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Haque S, Mengersen K, Barr I, Wang L, Yang W, Vardoulakis S, Bambrick H, Hu W. Towards development of functional climate-driven early warning systems for climate-sensitive infectious diseases: Statistical models and recommendations. ENVIRONMENTAL RESEARCH 2024; 249:118568. [PMID: 38417659 DOI: 10.1016/j.envres.2024.118568] [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: 11/27/2023] [Revised: 02/22/2024] [Accepted: 02/25/2024] [Indexed: 03/01/2024]
Abstract
Climate, weather and environmental change have significantly influenced patterns of infectious disease transmission, necessitating the development of early warning systems to anticipate potential impacts and respond in a timely and effective way. Statistical modelling plays a pivotal role in understanding the intricate relationships between climatic factors and infectious disease transmission. For example, time series regression modelling and spatial cluster analysis have been employed to identify risk factors and predict spatial and temporal patterns of infectious diseases. Recently advanced spatio-temporal models and machine learning offer an increasingly robust framework for modelling uncertainty, which is essential in climate-driven disease surveillance due to the dynamic and multifaceted nature of the data. Moreover, Artificial Intelligence (AI) techniques, including deep learning and neural networks, excel in capturing intricate patterns and hidden relationships within climate and environmental data sets. Web-based data has emerged as a powerful complement to other datasets encompassing climate variables and disease occurrences. However, given the complexity and non-linearity of climate-disease interactions, advanced techniques are required to integrate and analyse these diverse data to obtain more accurate predictions of impending outbreaks, epidemics or pandemics. This article presents an overview of an approach to creating climate-driven early warning systems with a focus on statistical model suitability and selection, along with recommendations for utilizing spatio-temporal and machine learning techniques. By addressing the limitations and embracing the recommendations for future research, we could enhance preparedness and response strategies, ultimately contributing to the safeguarding of public health in the face of evolving climate challenges.
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Affiliation(s)
- Shovanur Haque
- Ecosystem Change and Population Health Research Group, School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia
| | - Kerrie Mengersen
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia; Centre for Data Science (CDS), Queensland University of Technology (QUT), Brisbane, Australia
| | - Ian Barr
- World Health Organization Collaborating Centre for Reference and Research on Influenza, VIDRL, Doherty Institute, Melbourne, Australia; Department of Microbiology and Immunology, University of Melbourne, Victoria, Australia
| | - Liping Wang
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Division of Infectious disease, Chinese Centre for Disease Control and Prevention, China
| | - Weizhong Yang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China
| | - Sotiris Vardoulakis
- HEAL Global Research Centre, Health Research Institute, University of Canberra, ACT Canberra, 2601, Australia
| | - Hilary Bambrick
- National Centre for Epidemiology and Population Health, The Australian National University, ACT 2601 Canberra, Australia
| | - Wenbiao Hu
- Ecosystem Change and Population Health Research Group, School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia.
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Esmaeilzadeh P. Privacy Concerns About Sharing General and Specific Health Information on Twitter: Quantitative Study. JMIR Form Res 2024; 8:e45573. [PMID: 38214964 PMCID: PMC10789368 DOI: 10.2196/45573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 07/19/2023] [Accepted: 12/14/2023] [Indexed: 01/13/2024] Open
Abstract
BACKGROUND Twitter is a common platform for people to share opinions, discuss health-related topics, and engage in conversations with a wide audience. Twitter users frequently share health information related to chronic diseases, mental health, and general wellness topics. However, sharing health information on Twitter raises privacy concerns as it involves sharing personal and sensitive data on a web-based platform. OBJECTIVE This study aims to adopt an interactive approach and develop a model consisting of privacy concerns related to web-based vendors and web-based peers. The research model integrates the 4 dimensions of concern for information privacy that express concerns related to the practices of companies and the 4 dimensions of peer privacy concern that reflect concerns related to web-based interactions with peers. This study examined how this interaction may affect individuals' information-sharing behavior on Twitter. METHODS Data were collected from 329 Twitter users in the United States using a web-based survey. RESULTS Results suggest that privacy concerns related to company practices might not significantly influence the sharing of general health information, such as details about hospitals and medications. However, privacy concerns related to companies and third parties can negatively shape the disclosure of specific health information, such as personal medical issues (β=-.43; P<.001). Findings show that peer-related privacy concerns significantly predict sharing patterns associated with general (β=-.38; P<.001) and specific health information (β=-.72; P<.001). In addition, results suggest that people may disclose more general health information than specific health information owing to peer-related privacy concerns (t165=4.72; P<.001). The model explains 41% of the variance in general health information disclosure and 67% in specific health information sharing on Twitter. CONCLUSIONS The results can contribute to privacy research and propose some practical implications. The findings provide insights for developers, policy makers, and health communication professionals about mitigating privacy concerns in web-based health information sharing. It particularly underlines the importance of addressing peer-related privacy concerns. The study underscores the need to build a secure and trustworthy web-based environment, emphasizing the significance of peer interactions and highlighting the need for improved regulations, clear data handling policies, and users' control over their own data.
