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Tsironis G, Daglis T, Tsagarakis KP. The circular economy through the prism of machine learning and the YouTube video media platform. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 368:121977. [PMID: 39116810 DOI: 10.1016/j.jenvman.2024.121977] [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: 04/25/2024] [Revised: 06/25/2024] [Accepted: 07/19/2024] [Indexed: 08/10/2024]
Abstract
The transition to a Circular Economy (CE) is rapidly gaining ground across countries and industries. It is the means of achieving more sustainable development by adopting innovative environmentally friendly strategies and saving primary resources. There are several studies indicating the increasing public and corporate interest in the CE but still remain limited in terms of the multitude and utilization of social media data. This work aims to shed light on the most common topics discussed on the YouTube platform, related to the CE. For this reason, we selected 17 videos including the term "Circular Economy" since these have been the most relevant with a sufficient number of comments and views. The model identified two main topics referring to "Sustainable industry and environmental responsibility" and "Circular Economy and resource management" which is a strong indicator of the people's interest in the utilization of resources alongside industrial and corporate activities. The two-topic configuration presented the highest coherence score; however, five and ten-topic configurations have been deployed since there was no extreme differentiation in the model's performance, which could provide more detailed insights. This work's innovation lies in utilizing Machine Learning techniques and social media data to unravel CE's debates.
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Affiliation(s)
- Georgios Tsironis
- Department of Environmental Engineering, Democritus University, Xanthi, 67100, Greece
| | - Theodoros Daglis
- School of Production Engineering and Management, Technical University of Crete, 73100, Chania, Greece
| | - Konstantinos P Tsagarakis
- School of Production Engineering and Management, Technical University of Crete, 73100, Chania, Greece.
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2
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Sasse K, Mahabir R, Gkountouna O, Crooks A, Croitoru A. Understanding the determinants of vaccine hesitancy in the United States: A comparison of social surveys and social media. PLoS One 2024; 19:e0301488. [PMID: 38843170 PMCID: PMC11156396 DOI: 10.1371/journal.pone.0301488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Accepted: 03/12/2024] [Indexed: 06/09/2024] Open
Abstract
The COVID-19 pandemic prompted governments worldwide to implement a range of containment measures, including mass gathering restrictions, social distancing, and school closures. Despite these efforts, vaccines continue to be the safest and most effective means of combating such viruses. Yet, vaccine hesitancy persists, posing a significant public health concern, particularly with the emergence of new COVID-19 variants. To effectively address this issue, timely data is crucial for understanding the various factors contributing to vaccine hesitancy. While previous research has largely relied on traditional surveys for this information, recent sources of data, such as social media, have gained attention. However, the potential of social media data as a reliable proxy for information on population hesitancy, especially when compared with survey data, remains underexplored. This paper aims to bridge this gap. Our approach uses social, demographic, and economic data to predict vaccine hesitancy levels in the ten most populous US metropolitan areas. We employ machine learning algorithms to compare a set of baseline models that contain only these variables with models that incorporate survey data and social media data separately. Our results show that XGBoost algorithm consistently outperforms Random Forest and Linear Regression, with marginal differences between Random Forest and XGBoost. This was especially the case with models that incorporate survey or social media data, thus highlighting the promise of the latter data as a complementary information source. Results also reveal variations in influential variables across the five hesitancy classes, such as age, ethnicity, occupation, and political inclination. Further, the application of models to different MSAs yields mixed results, emphasizing the uniqueness of communities and the need for complementary data approaches. In summary, this study underscores social media data's potential for understanding vaccine hesitancy, emphasizes the importance of tailoring interventions to specific communities, and suggests the value of combining different data sources.
