propose an approach to analyze vaccine sentiments in space and time by using supervised machine learning combined with word embedding techniques. Taking the United States as a test case, we utilize a Twitter dataset (approximately 11.7 million tweets) from January 2015 to July 2021 and measure and map vaccine sentiments (Pro-vaccine, Anti-vaccine, and Neutral) across the nation. In doing so, we can capture the heterogeneous public opinions within social media discussions regarding vaccination among states. Results show how positive sentiment in social media has a strong correlation with the actual vaccinated population. Furthermore, we introduce a simple ratio between Anti and Pro-vaccine as a proxy to quantify vaccine hesitancy and show how our results align with other traditional survey approaches. The proposed approach illustrates the potential to monitor the dynamics of vaccine opinion distribution online, which we hope, can be helpful to explain vaccination rates for the ongoing COVID-19 pandemic.
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Affiliation(s)
- Qingqing Chen
- Department of Geography, University at Buffalo, Buffalo, NY, USA
| | - Andrew Crooks
- Department of Geography, University at Buffalo, Buffalo, NY, USA
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Cascini F, Pantovic A, Al-Ajlouni YA, Failla G, Puleo V, Melnyk A, Lontano A, Ricciardi W. Social media and attitudes towards a COVID-19 vaccination: A systematic review of the literature. EClinicalMedicine 2022; 48:101454. [PMID: 35611343 PMCID: PMC9120591 DOI: 10.1016/j.eclinm.2022.101454] [Citation(s) in RCA: 88] [Impact Index Per Article: 44.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 04/24/2022] [Accepted: 04/27/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Vaccine hesitancy continues to limit global efforts in combatting the COVID-19 pandemic. Emerging research demonstrates the role of social media in disseminating information and potentially influencing people's attitudes towards public health campaigns. This systematic review sought to synthesize the current evidence regarding the potential role of social media in shaping COVID-19 vaccination attitudes, and to explore its potential for shaping public health interventions to address the issue of vaccine hesitancy. METHODS We performed a systematic review of the studies published from inception to 13 of March2022 by searching PubMed, Web of Science, Embase, PsychNET, Scopus, CINAHL, and MEDLINE. Studies that reported outcomes related to coronavirus disease 2019 (COVID-19) vaccine (attitudes, opinion, etc.) gathered from the social media platforms, and those analyzing the relationship between social media use and COVID-19 hesitancy/acceptance were included. Studies that reported no outcome of interest or analyzed data from sources other than social media (websites, newspapers, etc.) will be excluded. The Newcastle Ottawa Scale (NOS) was used to assess the quality of all cross-sectional studies included in this review. This study is registered with PROSPERO (CRD42021283219). FINDINGS Of the 2539 records identified, a total of 156 articles fully met the inclusion criteria. Overall, the quality of the cross-sectional studies was moderate - 2 studies received 10 stars, 5 studies received 9 stars, 9 studies were evaluated with 8, 12 studies with 7,16 studies with 6, 11 studies with 5, and 6 studies with 4 stars. The included studies were categorized into four categories. Cross-sectional studies reporting the association between reliance on social media and vaccine intentions mainly observed a negative relationship. Studies that performed thematic analyses of extracted social media data, mainly observed a domination of vaccine hesitant topics. Studies that explored the degree of polarization of specific social media contents related to COVID-19 vaccines observed a similar degree of content for both positive and negative tone posted on different social media platforms. Finally, studies that explored the fluctuations of vaccination attitudes/opinions gathered from social media identified specific events as significant cofactors that affect and shape vaccination intentions of individuals. INTERPRETATION This thorough examination of the various roles social media can play in disseminating information to the public, as well as how individuals behave on social media in the context of public health events, articulates the potential of social media as a platform of public health intervention to address vaccine hesitancy. FUNDING None.