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Affiliation(s)
- Pouyan Esmaeilzadeh
- Department of Information Systems and Business Analytics, College of Business, Florida International University, Miami, FL, United States
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3
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Wang X, Wang X, Zhai Y. Advancing sustainable financial management in greening companies through big data technology innovation. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:5641-5654. [PMID: 38123775 DOI: 10.1007/s11356-023-30950-6] [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: 07/13/2023] [Accepted: 11/03/2023] [Indexed: 12/23/2023]
Abstract
Incorporating sustainability into financial management procedures has emerged as a critical component in the modern business landscape for organizations looking to strengthen their environmental stewardship while guaranteeing financial viability. The study "Advancing Sustainable Financial Management in Greening Companies through Big Data Technology Innovation" explains the crucial role that big data technologies play in empowering businesses to incorporate environmental sustainability into their financial management strategies. The research the strong link between big data analytics and the optimization of sustainable financial management in businesses from year 1990 to 2022. The study's findings show that big data analytics enables firms to make data-driven decisions, significantly increasing the effectiveness of their sustainability activities. With the enormous amounts of data that big data technologies can analyze, businesses can access actionable insights that make it easier to identify and reduce environmental impacts, use resources more efficiently, and streamline supply chains to support sustainability. To emphasizes the businesses can match their financial goals with sustainability objectives through big data technology without sacrificing profitability. Big data analytics may help businesses assess environmental risks and find possibilities for sustainable investment, enabling them to make well-informed financial decisions consistent with their commitment to environmental stewardship. The conclusion emphasizes the businesses to adopt big data technology focusing on long-term financial management strategically. The growing environmental problems that endanger the world's ecosystems underscore even more how crucial it is to include these advancements. Therefore, integrating sustainability into financial management using big data technology is not just a choice but a requirement for businesses to succeed in this century. The study demonstrated that the businesses, decision-makers, and other stakeholders to understand and use big data technologies' potential to advance sustainable financial management and build more resilient and sustainable corporate environments.
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Affiliation(s)
- Xueyan Wang
- School of Accounting and Finance,Shijiazhuang Vocational College of Finance and Economics, Shijiazhuang, 050000, Hebei, China
| | - Xiaoli Wang
- Department of Law and Economic Trade, Hebei Vocational College of Labour Relations, Shijiazhuang, 050000, Hebei, China.
| | - Yingying Zhai
- School of Accounting and Finance,Shijiazhuang Vocational College of Finance and Economics, Shijiazhuang, 050000, Hebei, China
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Biswas MR, Shah Z. Extracting factors associated with vaccination from Twitter data and mapping to behavioral models. Hum Vaccin Immunother 2023; 19:2281729. [PMID: 38013461 PMCID: PMC10760324 DOI: 10.1080/21645515.2023.2281729] [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: 03/07/2023] [Accepted: 11/05/2023] [Indexed: 11/29/2023] Open
Abstract
Social media platform, particularly Twitter, is a rich data source that allows monitoring of public opinions and attitudes toward vaccines.Established behavioral models like the 5C psychological antecedents model and the Health Belief Model (HBM) provide a well-structured framework for analyzing shifts in vaccine-related behavior. This study examines if the extracted data from Twitter contains valuable insights regarding public attitudes toward vaccines and can be mapped to two behavioral models. This study focuses on the Arab population, and a search was carried out on Twitter using: ' تلقيحي OR تطعيم OR تطعيمات OR لقاح OR لقاحات' for two years from January 2020 to January 2022. Then, BERTopicmodeling was applied, and several topics were extracted. Finally, the topics were manually mapped to the factors of the 5C model and HBM. 1,068,466 unique users posted 3,368,258 vaccine-related tweets in Arabic. Topic modeling generated 25 topics, which were mapped to the 15 factors of the 5C model and HBM. Among the users, 32.87%were male, and 18.06% were female. A significant 55.77% of the users were from the MENA (Middle East and North Africa) region. Twitter users were more inclined to accept vaccines when they trusted vaccine safety and effectiveness, but vaccine hesitancy increased due to conspiracy theories and misinformation. The association of topics with these theoretical frameworks reveals the availability and diversity of Twitter data that can predict behavioral change toward vaccines. It allows the preparation of timely and effective interventions for vaccination programs compared to traditional methods.