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Affiliation(s)
- Kuleen Sasse
- Department of Computer Science, The Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Ron Mahabir
- Geographic Data Science Lab, Department of Geography and Planning, University of Liverpool, Liverpool, United Kingdom
| | - Olga Gkountouna
- Geographic Data Science Lab, Department of Geography and Planning, University of Liverpool, Liverpool, United Kingdom
| | - Andrew Crooks
- Department of Geography, University at Buffalo, Buffalo, New York, United States of America
| | - Arie Croitoru
- Department of Computational and Data Sciences, George Mason University, Fairfax, Virginia, United States of America
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3
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Porreca A, Di Nicola M. Flu vaccination coverage in Italy in the COVID-19 era: A fuzzy functional k-means (FFKM) approach. J Infect Public Health 2023; 16:1742-1749. [PMID: 37738690 DOI: 10.1016/j.jiph.2023.08.025] [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: 06/24/2023] [Revised: 08/17/2023] [Accepted: 08/31/2023] [Indexed: 09/24/2023] Open
Abstract
BACKGROUND In Europe, flu vaccination coverage has decreased, and there are complex barriers to overcome to vaccinate against flu. Many studies have been conducted to estimate vaccination coverage. The COVID-19 pandemic threatens to disrupt immunization programs in many countries, including Italy, where vaccination against the flu is recommended but not mandatory. This paper aims to understand changes in flu vaccine uptake in Italian regions. METHODS Using functional data analysis and fuzzy functional k-means clustering, we investigated changes in flu vaccine coverage in Italian regions before (2010-2019) and after (2020-2022) the COVID-19 vaccination period. RESULTS The period of COVID-19 pandemic brought an increase in vaccine coverage globally. Elbow's method determined that the optimal number of clusters in vaccination uptake is 2. Apulia, Basilicata, Emilia Romagna, Liguria, Molise, Tuscany, and Umbria in 2019 belong less to the group with low flu vaccination uptake (G1) but increase their tendency to belong to this group over time: they decrease their propensity to be vaccinated for flu. For others, it seems that COVID-19 served as a push to increase flu vaccination coverage rates. Sicily appears to be the region that has responded best to the pandemic, changing its membership value from 2019 to 2022. CONCLUSION The present study highlights that the COVID-19 era has resulted in a higher flu vaccination coverage rate. Moreover, the regional level's improvement or worsening in flu vaccination coverage rate is not affected by the historical gap and socio-cultural and economic differences prevailing among Italian regions.
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Affiliation(s)
- Annamaria Porreca
- Department Of Medical, Oral and Biotechnological Sciences, Chieti, Italy.
| | - Marta Di Nicola
- Department Of Medical, Oral and Biotechnological Sciences, Chieti, Italy
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Bianchi FP, Tafuri S. Spreading of misinformation on mass media and digital platforms regarding vaccines. A systematic scoping review on stakeholders, policymakers, and sentiments/behavior of Italian consumers. Hum Vaccin Immunother 2023; 19:2259398. [PMID: 37782549 PMCID: PMC10547076 DOI: 10.1080/21645515.2023.2259398] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 09/12/2023] [Indexed: 10/04/2023] Open
Abstract
Studies on traditional and social media have found that misinformation about vaccines has been widely spread over the last decade, negatively impacting public opinion and people's willingness to get vaccinated. We reviewed the sentiments of Italian users to define the characteristic of anti-vax and pro-vax contents and defined the strategies to deal with the misinformation. Scopus, MEDLINE/PubMed, Google Scholar (up to page 10), and ISI Web of Knowledge databases were systematically searched. Research articles, brief reports, commentaries, and letters published between January 1, 2010 and March 30, 2022 were included in the search. No-vax or ambiguous contents in Italian mass media are not prevalent compared to neutral and pro-vax content; the communication of no-vax groups is significantly simplified, favoring the understanding of the topics by users. Events related to vaccinations are associated with news coverage by media, search engine consultations, and user reactions on social networks. In this context, the activity of no-vax groups is triggered, and misinformation and fake news spread even further. A multifactorial approach is necessary to manage online user sentiment and use mass and social media as health promotion tools.
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Affiliation(s)
| | - Silvio Tafuri
- Interdisciplinary Department of Medicine, Aldo Moro University of Bari, Bari, Italy
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5
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Murero M. Coordinated inauthentic behavior: An innovative manipulation tactic to amplify COVID-19 anti-vaccine communication outreach via social media. FRONTIERS IN SOCIOLOGY 2023; 8:1141416. [PMID: 37006634 PMCID: PMC10060790 DOI: 10.3389/fsoc.2023.1141416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 02/13/2023] [Indexed: 06/19/2023]
Abstract
Coordinated inauthentic behavior (CIB) is a manipulative communication tactic that uses a mix of authentic, fake, and duplicated social media accounts to operate as an adversarial network (AN) across multiple social media platforms. The article aims to clarify how CIB's emerging communication tactic "secretly" exploits technology to massively harass, harm, or mislead the online debate around crucial issues for society, like the COVID-19 vaccination. CIB's manipulative operations could be one of the greatest threats to freedom of expression and democracy in our society. CIB campaigns mislead others by acting with pre-arranged exceptional similarity and "secret" operations. Previous theoretical frameworks failed to evaluate the role of CIB on vaccination attitudes and behavior. In light of recent international and interdisciplinary CIB research, this study critically analyzes the case of a COVID-19 anti-vaccine adversarial network removed from Meta at the end of 2021 for brigading. A violent and harmful attempt to tactically manipulate the COVID-19 vaccine debate in Italy, France, and Germany. The following focal issues are discussed: (1) CIB manipulative operations, (2) their extensions, and (3) challenges in CIB's identification. The article shows that CIB acts in three domains: (i) structuring inauthentic online communities, (ii) exploiting social media technology, and (iii) deceiving algorithms to extend communication outreach to unaware social media users, a matter of concern for the general audience of CIB-illiterates. Upcoming threats, open issues, and future research directions are discussed.