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Affiliation(s)
- Fidelia Cascini
- Department of Life Sciences and Public Health, Section of Hygiene and Public Health, Università Cattolica del Sacro Cuore, L.go Francesco Vito 1, Rome 00168, Italy
- Corresponding author.
| | - Ana Pantovic
- Faculty of Biology, University of Belgrade, Belgrade, Serbia
| | | | - Giovanna Failla
- Department of Life Sciences and Public Health, Section of Hygiene and Public Health, Università Cattolica del Sacro Cuore, L.go Francesco Vito 1, Rome 00168, Italy
| | - Valeria Puleo
- Department of Life Sciences and Public Health, Section of Hygiene and Public Health, Università Cattolica del Sacro Cuore, L.go Francesco Vito 1, Rome 00168, Italy
| | - Andriy Melnyk
- Department of Life Sciences and Public Health, Section of Hygiene and Public Health, Università Cattolica del Sacro Cuore, L.go Francesco Vito 1, Rome 00168, Italy
| | - Alberto Lontano
- Department of Life Sciences and Public Health, Section of Hygiene and Public Health, Università Cattolica del Sacro Cuore, L.go Francesco Vito 1, Rome 00168, Italy
| | - Walter Ricciardi
- Department of Life Sciences and Public Health, Section of Hygiene and Public Health, Università Cattolica del Sacro Cuore, L.go Francesco Vito 1, Rome 00168, Italy
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Sauvayre R, Vernier J, Chauvière C. Using supervised learning to analyze the French vaccine debate on Twitter. JMIR Med Inform 2022; 10:e37831. [PMID: 35512274 PMCID: PMC9116457 DOI: 10.2196/37831] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 05/01/2022] [Accepted: 05/04/2022] [Indexed: 11/13/2022] Open
Abstract
Background As the COVID-19 pandemic progressed, disinformation, fake news, and conspiracy theories spread through many parts of society. However, the disinformation spreading through social media is, according to the literature, one of the causes of increased COVID-19 vaccine hesitancy. In this context, the analysis of social media posts is particularly important, but the large amount of data exchanged on social media platforms requires specific methods. This is why machine learning and natural language processing models are increasingly applied to social media data. Objective The aim of this study is to examine the capability of the CamemBERT French-language model to faithfully predict the elaborated categories, with the knowledge that tweets about vaccination are often ambiguous, sarcastic, or irrelevant to the studied topic. Methods A total of 901,908 unique French-language tweets related to vaccination published between July 12, 2021, and August 11, 2021, were extracted using Twitter’s application programming interface (version 2; Twitter Inc). Approximately 2000 randomly selected tweets were labeled with 2 types of categorizations: (1) arguments for (pros) or against (cons) vaccination (health measures included) and (2) type of content (scientific, political, social, or vaccination status). The CamemBERT model was fine-tuned and tested for the classification of French-language tweets. The model’s performance was assessed by computing the F1-score, and confusion matrices were obtained. Results The accuracy of the applied machine learning reached up to 70.6% for the first classification (pro and con tweets) and up to 90% for the second classification (scientific and political tweets). Furthermore, a tweet was 1.86 times more likely to be incorrectly classified by the model if it contained fewer than 170 characters (odds ratio 1.86; 95% CI 1.20-2.86). Conclusions The accuracy of the model is affected by the classification chosen and the topic of the message examined. When the vaccine debate is jostled by contested political decisions, tweet content becomes so heterogeneous that the accuracy of the model drops for less differentiated classes. However, our tests showed that it is possible to improve the accuracy by selecting tweets using a new method based on tweet length.
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Affiliation(s)
- Romy Sauvayre
- Laboratoire de Psychologie Sociale et Cognitive, Université Clermont Auvergne, CNRS, Clermont-Ferrand, FR.,Polytech Clermont, 2 avenue Blaise Pascal, Aubiere, FR
| | - Jessica Vernier
- Laboratoire de Psychologie Sociale et Cognitive, Université Clermont Auvergne, CNRS, Clermont-Ferrand, FR
| | - Cédric Chauvière
- Laboratoire de Mathématiques Blaise Pascal, Université Clermont Auvergne, CNRS, Clermont-Ferrand, FR.,Polytech Clermont, 2 avenue Blaise Pascal, Aubiere, FR
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Yang G, Wang Z, Chen L. Investigating the Public Sentiment in Major Public Emergencies Through the Complex Networks Method: A Case Study of COVID-19 Epidemic. Front Public Health 2022; 10:847161. [PMID: 35425751 PMCID: PMC9002016 DOI: 10.3389/fpubh.2022.847161] [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: 01/05/2022] [Accepted: 02/28/2022] [Indexed: 11/14/2022] Open
Abstract
The main purpose of this study is to investigate what topic indicators correlate with public sentiment during “coronavirus disease 2019 (COVID-19) epidemic” and which indicators control the complex networks of the topic indicators. We obtained 68,098 Weibo, categorized them into 11 topic indicators, and grouped these indicators into three dimensions. Then, we constructed the complex networks model of Weibo's topics and examined the key indicators affecting the public's sentiment during the major public emergency. The results showed that “positive emotion” is positively correlated with “recordings of epidemic” and “foreign comparisons,” while “negative emotion” is negatively correlated with “government image,” “recordings of epidemic,” and “asking for help online.” In addition, the two vertexes of “recordings of epidemic” and “foreign comparisons” are the most important “bridges” which connect the government and the public. The “recordings of epidemic” is the main connection “hub” between the government and the media. In other words, the “recordings of epidemic” is the central topic indicator that controls the entire topic network. In conclusion, the government should publish the advance of the events through official media on time and transparent way and create a platform where everyone can speak directly to the government for advice and assistance during a major public emergency in the future.