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Affiliation(s)
- Md. Rafiul Biswas
- Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Zubair Shah
- Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
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Chua S, Sabang JA, Chew KS, Nohuddin PNE. Textual Analysis of Tweets Associated with Domestic Violence. IRANIAN JOURNAL OF PUBLIC HEALTH 2023; 52:2402-2411. [PMID: 38106840 PMCID: PMC10719714 DOI: 10.18502/ijph.v52i11.14039] [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: 08/07/2022] [Accepted: 10/19/2023] [Indexed: 12/19/2023]
Abstract
Background Domestic violence is a global public health concern as stated by World Health Organization. We aimed to conduct a textual analysis of tweets associated with domestic violence through keyword identification, word trends and word collocations. The data was obtained from Twitter, focusing on publicly available tweets written in English. The objectives are to find out if the identified keywords, word trends and word collocations can help differentiate between domestic violence-related tweets and non-domestic violence-related tweets, as well as, to analyze the textual characteristics of domestic violence-related tweets and non-domestic violence-related tweets. Methods Overall, 11,041 tweets were collected using a few keywords over a period of 15 days from 22 March 2021 to 5 April 2021. A text analysis approach was used to discover the most frequent keywords used, the word trends of those keywords and the word collocations of the keywords in differentiating between domestic violence-related or non-domestic violence-related tweets. Results Domestic violence-related tweets and non-domestic violence-related tweets had differentiating characteristics, despite sharing several main keywords. In particular, keywords like "domestic", "violence" and "suicide" featured prominently in domestic-violence related tweets but not in non-domestic violence-related tweets. Significant differences could also be seen in the frequency of keywords and the word trends in the collection of the tweets. Conclusion These findings are significant in helping to automate the flagging of domestic-violence related tweets and alert the authorities so that they can take proactive steps such as assisting the victims in getting medical, police and legal help as needed.
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Affiliation(s)
- Stephanie Chua
- Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, Sarawak, Malaysia
| | - Janice Allison Sabang
- Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, Sarawak, Malaysia
| | - Keng Sheng Chew
- Faculty of Medicine and Health Sciences, Universiti Malaysia Sarawak, Sarawak, Malaysia
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Gao J, Gallegos GA, West JF. Public Health Policy, Political Ideology, and Public Emotion Related to COVID-19 in the U.S. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:6993. [PMID: 37947551 PMCID: PMC10649259 DOI: 10.3390/ijerph20216993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 10/25/2023] [Accepted: 10/26/2023] [Indexed: 11/12/2023]
Abstract
Social networks, particularly Twitter 9.0 (known as X as of 23 July 2023), have provided an avenue for prompt interactions and sharing public health-related concerns and emotions, especially during the COVID-19 pandemic when in-person communication became less feasible due to stay-at-home policies in the United States (U.S.). The study of public emotions extracted from social network data has garnered increasing attention among scholars due to its significant predictive value for public behaviors and opinions. However, few studies have explored the associations between public health policies, local political ideology, and the spatial-temporal trends of emotions extracted from social networks. This study aims to investigate (1) the spatial-temporal clustering trends (or spillover effects) of negative emotions related to COVID-19; and (2) the association relationships between public health policies such as stay-at-home policies, political ideology, and the negative emotions related to COVID-19. This study employs multiple statistical methods (zero-inflated Poisson (ZIP) regression, random-effects model, and spatial autoregression (SAR) model) to examine relationships at the county level by using the data merged from multiple sources, mainly including Twitter 9.0, Johns Hopkins, and the U.S. Census Bureau. We find that negative emotions related to COVID-19 extracted from Twitter 9.0 exhibit spillover effects, with counties implementing stay-at-home policies or leaning predominantly Democratic showing higher levels of observed negative emotions related to COVID-19. These findings highlight the impact of public health policies and political polarization on spatial-temporal public emotions exhibited in social media. Scholars and policymakers can benefit from understanding how public policies and political ideology impact public emotions to inform and enhance their communication strategies and intervention design during public health crises such as the COVID-19 pandemic.
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Affiliation(s)
- Jingjing Gao
- Department of Management, Policy and Community Health, School of Public Health, The University of Texas Health Science Center at Houston (UTHealth Houston), El Paso, TX 79905, USA;
| | - Gabriela A. Gallegos
- Department of Management, Policy and Community Health, School of Public Health, The University of Texas Health Science Center at Houston (UTHealth Houston), El Paso, TX 79905, USA;
| | - Joe F. West
- College of Health Sciences, The University of North Carolina at Pembroke, Pembroke, NC 28372, USA;
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Pandey DP, Thapa NB. Analysis of News Media-Reported Snakebite Envenoming in Nepal during 2010-2022. PLoS Negl Trop Dis 2023; 17:e0011572. [PMID: 37639403 PMCID: PMC10491300 DOI: 10.1371/journal.pntd.0011572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 09/08/2023] [Accepted: 08/04/2023] [Indexed: 08/31/2023] Open
Abstract
BACKGROUND Snakebite envenoming is a well-known medical emergency in the Terai of Nepal in particular. However, there is an epidemiological knowledge gap. The news media data available online provide substantial information on envenomings. Assessing this information can be a pristine approach for understanding snakebite epidemiology and conducting knowledge-based interventions. We firstly analyzed news media-reported quantitative information on conditions under which bites occur, treatment-seeking behavior of victims, and outcomes of snakebite envenomings in Nepal. METHODOLOGY/PRINCIPAL FINDINGS We analyzed 308 Nepalese snakebite envenomed cases reported in 199 news media articles published between 2010 and 2022 using descriptive statistics, Wilcoxon, and Chi-square tests to know why and how victims were bitten, their treatment-seeking behavior, and the outcomes. These envenomated cases known with substantial information represented 48 districts (mostly located in the Terai region) of Nepal. These envenomings mostly occurred in residential areas affecting children. Generally, envenomings among males and females were not significantly different. But, in residential areas, females were more envenomed than males. Further, victims' extremities were often exposed to venomous snakebites while their active status and these episodes often occurred at night while victims were passive during snakebites indoors and immediate surroundings of houses. Snakebite deaths were less among referred than non-referred cases, males than females, and while active than passive conditions of victims. CONCLUSION/SIGNIFICANCE The most of reported envenomed patients were children, and most envenomings were due to cobra bites. Consultation with traditional healers complicated snakebite management. In most cases, deaths that occur without medical interventions are a severe snakebite consequence in Nepal. Further, several deaths in urban areas and mountains and higher hills of Nepal suggest immediate need of snakebite management interventions in the most affected districts. Therefore, there is an urgent need to immediately admit Nepalese snakebite victims to nearby snakebite treatment centers without adopting non-recommended prehospital interventions. The strategies for preventing snakebite and controlling venom effects should also include hilly and mountain districts where snakebite-associated deaths are reported.