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6
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Wang Y, Chen Y. Characterizing discourses about COVID-19 vaccines on Twitter: a topic modeling and sentiment analysis approach. JOURNAL OF COMMUNICATION IN HEALTHCARE 2023; 16:103-112. [PMID: 36919802 DOI: 10.1080/17538068.2022.2054196] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
BACKGROUND Evidence-based health communication is crucial for facilitating vaccine-related knowledge and addressing vaccine hesitancy. To that end, it is important to understand the discourses about COVID-19 vaccination and attend to the publics' emotions underlying those discourses. METHODS We collect tweets related to COVID-19 vaccines from March 2020 to March 2021. In total, 304,292 tweets from 134,015 users are collected. We conduct a Latent Dirichlet Allocation (LDA) modeling analysis and a sentiment analysis to analyze the discourse themes and sentiments. RESULTS This study identifies seven themes of COVID-19 vaccine-related discourses. Vaccine advocacy (24.82%) is the most widely discussed topic about COVID-19 vaccines, followed by vaccine hesitancy (22.29%), vaccine rollout (12.99%), vaccine facts (12.61%), recognition for healthcare workers (12.47%), vaccine side effects (10.07%), and vaccine policies (4.75%). Trust is the most salient emotion associated with COVID-19 vaccine discourses, followed by anticipation, fear, joy, sadness, anger, surprise, and disgust. Among the seven topics, vaccine advocacy tweets are most likely to receive likes and comments, and vaccine fact tweets are most likely to receive retweets. CONCLUSIONS When talking about vaccines, publics' emotions are dominated by trust and anticipation, yet mixed with fear and sadness. Although tweets about vaccine hesitancy are prevalent on Twitter, those messages receive fewer likes and comments than vaccine advocacy messages. Over time, tweets about vaccine advocacy and vaccine facts become more dominant whereas tweets about vaccine hesitancy become less dominant among COVID-19 vaccine discourses, suggesting that publics become more confident about COVID-19 vaccines as they obtain more information.
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Affiliation(s)
- Yuan Wang
- Department of Communication, University of Maryland, College Park, MD, USA
| | - Yonghao Chen
- College of Information Studies, University of Maryland, College Park, MD, USA
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7
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Karyukin V, Mutanov G, Mamykova Z, Nassimova G, Torekul S, Sundetova Z, Negri M. On the development of an information system for monitoring user opinion and its role for the public. JOURNAL OF BIG DATA 2022; 9:110. [PMID: 36465138 PMCID: PMC9684810 DOI: 10.1186/s40537-022-00660-w] [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: 11/29/2021] [Accepted: 10/20/2022] [Indexed: 06/17/2023]
Abstract
Social media services and analytics platforms are rapidly growing. A large number of various events happen mostly every day, and the role of social media monitoring tools is also increasing. Social networks are widely used for managing and promoting brands and different services. Thus, most popular social analytics platforms aim for business purposes while monitoring various social, economic, and political problems remains underrepresented and not covered by thorough research. Moreover, most of them focus on resource-rich languages such as the English language, whereas texts and comments in other low-resource languages, such as the Russian and Kazakh languages in social media, are not represented well enough. So, this work is devoted to developing and applying the information system called the OMSystem for analyzing users' opinions on news portals, blogs, and social networks in Kazakhstan. The system uses sentiment dictionaries of the Russian and Kazakh languages and machine learning algorithms to determine the sentiment of social media texts. The whole structure and functionalities of the system are also presented. The experimental part is devoted to building machine learning models for sentiment analysis on the Russian and Kazakh datasets. Then the performance of the models is evaluated with accuracy, precision, recall, and F1-score metrics. The models with the highest scores are selected for implementation in the OMSystem. Then the OMSystem's social analytics module is used to thoroughly analyze the healthcare, political and social aspects of the most relevant topics connected with the vaccination against the coronavirus disease. The analysis allowed us to discover the public social mood in the cities of Almaty and Nur-Sultan and other large regional cities of Kazakhstan. The system's study included two extensive periods: 10-01-2021 to 30-05-2021 and 01-07-2021 to 12-08-2021. In the obtained results, people's moods and attitudes to the Government's policies and actions were studied by such social network indicators as the level of topic discussion activity in society, the level of interest in the topic in society, and the mood level of society. These indicators calculated by the OMSystem allowed careful identification of alarming factors of the public (negative attitude to the government regulations, vaccination policies, trust in vaccination, etc.) and assessment of the social mood.