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Affiliation(s)
- Guang Yang
- School of Education Science, Jiangsu Normal University, Xuzhou, China
| | - Zhidan Wang
- School of Education Science, Jiangsu Normal University, Xuzhou, China
| | - Lin Chen
- School of Education Science, Jiangsu Normal University, Xuzhou, China
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Spatio-Temporal Patterns of Fitness Behavior in Beijing Based on Social Media Data. SUSTAINABILITY 2022. [DOI: 10.3390/su14074106] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Fitness is an important way to ensure the health of the population, and it is important to actively understand fitness behavior. Although social media Weibo data (the Chinese Tweeter) can provide multidimensional information in terms of objectivity and generalizability, there is still more latent potential to tap. Based on Sina Weibo social media data in the year 2017, this study was conducted to explore the spatial and temporal patterns of urban residents’ different fitness behaviors and related influencing factors within the Fifth Ring Road of Beijing. FastAI, LDA, geodetector technology, and GIS spatial analysis methods were employed in this study. It was found that fitness behaviors in the study area could be categorized into four types. Residents can obtain better fitness experiences in sports venues. Different fitness types have different polycentric spatial distribution patterns. The residents’ fitness frequency shows an obvious periodic distribution (weekly and 24 h). The spatial distribution of the fitness behavior of residents is mainly affected by factors, such as catering services, education and culture, companies, and public facilities. This research could help to promote the development of urban residents’ fitness in Beijing.
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Xavier T, Lambert J. Sentiment and emotion trends in nurses' tweets about the COVID-19 pandemic. J Nurs Scholarsh 2022; 54:613-622. [PMID: 35343050 PMCID: PMC9115286 DOI: 10.1111/jnu.12775] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 01/21/2022] [Accepted: 03/04/2022] [Indexed: 01/09/2023]
Abstract
PURPOSE Twitter is being increasingly used by nursing professionals to share ideas, information, and opinions about the global pandemic, yet there continues to be a lack of research on how nurse sentiment is associated with major events happening on the frontline. The purpose of the study was to quantitatively identify sentiments, emotions, and trends in nurses' tweets and to explore the variations in sentiments and emotions over a period in 2020 with respect to the number of cases and deaths of COVID-19 worldwide. DESIGN A cross-sectional data mining study was held from March 3, 2020 through December 3, 2020. The tweets related to COVID-19 were downloaded using the tweet IDs available from a public website. Data were processed and filtered by searching for keywords related to nursing in the profile description field using the R software and JMP Pro Version 16 and the sentiment analysis of each tweet was done using AFINN, Bing, and NRC lexicon. FINDINGS A total of 13,868 tweets from the Twitter accounts of self-identified nurses were included in the final analysis. The sentiment scores of nurses' tweets fluctuated over time and some clear patterns emerged related to the number of COVID-19 cases and deaths. Joy decreased and sadness increased over time as the pandemic impacts increased. CONCLUSIONS Our study shows that Twitter data can be leveraged to study the emotions and sentiments of nurses, and the findings suggest that the emotional realm of nurses was affected during the COVID-19 pandemic according to the emotional trends observed in tweets. CLINICAL RELEVANCE The study provides insight into what nurses are feeling, and findings from this study highlight the importance of developing and implementing interventions targeted at nurses at the workplace to prevent mental health consequences.