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Affiliation(s)
- Deb P. Pandey
- Department of Veterinary Microbiology and Parasitology, Agriculture and Forestry University, Rampur, Chitwan, Bagmati Province, Nepal
| | - Narayan B. Thapa
- Department of Pediatrics, Bharatpur Hospital, Bharatpur, Chitwan, Bagmati Province, Nepal
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Keshavamurthy R, Charles LE. Predicting Kyasanur forest disease in resource-limited settings using event-based surveillance and transfer learning. Sci Rep 2023; 13:11067. [PMID: 37422454 PMCID: PMC10329696 DOI: 10.1038/s41598-023-38074-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 07/02/2023] [Indexed: 07/10/2023] Open
Abstract
In recent years, the reports of Kyasanur forest disease (KFD) breaking endemic barriers by spreading to new regions and crossing state boundaries is alarming. Effective disease surveillance and reporting systems are lacking for this emerging zoonosis, hence hindering control and prevention efforts. We compared time-series models using weather data with and without Event-Based Surveillance (EBS) information, i.e., news media reports and internet search trends, to predict monthly KFD cases in humans. We fitted Extreme Gradient Boosting (XGB) and Long Short Term Memory models at the national and regional levels. We utilized the rich epidemiological data from endemic regions by applying Transfer Learning (TL) techniques to predict KFD cases in new outbreak regions where disease surveillance information was scarce. Overall, the inclusion of EBS data, in addition to the weather data, substantially increased the prediction performance across all models. The XGB method produced the best predictions at the national and regional levels. The TL techniques outperformed baseline models in predicting KFD in new outbreak regions. Novel sources of data and advanced machine-learning approaches, e.g., EBS and TL, show great potential towards increasing disease prediction capabilities in data-scarce scenarios and/or resource-limited settings, for better-informed decisions in the face of emerging zoonotic threats.
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Affiliation(s)
- Ravikiran Keshavamurthy
- Pacific Northwest National Laboratory, Richland, WA, 99354, USA
- Paul G. Allen School for Global Health, Washington State University, Pullman, WA, 99164, USA
| | - Lauren E Charles
- Pacific Northwest National Laboratory, Richland, WA, 99354, USA.
- Paul G. Allen School for Global Health, Washington State University, Pullman, WA, 99164, USA.
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9
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Lane JM, Habib D, Curtis B. Linguistic Methodologies to Surveil the Leading Causes of Mortality: Scoping Review of Twitter for Public Health Data. J Med Internet Res 2023; 25:e39484. [PMID: 37307062 PMCID: PMC10337472 DOI: 10.2196/39484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 01/26/2023] [Accepted: 02/07/2023] [Indexed: 02/10/2023] Open
Abstract
BACKGROUND Twitter has become a dominant source of public health data and a widely used method to investigate and understand public health-related issues internationally. By leveraging big data methodologies to mine Twitter for health-related data at the individual and community levels, scientists can use the data as a rapid and less expensive source for both epidemiological surveillance and studies on human behavior. However, limited reviews have focused on novel applications of language analyses that examine human health and behavior and the surveillance of several emerging diseases, chronic conditions, and risky behaviors. OBJECTIVE The primary focus of this scoping review was to provide a comprehensive overview of relevant studies that have used Twitter as a data source in public health research to analyze users' tweets to identify and understand physical and mental health conditions and remotely monitor the leading causes of mortality related to emerging disease epidemics, chronic diseases, and risk behaviors. METHODS A literature search strategy following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extended guidelines for scoping reviews was used to search specific keywords on Twitter and public health on 5 databases: Web of Science, PubMed, CINAHL, PsycINFO, and Google Scholar. We reviewed the literature comprising peer-reviewed empirical research articles that included original research published in English-language journals between 2008 and 2021. Key information on Twitter data being leveraged for analyzing user language to study physical and mental health and public health surveillance was extracted. RESULTS A total of 38 articles that focused primarily on Twitter as a data source met the inclusion criteria for review. In total, two themes emerged from the literature: (1) language analysis to identify health threats and physical and mental health understandings about people and societies and (2) public health surveillance related to leading causes of mortality, primarily representing 3 categories (ie, respiratory infections, cardiovascular disease, and COVID-19). The findings suggest that Twitter language data can be mined to detect mental health conditions, disease surveillance, and death rates; identify heart-related content; show how health-related information is shared and discussed; and provide access to users' opinions and feelings. CONCLUSIONS Twitter analysis shows promise in the field of public health communication and surveillance. It may be essential to use Twitter to supplement more conventional public health surveillance approaches. Twitter can potentially fortify researchers' ability to collect data in a timely way and improve the early identification of potential health threats. Twitter can also help identify subtle signals in language for understanding physical and mental health conditions.