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Affiliation(s)
| | | | - Zhanl Mamykova
- Al-Farabi Kazakh National University, Almaty, 050040 Kazakhstan
| | | | - Saule Torekul
- Al-Farabi Kazakh National University, Almaty, 050040 Kazakhstan
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Gao H, Yin H, Peng L, Wang H. Effectiveness of Social Video Platforms in Promoting COVID-19 Vaccination Among Youth: A Content-Specific Analysis of COVID-19 Vaccination Topic Videos on Bilibili. Risk Manag Healthc Policy 2022; 15:1621-1639. [PMID: 36071816 PMCID: PMC9444025 DOI: 10.2147/rmhp.s374420] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 08/25/2022] [Indexed: 12/15/2022] Open
Abstract
Background With the widespread promotion of the COVID-19 vaccination in China, videos about the vaccination have become increasingly available on social video platforms. With the User Generated Content model, different creators’ interpretations of COVID-19 vaccines may influence the attitudes towards the vaccines and vaccination. Objective To explore the overview of COVID-19 vaccine-related videos on Bilibili, discussing the communication effects of COVID-19 topic videos and its influencing factors. Methods A content analysis was applied to the 202 video samples obtained through data mining regarding the creator’s information, video presentation, and COVID-19 vaccine-related content. Results Individuals and medical professionals preferred VLOG videos, media chose to upload informational videos, and enterprises preferred to post showcase videos. Individuals were more likely to discuss the adverse reactions in their videos, while medical professionals were more likely to discuss the vaccination process for the COVID-19 vaccine. Videos with core issues positively influenced the video’s dissemination breadth. The attitudes toward the COVID-19 vaccine in the videos positively influence the recognition of the videos. The richness of knowledge points related to the COVID-19 vaccine negatively affected the recognition and participation. Conclusion Social video platforms could play an active role in the vaccination promotion for the youth. Health promotion-related departments and individuals could strengthen agenda setting, grasp the characteristics of young groups, and express positive attitudes toward health issues to achieve better health (vaccine) promotion.
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Affiliation(s)
- Hao Gao
- School of Journalism and Communication, Nanjing Normal University, Nanjing, 210097, People’s Republic of China
| | - Hao Yin
- School of Journalism and Communication, Nanjing Normal University, Nanjing, 210097, People’s Republic of China
| | - Li Peng
- School of Journalism and Communication, Nanjing Normal University, Nanjing, 210097, People’s Republic of China
| | - Han Wang
- School of Journalism and Communication, Jinan University, Guangzhou, Guangdong, 510632, People’s Republic of China
- Correspondence: Han Wang, School of Journalism and Communication, Jinan University, No. 601, West Huangpu Avenue, Guangzhou, Guangdong, 510632, People’s Republic of China, Email
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9
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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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10
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Sánchez PRP, Folgado-Fernández JA, Sánchez MAR. Virtual Reality Technology: Analysis based on text and opinion mining. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:7856-7885. [PMID: 35801447 DOI: 10.3934/mbe.2022367] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The purpose of this research is to highlight the importance of periodically analyzing the data obtained from the technological sources used by customers, such as user comments on social networks and videos, using qualitative data analysis software. This research analyzes user sentiments, words, and opinions about virtual reality (VR) videos on YouTube in order to explore user reactions to such videos, as well as to establish whether this technology contributes to the sustainability of natural environments. User-generated data can provide important information for decision making about future policies of companies that produce video content. The results of our analysis of 12 videos revealed that users predominantly perceived these videos positively. This conclusion was supported by the findings of an opinion and text analysis, which identified positive reviews for videos and channels with many followers and large numbers of visits. The features such as the quality of the video and the accessibility of technology were appreciated by the viewers, whereas videos that are 100% VR and require special glasses to view them do not have as many visits. However, VR was seen to be a product which viewers were interested in and, according to Google, there are an increasing number of searches and sales of VR glasses in holiday seasons. Emotions of wonder and joy are more evident than emotions of anger or frustration, so positive feelings can be seen to be predominant.