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Affiliation(s)
- Teenu Xavier
- PhD Candidate, College of Nursing, University of Cincinnati, Cincinnati, Ohio, USA
| | - Joshua Lambert
- Assistant Professor, Biostatistician, College of Nursing, University of Cincinnati, Cincinnati, Ohio, USA
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Kim M, Noh Y, Yamada A, Hong SH. Comparison of the Erectile Dysfunction Drugs Sildenafil and Tadalafil Using Patient Medication Reviews: Topic Modeling Study. JMIR Med Inform 2022; 10:e32689. [PMID: 35225813 PMCID: PMC8922152 DOI: 10.2196/32689] [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: 08/06/2021] [Revised: 10/22/2021] [Accepted: 11/17/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Topic modeling of patient medication reviews of erectile dysfunction (ED) drugs can help identify patient preferences regarding ED treatment options. The identification of a set of topics important to the patient from social network service drug reviews would inform the design of patient-centered medication counseling. OBJECTIVE This study aimed to (1) identify the distinctive topics from patient medication reviews unique to tadalafil versus sildenafil; (2) determine if the primary topics are distributed differently for each drug and for each patient characteristic (age and time on ED drug therapy); and (3) test if the primary topics affect satisfaction with ED drug therapy controlling for patient characteristics. METHODS Data were collected from the patient medication reviews of sildenafil and tadalafil posted on WebMD and Ask a Patient. The latent Dirichlet allocation method of natural language processing was used to identify 5 distinctive topics from the patient medication reviews on each drug. Analysis of variance and a 2-sample t test were conducted to compare the topic distribution and assess whether patient satisfaction varies with the primary topics, age, and time on medication for each ED drug. Statistical significance was tested at an alpha of .05. RESULTS The patient medication reviews of sildenafil (N=463) had 2 topics on treatment benefit and 1 each on medication safety, marketing claim, and treatment comparison, while the patient medication reviews of tadalafil (N=919) had 2 topics on medication safety and 1 each on the remaining subjects. Sildenafil's reviewers quite frequently (94/463, 20.4%) mentioned erection sustainability as their primary topic, whereas tadalafil's reviewers were more concerned about severe medication safety. Those who mentioned erection sustainability as their primary topic were quite satisfied with their treatment as opposed to those who mentioned severe medication safety as their primary topic (score 3.85 vs 2.44). The discovered topics reflected the marketing claims of blue magic and amber romance for sildenafil and tadalafil, respectively. The topic of blue magic was preferred among younger patients, while the topic of amber romance was preferred among older patients. The topic alternative choices, which appeared for both the ED drugs, reflected patient interest in the comparative effectiveness and price outside the drug labeling information. CONCLUSIONS The patient medication reviews of ED drugs reflect patient preferences regarding drug labeling information, marketing claims, and alternative treatment choices. The patient preferences concerning ED treatment attributes inform the design of patient-centered communication for improved ED drug therapy.
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Affiliation(s)
- Maryanne Kim
- College of Pharmacy, Seoul National University, Seoul, Republic of Korea.,Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, Republic of Korea
| | - Youran Noh
- College of Pharmacy, Seoul National University, Seoul, Republic of Korea.,Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, Republic of Korea
| | - Akihiko Yamada
- College of Pharmacy, Seoul National University, Seoul, Republic of Korea
| | - Song Hee Hong
- College of Pharmacy, Seoul National University, Seoul, Republic of Korea.,Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, Republic of Korea
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Implications of a Twitter data-centred methodology for assessing commuters' perceptions of the Delhi metro in India. COMPUTATIONAL URBAN SCIENCE 2022; 2:38. [PMID: 36311354 PMCID: PMC9589671 DOI: 10.1007/s43762-022-00066-7] [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] [Received: 07/13/2022] [Accepted: 10/07/2022] [Indexed: 11/05/2022]
Abstract
Owing to the onset of the new media age, the idea of e-public participation has proven to be a great complement to the limitations of the conventional public participation approach. In this respect, location-based social networks (LBSN) data can prove to be a game shift in this digital era to offer an insight into the commuter perception of service delivery. The paper aims to investigate the potential of using Twitter data to assess commuters’ perceptions of the Delhi metro, India, by presenting a comprehensive methodology for extracting, processing, and interpreting the data. The study extracts Twitter data from the official handle of the Delhi metro, performs semantic and sentiment analysis to comprehend commuters’ concerns and assesses commuters’ sentiments on the predicted concerns. The paper outlines that the current depth of Twitter data is more inclined to instantaneous responses to grievances encountered. Moreover, the analysis presents that for the data extraction period, the topics ‘Ride Safety’ and ‘Crowding’ have the lowest scores, while ‘Personnel Attitude’ and ‘Customer Interface’ have the highest scores. Further, the paper highlights insights gleaned from Twitter data in addition to the aspects included in the conventional satisfaction survey. The paper concludes by outlining the opportunities and limitations of LBSN analytics for effective public transportation decision-making in India.