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Affiliation(s)
- Jamil M Lane
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Daniel Habib
- Technology and Translational Research Unit, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, United States
| | - Brenda Curtis
- Technology and Translational Research Unit, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, United States
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Zammarchi G, Mola F, Conversano C. Using sentiment analysis to evaluate the impact of the COVID-19 outbreak on Italy's country reputation and stock market performance. STAT METHOD APPL-GER 2023; 32:1-22. [PMID: 37360253 PMCID: PMC10068702 DOI: 10.1007/s10260-023-00690-5] [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] [Accepted: 03/12/2023] [Indexed: 04/05/2023]
Abstract
During the recent Coronavirus disease 2019 (COVID-19) outbreak, the microblogging service Twitter has been widely used to share opinions and reactions to events. Italy was one of the first European countries to be severely affected by the outbreak and to establish lockdown and stay-at-home orders, potentially leading to country reputation damage. We resort to sentiment analysis to investigate changes in opinions about Italy reported on Twitter before and after the COVID-19 outbreak. Using different lexicons-based methods, we find a breakpoint corresponding to the date of the first established case of COVID-19 in Italy that causes a relevant change in sentiment scores used as a proxy of the country's reputation. Next, we demonstrate that sentiment scores about Italy are associated with the values of the FTSE-MIB index, the Italian Stock Exchange main index, as they serve as early detection signals of changes in the values of FTSE-MIB. Lastly, we evaluate whether different machine learning classifiers were able to determine the polarity of tweets posted before and after the outbreak with a different level of accuracy.
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Affiliation(s)
- Gianpaolo Zammarchi
- Department of Economics and Business Science, University of Cagliari, Cagliari, Italy
| | - Francesco Mola
- Department of Economics and Business Science, University of Cagliari, Cagliari, Italy
| | - Claudio Conversano
- Department of Economics and Business Science, University of Cagliari, Cagliari, Italy
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11
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MacIntyre CR, Chen X, Kunasekaran M, Quigley A, Lim S, Stone H, Paik HY, Yao L, Heslop D, Wei W, Sarmiento I, Gurdasani D. Artificial intelligence in public health: the potential of epidemic early warning systems. J Int Med Res 2023; 51:3000605231159335. [PMID: 36967669 PMCID: PMC10052500 DOI: 10.1177/03000605231159335] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023] Open
Abstract
The use of artificial intelligence (AI) to generate automated early warnings in epidemic surveillance by harnessing vast open-source data with minimal human intervention has the potential to be both revolutionary and highly sustainable. AI can overcome the challenges faced by weak health systems by detecting epidemic signals much earlier than traditional surveillance. AI-based digital surveillance is an adjunct to-not a replacement of-traditional surveillance and can trigger early investigation, diagnostics and responses at the regional level. This narrative review focuses on the role of AI in epidemic surveillance and summarises several current epidemic intelligence systems including ProMED-mail, HealthMap, Epidemic Intelligence from Open Sources, BlueDot, Metabiota, the Global Biosurveillance Portal, Epitweetr and EPIWATCH. Not all of these systems are AI-based, and some are only accessible to paid users. Most systems have large volumes of unfiltered data; only a few can sort and filter data to provide users with curated intelligence. However, uptake of these systems by public health authorities, who have been slower to embrace AI than their clinical counterparts, is low. The widespread adoption of digital open-source surveillance and AI technology is needed for the prevention of serious epidemics.