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Affiliation(s)
- Pedro R Palos Sánchez
- Department Financial Economy and Operations Management, University of Seville, Spain
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11
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Roberts SB, Das SK, Sayer RD, Caldwell AE, Wyatt HR, Mehta TS, Gorczyca AM, Oslund JL, Peters JC, Friedman JE, Chiu CY, Greenway FL, Donnelly JE, Dao MC, Cuevas AG, Affuso O, Wilkinson LL, Thomas D, Al-Ozairi E, Yannakoulia M, Khazrai YM, Manalac RJ, Bachiashvili V, Hill JO. Technical report: an online international weight control registry to inform precision approaches to healthy weight management. Int J Obes (Lond) 2022; 46:1728-1733. [PMID: 35710944 PMCID: PMC9201790 DOI: 10.1038/s41366-022-01158-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 05/18/2022] [Accepted: 05/24/2022] [Indexed: 11/11/2022]
Abstract
BACKGROUND Personalizing approaches to prevention and treatment of obesity will be a crucial aspect of precision health initiatives. However, in considering individual susceptibility to obesity, much remains to be learned about how to support healthy weight management in different population subgroups, environments and geographical locations. SUBJECTS/METHODS The International Weight Control Registry (IWCR) has been launched to facilitate a deeper and broader understanding of the spectrum of factors contributing to success and challenges in weight loss and weight loss maintenance in individuals and across population groups. The IWCR registry aims to recruit, enroll and follow a diverse cohort of adults with varying rates of success in weight management. Data collection methods include questionnaires of demographic variables, weight history, and behavioral, cultural, economic, psychological, and environmental domains. A subset of participants will provide objective measures of physical activity, weight, and body composition along with detailed reports of dietary intake. Lastly, participants will be able to provide qualitative information in an unstructured format on additional topics they feel are relevant, and environmental data will be obtained from public sources based on participant zip code. CONCLUSIONS The IWCR will be a resource for researchers to inform improvements in interventions for weight loss and weight loss maintenance in different countries, and to examine environmental and policy-level factors that affect weight management in different population groups. This large scale, multi-level approach aims to inform efforts to reduce the prevalence of obesity worldwide and its associated comorbidities and economic impacts. TRIAL REGISTRATION NCT04907396 (clinicaltrials.gov) sponsor SB Roberts; Tufts University IRB #13075.
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Affiliation(s)
- Susan B. Roberts
- grid.429997.80000 0004 1936 7531Energy Metabolism, Jean Mayer USDA Human Nutrition Center on Aging, Tufts University, Boston, MA 02111 USA
| | - Sai Krupa Das
- grid.429997.80000 0004 1936 7531Energy Metabolism, Jean Mayer USDA Human Nutrition Center on Aging, Tufts University, Boston, MA 02111 USA
| | - R. Drew Sayer
- grid.265892.20000000106344187Department of Nutrition Sciences, University of Alabama at Birmingham, Birmingham, AL 35294 USA
| | - Ann E. Caldwell
- grid.430503.10000 0001 0703 675XDivision of Endocrinology, Metabolism, and Diabetes, University of Colorado School of Medicine, Aurora, CO 80045 USA
| | - Holly R. Wyatt
- grid.265892.20000000106344187Department of Nutrition Sciences, University of Alabama at Birmingham, Birmingham, AL 35294 USA
| | - Tapan S. Mehta
- grid.265892.20000000106344187Department of Family and Community Medicine, University of Alabama at Birmingham Heersink School of Medicine, Birmingham, AL 35233 USA
| | - Anna M. Gorczyca
- grid.412016.00000 0001 2177 6375Department of Internal Medicine, University of Kansas Medical Center, Kansas City, KS 66160 USA
| | - Jennifer L. Oslund
- grid.429997.80000 0004 1936 7531Energy Metabolism, Jean Mayer USDA Human Nutrition Center on Aging, Tufts University, Boston, MA 02111 USA
| | - John C. Peters
- grid.430503.10000 0001 0703 675XDivision of Endocrinology, Metabolism, and Diabetes, University of Colorado School of Medicine, Aurora, CO 80045 USA
| | - James E. Friedman
- grid.265892.20000000106344187Department of Nutrition Sciences, University of Alabama at Birmingham, Birmingham, AL 35294 USA
| | - Chia-Ying Chiu
- grid.265892.20000000106344187Division of Pulmonary, Allergy and Critical Care Medicine, University of Alabama at Birmingham Heersink School of Medicine, Birmingham, AL 35233 USA
| | - Frank L. Greenway
- grid.410428.b0000 0001 0665 5823Clinical Trials Unit, Pennington Biomedical Research Center, Louisiana State University System, Baton Rouge, LA 70808 USA
| | - Joseph E. Donnelly
- grid.412016.00000 0001 2177 6375Department of Internal Medicine, University of Kansas Medical Center, Kansas City, KS 66160 USA
| | - Maria Carlota Dao
- grid.167436.10000 0001 2192 7145Department of Agriculture, Nutrition, and Food Systems, University of New Hampshire, Durham, NH 03824 USA
| | - Adolfo G. Cuevas
- grid.429997.80000 0004 1936 7531Department of Community Health, Tufts University, Medford, MA 02155 USA
| | - Olivia Affuso
- grid.265892.20000000106344187Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL 35233 USA
| | - Larrell L. Wilkinson