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Wang S, Huang X, Hu T, Zhang M, Li Z, Ning H, Corcoran J, Khan A, Liu Y, Zhang J, Li X. The times, they are a-changin': tracking shifts in mental health signals from early phase to later phase of the COVID-19 pandemic in Australia. BMJ Glob Health 2022; 7:e007081. [PMID: 35058303 PMCID: PMC8889467 DOI: 10.1136/bmjgh-2021-007081] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 12/09/2021] [Indexed: 11/24/2022] Open
Abstract
INTRODUCTION Widespread problems of psychological distress have been observed in many countries following the outbreak of COVID-19, including Australia. What is lacking from current scholarship is a national-scale assessment that tracks the shifts in mental health during the pandemic timeline and across geographic contexts. METHODS Drawing on 244 406 geotagged tweets in Australia from 1 January 2020 to 31 May 2021, we employed machine learning and spatial mapping techniques to classify, measure and map changes in the Australian public's mental health signals, and track their change across the different phases of the pandemic in eight Australian capital cities. RESULTS Australians' mental health signals, quantified by sentiment scores, have a shift from pessimistic (early pandemic) to optimistic (middle pandemic), reflected by a 174.1% (95% CI 154.8 to 194.5) increase in sentiment scores. However, the signals progressively recessed towards a more pessimistic outlook (later pandemic) with a decrease in sentiment scores by 48.8% (95% CI 34.7 to 64.9). Such changes in mental health signals vary across capital cities. CONCLUSION We set out a novel empirical framework using social media to systematically classify, measure, map and track the mental health of a nation. Our approach is designed in a manner that can readily be augmented into an ongoing monitoring capacity and extended to other nations. Tracking locales where people are displaying elevated levels of pessimistic mental health signals provide important information for the smart deployment of finite mental health services. This is especially critical in a time of crisis during which resources are stretched beyond normal bounds.
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Affiliation(s)
- Siqin Wang
- School of Earth and Environmental Sciences, The University of Queensland, Saint Lucia, Queensland, Australia
| | - Xiao Huang
- Department of Geosciences, University of Arkansas, Fayetteville, Arkansas, USA
| | - Tao Hu
- Department of Geography, Oklahoma State University, Stillwater, Oklahoma, USA
| | - Mengxi Zhang
- Department of Nutrition and Health Science, Ball State University, Muncie, Indiana, USA
| | - Zhenlong Li
- Geoinformation and Big Data Research Laboratory, Department of Geography, University of South Carolina, Columbia, South Carolina, USA
- Big Data Health Science Center, University of South Carolina, Columbia, South Carolina, USA
| | - Huan Ning
- Geoinformation and Big Data Research Laboratory, Department of Geography, University of South Carolina, Columbia, South Carolina, USA
- Big Data Health Science Center, University of South Carolina, Columbia, South Carolina, USA
| | - Jonathan Corcoran
- School of Earth and Environmental Sciences, The University of Queensland, Saint Lucia, Queensland, Australia
| | - Asaduzzaman Khan
- School of Health and Rehabilitation Sciences, The University of Queensland - Saint Lucia Campus, Saint Lucia, Queensland, Australia
| | - Yan Liu
- School of Earth and Environmental Sciences, The University of Queensland, Saint Lucia, Queensland, Australia
| | - Jiajia Zhang
- Big Data Health Science Center, University of South Carolina, Columbia, South Carolina, USA
- Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, USA
| | - Xiaoming Li
- Big Data Health Science Center, University of South Carolina, Columbia, South Carolina, USA
- Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, USA
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Chandrasekaran R, Desai R, Shah H, Kumar V, Moustakas E. Examining Public Sentiments and Attitudes Toward COVID-19 Vaccination: Infoveillance Study Using Twitter Posts. JMIR INFODEMIOLOGY 2022; 2:e33909. [PMID: 35462735 PMCID: PMC9014796 DOI: 10.2196/33909] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 03/03/2022] [Accepted: 03/19/2022] [Indexed: 02/06/2023]
Abstract