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Affiliation(s)
- Chandini Raina MacIntyre
- Biosecurity Program, The Kirby Institute, Faculty of Medicine, University of New South Wales, Sydney, Australia
- College of Public Service & Community Solutions, Arizona State University, Tempe, United States
| | - Xin Chen
- Biosecurity Program, The Kirby Institute, Faculty of Medicine, University of New South Wales, Sydney, Australia
| | - Mohana Kunasekaran
- Biosecurity Program, The Kirby Institute, Faculty of Medicine, University of New South Wales, Sydney, Australia
| | - Ashley Quigley
- Biosecurity Program, The Kirby Institute, Faculty of Medicine, University of New South Wales, Sydney, Australia
| | - Samsung Lim
- Biosecurity Program, The Kirby Institute, Faculty of Medicine, University of New South Wales, Sydney, Australia
- School of Civil and Environmental Engineering, University of New South Wales, Sydney, Australia
| | - Haley Stone
- Biosecurity Program, The Kirby Institute, Faculty of Medicine, University of New South Wales, Sydney, Australia
| | - Hye-Young Paik
- School of Computer Science and Engineering, Faulty of Engineering, University of New South Wales, Sydney, Australia
| | - Lina Yao
- School of Computer Science and Engineering, Faulty of Engineering, University of New South Wales, Sydney, Australia
| | - David Heslop
- School of Population Health, Faculty of Medicine, University of New South Wales, Sydney, Australia
| | - Wenzhao Wei
- Biosecurity Program, The Kirby Institute, Faculty of Medicine, University of New South Wales, Sydney, Australia
| | - Ines Sarmiento
- Biosecurity Program, The Kirby Institute, Faculty of Medicine, University of New South Wales, Sydney, Australia
| | - Deepti Gurdasani
- William Harvey Research Institute, Queen Mary University of London, United Kingdom
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Swapnarekha H, Nayak J, Behera HS, Dash PB, Pelusi D. An optimistic firefly algorithm-based deep learning approach for sentiment analysis of COVID-19 tweets. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:2382-2407. [PMID: 36899539 DOI: 10.3934/mbe.2023112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
The unprecedented rise in the number of COVID-19 cases has drawn global attention, as it has caused an adverse impact on the lives of people all over the world. As of December 31, 2021, more than 2, 86, 901, 222 people have been infected with COVID-19. The rise in the number of COVID-19 cases and deaths across the world has caused fear, anxiety and depression among individuals. Social media is the most dominant tool that disturbed human life during this pandemic. Among the social media platforms, Twitter is one of the most prominent and trusted social media platforms. To control and monitor the COVID-19 infection, it is necessary to analyze the sentiments of people expressed on their social media platforms. In this study, we proposed a deep learning approach known as a long short-term memory (LSTM) model for the analysis of tweets related to COVID-19 as positive or negative sentiments. In addition, the proposed approach makes use of the firefly algorithm to enhance the overall performance of the model. Further, the performance of the proposed model, along with other state-of-the-art ensemble and machine learning models, has been evaluated by using performance metrics such as accuracy, precision, recall, the AUC-ROC and the F1-score. The experimental results reveal that the proposed LSTM + Firefly approach obtained a better accuracy of 99.59% when compared with the other state-of-the-art models.
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Affiliation(s)
- H Swapnarekha
- Department of Information Technology, Aditya Institute of Technology and Management (AITAM), Tekkali, Andhra Pradesh 532201, India
- Department of Information Technology, Veer Surendra Sai University of Technology, Burla 768018, India
| | - Janmenjoy Nayak
- Department of Computer Science, Maharaja Sriram Chandra Bhanja Deo University, Baripada, Odisha 757003, India
| | - H S Behera
- Department of Information Technology, Veer Surendra Sai University of Technology, Burla 768018, India
| | - Pandit Byomakesha Dash
- Department of Information Technology, Aditya Institute of Technology and Management (AITAM), Tekkali, Andhra Pradesh 532201, India
| | - Danilo Pelusi
- Communication Sciences, University of Teramo, Coste Sant'agostino Campus, Teramo 64100, Italy
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13
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Luo L, Wang Y, Liu H. COVID-19 personal health mention detection from tweets using dual convolutional neural network. EXPERT SYSTEMS WITH APPLICATIONS 2022; 200:117139. [PMID: 35399189 PMCID: PMC8976569 DOI: 10.1016/j.eswa.2022.117139] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 01/13/2022] [Accepted: 03/29/2022] [Indexed: 05/05/2023]
Abstract
Twitter offers extensive and valuable information on the spread of COVID-19 and the current state of public health. Mining tweets could be an important supplement for public health departments in monitoring the status of COVID-19 in a timely manner and taking the appropriate actions to minimize its impact. Identifying personal health mentions (PHM) is the first step of social media public health surveillance. It aims to identify whether a person's health condition is mentioned in a tweet, and it serves as a crucial method in tracking pandemic conditions in real time. However, social media texts contain noise, many creative and novel phrases, sarcastic emoji expressions, and misspellings. In addition, the class imbalance issue is usually very serious. To address these challenges, we built a COVID-19 PHM dataset containing more than 11,000 annotated tweets, and we proposed a dual convolutional neural network (CNN) framework using this dataset. An auxiliary CNN in the dual CNN structure provides supplemental information for the primary CNN in order to detect PHMs from tweets more effectively. The experiment shows that the proposed structure could alleviate the effect of class imbalance and could achieve promising results. This automated approach could monitor public health in real time and save disease-prevention departments from the tedious manual work in public health surveillance.