- grid.265892.20000000106344187Department of Human Studies, University of Alabama at Birmingham, Birmingham, AL 35233 USA
| | - Diana Thomas
- grid.419884.80000 0001 2287 2270Department of Mathematical Sciences, United States Military Academy, West Point, NY 10996 USA
| | - Ebaa Al-Ozairi
- grid.452356.30000 0004 0518 1285Clinical Research Unit, Dasman Diabetes Institute, 15462 Kuwait City, Kuwait
| | - Mary Yannakoulia
- grid.15823.3d0000 0004 0622 2843Department of Nutrition and Dietetics, Harokopio University, El. Venizelou 70, 176 71 Kallithea, Greece
| | - Yeganeh M. Khazrai
- grid.9657.d0000 0004 1757 5329Department of Food Science and Human Nutrition, Università Campus Bio-Medico di Roma, Via Álvaro del Portillo, 21, 00128 Roma, RM Italy
| | - Raoul J. Manalac
- grid.64337.350000 0001 0662 7451Bariatric & Metabolic Institute, Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, LA USA
| | - Vasil Bachiashvili
- grid.265892.20000000106344187Department of Family and Community Medicine, University of Alabama at Birmingham Heersink School of Medicine, Birmingham, AL 35233 USA
| | - James O. Hill
- grid.265892.20000000106344187Department of Nutrition Sciences, University of Alabama at Birmingham, Birmingham, AL 35294 USA
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Aygun I, Kaya B, Kaya M. Aspect Based Twitter Sentiment Analysis on Vaccination and Vaccine Types in COVID-19 Pandemic with Deep Learning. IEEE J Biomed Health Inform 2021; 26:2360-2369. [PMID: 34874877 DOI: 10.1109/jbhi.2021.3133103] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Due to the COVID-19 pandemic, vaccine development and community vaccination studies are carried out all over the world. At this stage, the opposition to the vaccine seen in the society or the lack of trust in the developed vaccine is an important factor hampering vaccination activities. In this study, aspect-base sentiment analysis was conducted for USA, UK, Canada, Turkey, France, Germany, Spain and Italy showing the approach of twitter users to vaccination and vaccine types during the COVID-19 period. Within the scope of this study, two datasets in English and Turkish were prepared with 928,402 different vaccine-focused tweets collected by country. In the classification of tweets, 4 different aspects (policy, health, media and other) and 4 different BERT models (mBERT-base, BioBERT, ClinicalBERT nad BERTurk) were used. 6 different COVID-19 vaccines with the highest frequency among the datasets were selected and sentiment analysis was made by using Twitter posts regarding these vaccines. To the best of our knowledge, this paper is the first attempt to understand people's views about vaccination and types of vaccines. With the experiments conducted, the results of the views of the people on vaccination and vaccine types were presented according to the countries. The success of the method proposed in this study in the F1 Score was between 84% and 88% in datasets divided by country, while the total accuracy value was 87%.
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Gao H, Guo D, Wu J, Zhao Q, Li L. Changes of the Public Attitudes of China to Domestic COVID-19 Vaccination After the Vaccines Were Approved: A Semantic Network and Sentiment Analysis Based on Sina Weibo Texts. Front Public Health 2021; 9:723015. [PMID: 34858918 PMCID: PMC8632040 DOI: 10.3389/fpubh.2021.723015] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 10/11/2021] [Indexed: 11/25/2022] Open
Abstract
Introduction: On December 31, 2020, the Chinese government announced that the domestic coronavirus disease-2019 (COVID-19) vaccines have obtained approval for conditional marketing and are free for vaccination. This release drove the attention of the public and intense debates on social media, which reflected public attitudes to the domestic vaccine. This study examines whether the public concerns and public attitudes to domestic COVID-19 vaccines changed after the official announcement. Methods: This article used big data analytics in the research, including semantic network and sentiment analysis. The purpose of the semantic network is to obtain the public concerns about domestic vaccines. Sentiment analysis reflects the sentiments of the public to the domestic vaccines and the emotional changes by comparing the specific sentiments shown on the posts before and after the official announcement. Results: There exists a correlation between the public concerns about domestic vaccines before and after the official announcement. According to the semantic network analysis, the public concerns about vaccines have changed after the official announcement. The public focused on the performance issues of the vaccines before the official approval, but they cared more about the practical issues of vaccination after that. The sentiment analysis showed that both positive and negative emotions increased among the public after the official announcement. “Good” was the most increased positive emotion and indicated great public appreciation for the production capacity and free vaccination. “Fear” was the significantly increased negative emotion and reflected the public concern about the safety of the vaccines. Conclusion: The official announcement of the approval for marketing improved the Chinese public acceptance of the domestic COVID-19 vaccines. In addition, safety and effectiveness are vital factors influencing public vaccine hesitancy.