Background A global rollout of vaccinations is currently underway to mitigate and protect people from the COVID-19 pandemic. Several individuals have been using social media platforms such as Twitter as an outlet to express their feelings, concerns, and opinions about COVID-19 vaccines and vaccination programs. This study examined COVID-19 vaccine–related tweets from January 1, 2020, to April 30, 2021, to uncover the topics, themes, and variations in sentiments of public Twitter users. Objective The aim of this study was to examine key themes and topics from COVID-19 vaccine–related English tweets posted by individuals, and to explore the trends and variations in public opinions and sentiments. Methods We gathered and assessed a corpus of 2.94 million COVID-19 vaccine–related tweets made by 1.2 million individuals. We used CoreX topic modeling to explore the themes and topics underlying the tweets, and used VADER sentiment analysis to compute sentiment scores and examine weekly trends. We also performed qualitative content analysis of the top three topics pertaining to COVID-19 vaccination. Results Topic modeling yielded 16 topics that were grouped into 6 broader themes underlying the COVID-19 vaccination tweets. The most tweeted topic about COVID-19 vaccination was related to vaccination policy, specifically whether vaccines needed to be mandated or optional (13.94%), followed by vaccine hesitancy (12.63%) and postvaccination symptoms and effects (10.44%) Average compound sentiment scores were negative throughout the 16 weeks for the topics postvaccination symptoms and side effects and hoax/conspiracy. However, consistent positive sentiment scores were observed for the topics vaccination disclosure, vaccine efficacy, clinical trials and approvals, affordability, regulation, distribution and shortage, travel, appointment and scheduling, vaccination sites, advocacy, opinion leaders and endorsement, and gratitude toward health care workers. Reversal in sentiment scores in a few weeks was observed for the topics vaccination eligibility and hesitancy. Conclusions Identification of dominant themes, topics, sentiments, and changing trends about COVID-19 vaccination can aid governments and health care agencies to frame appropriate vaccination programs, policies, and rollouts.
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Affiliation(s)
- Ranganathan Chandrasekaran
- Department of Information and Decision Sciences University of Illinois at Chicago Chicago, IL United States
| | - Rashi Desai
- Department of Information and Decision Sciences University of Illinois at Chicago Chicago, IL United States
| | - Harsh Shah
- Department of Information and Decision Sciences University of Illinois at Chicago Chicago, IL United States
| | - Vivek Kumar
- Department of Information and Decision Sciences University of Illinois at Chicago Chicago, IL United States
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Wu L, Peng Q, Lemke M, Hu T, Gong X. Spatial social network research: a bibliometric analysis. COMPUTATIONAL URBAN SCIENCE 2022; 2:21. [PMID: 37096207 PMCID: PMC10115482 DOI: 10.1007/s43762-022-00045-y] [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: 05/06/2022] [Accepted: 05/13/2022] [Indexed: 04/26/2023]
Abstract
A restless and dynamic intellectual landscape has taken hold in the field of spatial social network studies, given the increasingly attention towards fine-scale human dynamics in this urbanizing and mobile world. The measuring parameters of such dramatic growth of the literature include scientific outputs, domain categories, major journals, countries, institutions, and frequently used keywords. The research in the field has been characterized by fast development of relevant scholarly articles and growing collaboration among and across institutions. The Journal of Economic Geography, Annals of the Association of American Geographers, and Urban Studies ranked first, second, and third, respectively, according to average citations. The United States, United Kingdom, and China were the countries that yielded the most published studies in the field. The number of international collaborative studies published in non-native English-speaking countries (such as France, Italy, and the Netherlands) were higher than native English-speaking countries. Wuhan University, the University of Oxford, and Harvard University were the universities that published the most in the field. "Twitter", "big data", "networks", "spatial analysis", and "social capital" have been the major keywords over the past 20 years. At the same time, the keywords such as "social media", "Twitter", "big data", "geography", "China", "human mobility", "machine learning", "GIS", "location-based social networks", "clustering", "data mining", and "location-based services" have attracted increasing attention in that same time frame, indicating the future research trends.