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Affiliation(s)
- Linkai Luo
- Department of Supply Chain and Information Management, The Hang Seng University of Hong Kong, Hong Kong Special Administrative Region
| | - Yue Wang
- Department of Supply Chain and Information Management, The Hang Seng University of Hong Kong, Hong Kong Special Administrative Region
| | - Hai Liu
- Department of Computing, The Hang Seng University of Hong Kong, Hong Kong Special Administrative Region
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14
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Davidson C, Hodge K. Implementing Online Discussion and Mind Mapping to Investigate a Disease Outbreak. JOURNAL OF MICROBIOLOGY & BIOLOGY EDUCATION 2022; 23:e00025-22. [PMID: 36061315 PMCID: PMC9429932 DOI: 10.1128/jmbe.00025-22] [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: 02/14/2022] [Accepted: 05/31/2022] [Indexed: 06/15/2023]
Abstract
The recent increase in online learning modalities due to coronavirus disease 2019 (COVID-19) has created a significant gap in real-time discussions on complex issues. This lack of enrichment from student discussions levies the concern of a deficiency in strong learning outcomes. This learning activity focused on mind mapping to facilitate small group discussions on the 2010 cholera outbreak in Haiti. Students learned about the disease triangle and cause-and-effect relationships on a large spatial and temporal scale. In this case, the three points of the triangle represented the pathogen (Vibrio cholerae), the environment (Haiti), and the hosts (Haitians). Each student in each small group was required to read a unique article to present to their group on the day of the activity. Using mind mapping, each group illustrated relationships that may have exacerbated the cholera outbreak. Learning outcomes were assessed through the evaluation of questions relevant to that week's exercise. Students were assessed on their ability to recognize relationships between the pathogen, environment, and hosts, as well as the ability to apply what they learned to the present-day COVID-19 pandemic. The disease triangle activity is readily accessible and can be easily implemented for identifying cause-and-effect relationships in large-scale systems. Importantly, this learning activity retained real-time discussion-based problem-solving for improving students' critical thinking skills and approaches to complex issues.
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Affiliation(s)
- Cole Davidson
- University of Vermont, Department of Pharmacology, Burlington, Vermont, USA
- University of Vermont, Department of Microbiology and Molecular Genetics, Burlington, Vermont, USA
| | - Karin Hodge
- University of Vermont, Department of Microbiology and Molecular Genetics, Burlington, Vermont, USA
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15
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Deep Similarity Analysis and Forecasting of Actual Outbreak of Major Infectious Diseases using Internet-Sourced Data. J Biomed Inform 2022; 133:104148. [PMID: 35878824 DOI: 10.1016/j.jbi.2022.104148] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 05/16/2022] [Accepted: 07/19/2022] [Indexed: 11/23/2022]
Abstract
Perhaps no other generation in the span of recorded human history has endured the risks of infectious diseases as has the current generation. The prevalence of infectious diseases is caused mainly by unlimited contact between people in a highly globalized world. Disease control and prevention (CDC) promptly collect and produce disease outbreak statistics, but CDCs rely on a curated, centralized collection system, and requires up to two weeks of lead time. Consequently, the quick release of disease outbreak information has become a great challenge. Infectious disease outbreak information is recorded and spread somewhere on the Internet much faster than CDC announcements, and Internet-sourced data have shown non-substitutable potential to watch and predict infectious disease outbreaks in advance. In this study, we performed a thorough analysis to show the similarity between the Korean Center of Disease Control (KCDC) infectious disease datasets and three Internet-sourced data for nine major infectious diseases in terms of time-series volume. The results show that many of infectious disease outbreak have strongly related to Internet-sourced data. We analyzed several factors that affect the similarity. Our analysis shows that the increase in the number of Internet-sourced data correlates with the increase in the number of infected people and thus, show the positive similarity. We also found that the greater the number of infectious disease outbreaks corresponds to having a wider spread of outbreak regions, in which it also proves to have higher similarity. We presented the prediction result of infectious disease outbreak using various Internet-sourced data and an effective deep learning algorithm. It showed that there are positive correlations between the number of infected people or the number of related web data and the prediction accuracy. We developed and currently operate a web-based system to show the similarity between KCDC and related Internet-sourced data for infectious diseases. This paper helps people to identify what kind of Internet-sourced data they need to use to predict and track a specific infectious disease outbreak. We considered as much as nine major diseases and three kinds of Internet-sourced data together, and we can say that our finding did not depend on specific infectious disease nor specific Internet-sourced data.