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Affiliation(s)
- Hao Gao
- School of Journalism and Communication, Nanjing Normal University, Nanjing, China
| | - Difan Guo
- School of Journalism and Communication, Nanjing Normal University, Nanjing, China
| | - Jing Wu
- Faculty of Social Sciences, University of Ljubljana, Ljubljana, Slovenia
| | - Qingting Zhao
- School of Journalism and Communication, Nanjing Normal University, Nanjing, China
| | - Lina Li
- Film-Television and Communication College, Shanghai Normal University, Shanghai, China
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Alamoodi AH, Zaidan BB, Al-Masawa M, Taresh SM, Noman S, Ahmaro IYY, Garfan S, Chen J, Ahmed MA, Zaidan AA, Albahri OS, Aickelin U, Thamir NN, Fadhil JA, Salahaldin A. Multi-perspectives systematic review on the applications of sentiment analysis for vaccine hesitancy. Comput Biol Med 2021; 139:104957. [PMID: 34735945 PMCID: PMC8520445 DOI: 10.1016/j.compbiomed.2021.104957] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 10/15/2021] [Accepted: 10/15/2021] [Indexed: 01/04/2023]
Abstract
A substantial impediment to widespread Coronavirus disease (COVID-19) vaccination is vaccine hesitancy. Many researchers across scientific disciplines have presented countless studies in favor of COVID-19 vaccination, but misinformation on social media could hinder vaccination efforts and increase vaccine hesitancy. Nevertheless, studying people's perceptions on social media to understand their sentiment presents a powerful medium for researchers to identify the causes of vaccine hesitancy and therefore develop appropriate public health messages and interventions. To the best of the authors' knowledge, previous studies have presented vaccine hesitancy in specific cases or within one scientific discipline (i.e., social, medical, and technological). No previous study has presented findings via sentiment analysis for multiple scientific disciplines as follows: (1) social, (2) medical, public health, and (3) technology sciences. Therefore, this research aimed to review and analyze articles related to different vaccine hesitancy cases in the last 11 years and understand the application of sentiment analysis on the most important literature findings. Articles were systematically searched in Web of Science, Scopus, PubMed, IEEEXplore, ScienceDirect, and Ovid from January 1, 2010, to July 2021. A total of 30 articles were selected on the basis of inclusion and exclusion criteria. These articles were formed into a taxonomy of literature, along with challenges, motivations, and recommendations for social, medical, and public health and technology sciences. Significant patterns were identified, and opportunities were promoted towards the understanding of this phenomenon.
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Affiliation(s)
- A H Alamoodi
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris (UPSI), Perak, Malaysia.
| | - B B Zaidan
- Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan, ROC
| | - Maimonah Al-Masawa
- Centre for Tissue Engineering and Regenerative Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia Medical Centre, Jalan Yaacob Latif, 56000, Kuala Lumpur, Malaysia
| | - Sahar M Taresh
- Department of Kindergarten Educational Psychology, Taiz University, Yemen
| | - Sarah Noman
- Department of Community Health, Faculty of Medicine & Health Sciences, Universiti Putra Malaysia, Malaysia
| | - Ibraheem Y Y Ahmaro
- Computer Science Department, College of Information Technology, Hebron University, Hebron, Palestine
| | - Salem Garfan
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris (UPSI), Perak, Malaysia
| | - Juliana Chen
- The University of Sydney, Charles Perkins Centre, Discipline of Nutrition and Dietetics, School of Life and Environmental Sciences, Camperdown, New South Wales, Australia; Department of Clinical Medicine, Faculty of Medicine and Health Sciences, Macquarie University, Australia; Healthy Weight Clinic, MQ Health, Macquarie University Hospital, Australia
| | - M A Ahmed
- Computer Science and Mathematics College, Tikrit University, Iraq
| | - A A Zaidan
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris (UPSI), Perak, Malaysia
| | - O S Albahri
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris (UPSI), Perak, Malaysia
| | - Uwe Aickelin
- School of Computing and Information Systems, University of Melbourne, 700 Swanston Street, Victoria, 3010, Australia
| | - Noor N Thamir
- Department of Computer Science, University of Baghdad, Iraq
| | - Julanar Ahmed Fadhil
- Faculty of Computer Science and Information Technology, Universiti Putra Malaysia (UPM), 43400 Serdang, Malaysia
| | - Asmaa Salahaldin
- College of Graduate Studies, Universiti Tenaga Nasional (UNITEN), Kajang, Selangor, Malaysia
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Malik A, Antonino A, Khan ML, Nieminen M. Characterizing HIV discussions and engagement on Twitter. HEALTH AND TECHNOLOGY 2021. [DOI: 10.1007/s12553-021-00577-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
AbstractThe novel settings provided by social media facilitate users to seek and share information on a wide array of subjects, including healthcare and wellness. Analyzing health-related opinions and discussions on these platforms complement traditional public health surveillance systems to support timely and effective interventions. This study aims to characterize the HIV-related conversations on Twitter by identifying the prevalent topics and the key events and actors involved in these discussions. Through Twitter API, we collected tweets containing the hashtag #HIV for a one-year period. After pre-processing the collected data, we conducted engagement analysis, temporal analysis, and topic modeling algorithm on the analytical sample (n = 122,807). Tweets by HIV/AIDS/LGBTQ activists and physicians received the highest level of engagement. An upsurge in tweet volume and engagement was observed during global and local events such as World Aids Day and HIV/AIDS awareness and testing days for trans-genders, blacks, women, and the aged population. Eight topics were identified that include “stigma”, “prevention”, “epidemic in the developing countries”, “World Aids Day”, “treatment”, “events”, “PrEP”, and “testing”. Social media discussions offer a nuanced understanding of public opinions, beliefs, and sentiments about numerous health-related issues. The current study reports various dimensions of HIV-related posts on Twitter. Based on the findings, public health agencies and pertinent entities need to proactively use Twitter and other social media by engaging the public through involving influencers. The undertaken methodological choices may be applied to further assess HIV discourse on other popular social media platforms.