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Affiliation(s)
- Ling Wu
- Texas Research Data Center, Texas A&M University, College Station, USA
| | - Qiong Peng
- Department of Computer Science, Northeastern University, Boston, USA
| | - Michael Lemke
- Department of Social Sciences, University of Houston-Downtown, Houston, USA
| | - Tao Hu
- Department of Geography, Oklahoma State University, Stillwater, USA
| | - Xi Gong
- Department of Geography and Environmental Studies, University of New Mexico, Albuquerque, USA
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Trevino J, Malik S, Schmidt M. Integrating Google Trends Search Engine Query Data Into Adult Emergency Department Volume Forecasting: Infodemiology Study. JMIR INFODEMIOLOGY 2022; 2:e32386. [PMID: 37113800 PMCID: PMC10014085 DOI: 10.2196/32386] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 10/05/2021] [Accepted: 12/07/2021] [Indexed: 04/29/2023]
Abstract
Background The search for health information from web-based resources raises opportunities to inform the service operations of health care systems. Google Trends search query data have been used to study public health topics, such as seasonal influenza, suicide, and prescription drug abuse; however, there is a paucity of literature using Google Trends data to improve emergency department patient-volume forecasting. Objective We assessed the ability of Google Trends search query data to improve the performance of adult emergency department daily volume prediction models. Methods Google Trends search query data related to chief complaints and health care facilities were collected from Chicago, Illinois (July 2015 to June 2017). We calculated correlations between Google Trends search query data and emergency department daily patient volumes from a tertiary care adult hospital in Chicago. A baseline multiple linear regression model of emergency department daily volume with traditional predictors was augmented with Google Trends search query data; model performance was measured using mean absolute error and mean absolute percentage error. Results There were substantial correlations between emergency department daily volume and Google Trends "hospital" (r=0.54), combined terms (r=0.50), and "Northwestern Memorial Hospital" (r=0.34) search query data. The final Google Trends data-augmented model included the predictors Combined 3-day moving average and Hospital 3-day moving average and performed better (mean absolute percentage error 6.42%) than the final baseline model (mean absolute percentage error 6.67%)-an improvement of 3.1%. Conclusions The incorporation of Google Trends search query data into an adult tertiary care hospital emergency department daily volume prediction model modestly improved model performance. Further development of advanced models with comprehensive search query terms and complementary data sources may improve prediction performance and could be an avenue for further research.
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Affiliation(s)
- Jesus Trevino
- Department of Emergency Medicine The George Washington University School of Medicine & Health Sciences Washington, DC United States
| | - Sanjeev Malik
- Department of Emergency Medicine Northwestern University Feinberg School of Medicine Chicago, IL United States
| | - Michael Schmidt
- Department of Emergency Medicine Northwestern University Feinberg School of Medicine Chicago, IL United States
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Exploring public perceptions of the COVID-19 vaccine online from a cultural perspective: Semantic network analysis of two social media platforms in the United States and China. TELEMATICS AND INFORMATICS 2021; 65:101712. [PMID: 34887618 PMCID: PMC8429027 DOI: 10.1016/j.tele.2021.101712] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2021] [Revised: 07/28/2021] [Accepted: 08/30/2021] [Indexed: 01/14/2023]
Abstract
The development and uptake of the COVID-19 (coronavirus disease 2019) vaccine is a top priority in stifling the COVID-19 pandemic. How the public perceives the COVID-19 vaccine is directly associated with vaccine compliance and vaccination coverage. This study takes a cultural sensitivity perspective and adopts two well-known social media platforms in the United States (Twitter) and China (Weibo) to conduct a public perception comparison around the COVID-19 vaccine. By implementing semantic network analysis, results demonstrate that the two countries' social media users overlapped in themes concerning domestic vaccination policies, priority groups, challenges from COVID-19 variants, and the global pandemic situation. However, Twitter users were prone to disclose individual vaccination experiences, express anti-vaccine attitudes. In comparison, Weibo users manifested evident deference to authorities and exhibited more positive feelings toward the COVID-19 vaccine. Those disparities were explained by the cultural characteristics' differences between the two countries. The findings provide insights into comprehending public health issues in cross-cultural contexts and illustrate the potential of utilizing social media to conduct health informatics studies and investigate public perceptions during public health crisis time.