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Thomas DM, Siegel B, Baller D, Lindquist J, Cready G, Zervios JT, Nadglowski JF, Kyle TK. Can the Participant Speak Beyond Likert? Free-Text Responses in COVID-19 Obesity Surveys. Obesity (Silver Spring) 2020; 28:2268-2271. [PMID: 32909373 DOI: 10.1002/oby.23037] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/08/2020] [Revised: 08/27/2020] [Accepted: 09/07/2020] [Indexed: 10/23/2022]
Affiliation(s)
- Diana M Thomas
- Department of Mathematical Sciences, United States Military Academy, West Point, New York, USA
| | - Benjamin Siegel
- Department of Mathematical Sciences, United States Military Academy, West Point, New York, USA
| | - Daniel Baller
- Department of Mathematical Sciences, United States Military Academy, West Point, New York, USA
| | - Joseph Lindquist
- Department of Mathematical Sciences, United States Military Academy, West Point, New York, USA
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Chan MPS, Jamieson KH, Albarracin D. Prospective associations of regional social media messages with attitudes and actual vaccination: A big data and survey study of the influenza vaccine in the United States. Vaccine 2020; 38:6236-6247. [PMID: 32792251 PMCID: PMC7415418 DOI: 10.1016/j.vaccine.2020.07.054] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Revised: 07/24/2020] [Accepted: 07/25/2020] [Indexed: 11/29/2022]
Abstract
Regional Twitter vaccine content was prospectively associated with attitudes. Regional Twitter vaccine content was prospectively associated with vaccination. Discussing the influenza vaccine with others can remove the negative effects of Twitter vaccine content.
Objective Using longitudinal methods to assess regional associations between social media posts about vaccines and attitudes and actual vaccination against influenza in the US. Methods Geolocated tweets from U.S. counties (N = 115,330) were analyzed using MALLET LDA (Latent Dirichlet allocation) topic modeling techniques to correlate with prospective individual survey data (N = 3005) about vaccine attitudes, actual vaccination, and real-life discussions about vaccines with family and friends during the 2018–2019 influenza season. Results Ten topics were common across U.S. counties during the 2018–2019 influenza season. In the overall analyses, two of these topics (i.e., Vaccine Science Matters and Big Pharma) were associated with attitudes and behaviors. The topic concerning vaccine science in November-February was positively correlated with attitudes in February-March, r = 0.09, BF10 = 3. Moreover, among respondents who did not discuss the influenza vaccine with family and friends, the topic about vaccine fraud and children in November-February was negatively correlated with attitudes in February-March and with vaccination in February-March, and April-May (rs = −0.18 to −0.25, BF10 = 4–146). However, this was absent when participants had discussions about the influenza vaccine with family and friends. Discussion Regional vaccine content correlated with prospective measures of vaccine attitudes and actual vaccination. Conclusions Social media have demonstrated strong associations with vaccination patterns. When the associations are negative, discussions with family and friends appear to eliminate them. Programs to promote vaccination should encourage real-life conversations about vaccines.
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Affiliation(s)
- Man-Pui Sally Chan
- Department of Psychology, University of Illinois at Urbana-Champaign, IL, United States.
| | | | - Dolores Albarracin
- Department of Psychology, University of Illinois at Urbana-Champaign, IL, United States; The Annenberg Public Policy Center, University of Pennsylvania, PA, United States
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