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Hayawi K, Shahriar S, Serhani MA, Taleb I, Mathew SS. ANTi-Vax: a novel Twitter dataset for COVID-19 vaccine misinformation detection. Public Health 2021; 203:23-30. [PMID: 35016072 PMCID: PMC8648668 DOI: 10.1016/j.puhe.2021.11.022] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 11/02/2021] [Accepted: 11/27/2021] [Indexed: 11/01/2022]
Abstract
OBJECTIVES COVID-19 (SARS-CoV-2) pandemic has infected hundreds of millions and inflicted millions of deaths around the globe. Fortunately, the introduction of COVID-19 vaccines provided a glimmer of hope and a pathway to recovery. However, owing to misinformation being spread on social media and other platforms, there has been a rise in vaccine hesitancy which can lead to a negative impact on vaccine uptake in the population. The goal of this research is to introduce a novel machine learning-based COVID-19 vaccine misinformation detection framework. STUDY DESIGN We collected and annotated COVID-19 vaccine tweets and trained machine learning algorithms to classify vaccine misinformation. METHODS More than 15,000 tweets were annotated as misinformation or general vaccine tweets using reliable sources and validated by medical experts. The classification models explored were XGBoost, LSTM, and BERT transformer model. RESULTS The best classification performance was obtained using BERT, resulting in 0.98 F1-score on the test set. The precision and recall scores were 0.97 and 0.98, respectively. CONCLUSION Machine learning-based models are effective in detecting misinformation regarding COVID-19 vaccines on social media platforms.
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Affiliation(s)
- K Hayawi
- College of Technological Innovation, Zayed University, Abu Dhabi, United Arab Emirates.
| | - S Shahriar
- College of Technological Innovation, Zayed University, Abu Dhabi, United Arab Emirates
| | - M A Serhani
- College of Information Technology, UAE University, Abu Dhabi, United Arab Emirates
| | - I Taleb
- College of Technological Innovation, Zayed University, Abu Dhabi, United Arab Emirates
| | - S S Mathew
- College of Technological Innovation, Zayed University, Abu Dhabi, United Arab Emirates
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50
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Mollalo A, Mohammadi A, Mavaddati S, Kiani B. Spatial Analysis of COVID-19 Vaccination: A Scoping Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:12024. [PMID: 34831801 PMCID: PMC8624385 DOI: 10.3390/ijerph182212024] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 11/09/2021] [Accepted: 11/10/2021] [Indexed: 01/01/2023]
Abstract
Spatial analysis of COVID-19 vaccination research is increasing in recent literature due to the availability of COVID-19 vaccination data that usually contain location components. However, to our knowledge, no previous study has provided a comprehensive review of this research area. Therefore, in this scoping review, we examined the breadth of spatial and spatiotemporal vaccination studies to summarize previous findings, highlight research gaps, and provide guidelines for future research. We performed this review according to the five-stage methodological framework developed by Arksey and O'Malley. We screened all articles published in PubMed/MEDLINE, Scopus, and Web of Science databases, as of 21 September 2021, that had employed at least one form of spatial analysis of COVID-19 vaccination. In total, 36 articles met the inclusion criteria and were organized into four main themes: disease surveillance (n = 35); risk analysis (n = 14); health access (n = 16); and community health profiling (n = 2). Our findings suggested that most studies utilized preliminary spatial analysis techniques, such as disease mapping, which might not lead to robust inferences. Moreover, few studies addressed data quality, modifiable areal unit problems, and spatial dependence, highlighting the need for more sophisticated spatial and spatiotemporal analysis techniques.
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Affiliation(s)
- Abolfazl Mollalo
- Department of Public Health and Prevention Science, School of Health Sciences, Baldwin Wallace University, Berea, OH 44017, USA;
| | - Alireza Mohammadi
- Department of Geography and Urban Planning, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil 56199, Iran;
| | - Sara Mavaddati
- Faculty of Medicine & Surgery, Policlinic University Hospital of Bari Aldo Moro, 70124 Bari, Italy;
| | - Behzad Kiani
- Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad 91779, Iran
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