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16
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Grigsby-Toussaint D, Champagne A, Uhr J, Silva E, Noh M, Bradley A, Rashleigh P. US Black Maternal Health Advocacy Topics and Trends on Twitter: Temporal Infoveillance Study. JMIR INFODEMIOLOGY 2022; 2:e30885. [PMID: 35578642 PMCID: PMC9092478 DOI: 10.2196/30885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 08/12/2021] [Accepted: 02/16/2022] [Indexed: 11/26/2022]
Abstract
Background Black women in the United States disproportionately suffer adverse pregnancy and birth outcomes compared to White women. Economic adversity and implicit bias during clinical encounters may lead to physiological responses that place Black women at higher risk for adverse birth outcomes. The novel coronavirus disease of 2019 (COVID-19) further exacerbated this risk, as safety protocols increased social isolation in clinical settings, thereby limiting opportunities to advocate for unbiased care. Twitter, 1 of the most popular social networking sites, has been used to study a variety of issues of public interest, including health care. This study considers whether posts on Twitter accurately reflect public discourse during the COVID-19 pandemic and are being used in infodemiology studies by public health experts. Objective This study aims to assess the feasibility of Twitter for identifying public discourse related to social determinants of health and advocacy that influence maternal health among Black women across the United States and to examine trends in sentiment between 2019 and 2020 in the context of the COVID-19 pandemic. Methods Tweets were collected from March 1 to July 13, 2020, from 21 organizations and influencers and from 4 hashtags that focused on Black maternal health. Additionally, tweets from the same organizations and hashtags were collected from the year prior, from March 1 to July 13, 2019. Twint, a Python programming library, was used for data collection and analysis. We gathered the text of approximately 17,000 tweets, as well as all publicly available metadata. Topic modeling and k-means clustering were used to analyze the tweets. Results A variety of trends were observed when comparing the 2020 data set to the 2019 data set from the same period. The percentages listed for each topic are probabilities of that topic occurring in our corpus. In our topic models, tweets on reproductive justice, maternal mortality crises, and patient care increased by 67.46% in 2020 versus 2019. Topics on community, advocacy, and health equity increased by over 30% in 2020 versus 2019. In contrast, tweet topics that decreased in 2020 versus 2019 were as follows: tweets on Medicaid and medical coverage decreased by 27.73%, and discussions about creating space for Black women decreased by just under 30%. Conclusions The results indicate that the COVID-19 pandemic may have spurred an increased focus on advocating for improved reproductive health and maternal health outcomes among Black women in the United States. Further analyses are needed to capture a longer time frame that encompasses more of the pandemic, as well as more diverse voices to confirm the robustness of the findings. We also concluded that Twitter is an effective source for providing a snapshot of relevant topics to guide Black maternal health advocacy efforts.
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Affiliation(s)
- Diana Grigsby-Toussaint
- Department of Epidemiology School of Public Health Brown University Providence, RI United States
- Department of Behavioral and Social Sciences School of Public Health Brown University Providence, RI United States
| | | | - Justin Uhr
- Brown University Library Providence, RI United States
| | - Elizabeth Silva
- Department of Behavioral and Social Sciences School of Public Health Brown University Providence, RI United States
| | - Madeline Noh
- Department of Anthropology School of Public Health Brown University Providence, RI United States
| | - Adam Bradley
- Brown University Library Providence, RI United States
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17
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The where and when of COVID-19: Using ecological and Twitter-based assessments to examine impacts in a temporal and community context. PLoS One 2022; 17:e0264280. [PMID: 35196353 PMCID: PMC8865674 DOI: 10.1371/journal.pone.0264280] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 02/07/2022] [Indexed: 12/23/2022] Open
Abstract
In March 2020, residents of the Bronx, New York experienced one of the first significant community COVID-19 outbreaks in the United States. Focusing on intensive longitudinal data from 78 Bronx-based older adults, we used a multi-method approach to (1) examine 2019 to early pandemic (February-June 2020) changes in momentary psychological well-being of Einstein Aging Study (EAS) participants and (2) to contextualize these changes with community distress scores collected from public Twitter posts posted in Bronx County. We found increases in mean loneliness from 2019 to 2020; and participants that were higher in neuroticism had greater increases in thought unpleasantness and feeling depressed. Twitter-based Bronx community scores of anxiety, depressivity, and negatively-valenced affect showed elevated levels in 2020 weeks relative to 2019. Integration of EAS participant data and community data showed week-to-week fluctuations across 2019 and 2020. Results highlight how community-level data can characterize a rapidly changing environment to supplement individual-level data at no additional burden to individual participants.
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18
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Healthy vs. Unhealthy Food Images: Image Classification of Twitter Images. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19020923. [PMID: 35055742 PMCID: PMC8775411 DOI: 10.3390/ijerph19020923] [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: 11/30/2021] [Revised: 01/10/2022] [Accepted: 01/10/2022] [Indexed: 12/07/2022]
Abstract
Obesity is a modern public health problem. Social media images can capture eating behavior and the potential implications to health, but research for identifying the healthiness level of the food image is relatively under-explored. This study presents a deep learning architecture that transfers features from a 152 residual layer network (ResNet) for predicting the level of healthiness of food images that were built using images from the Google images search engine gathered in 2020. Features learned from the ResNet 152 were transferred to a second network to train on the dataset. The trained SoftMax layer was stacked on top of the layers transferred from ResNet 152 to build our deep learning model. We then evaluate the performance of the model using Twitter images in order to better understand the generalizability of the methods. The results show that the model is able to predict the images into their respective classes, including Definitively Healthy, Healthy, Unhealthy and Definitively Unhealthy at an F1-score of 78.8%. This finding shows promising results for classifying social media images by healthiness, which could contribute to maintaining a balanced diet at the individual level and also understanding general food consumption trends of the public.
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19
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Media discourse in China and Japan on the COVID-19 pandemic: comparative analysis of the first three months. JOURNAL OF INFORMATION COMMUNICATION & ETHICS IN SOCIETY 2022. [DOI: 10.1108/jices-05-2021-0047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Purpose
This study aims to analyze how English-language versions of e-newspapers in the first two countries affected, China and Japan, which are non-English-speaking countries and have different socio-economic and political settings, have highlighted Coronavirus disease 2019 (COVID-19) pandemic news and informed the global community.
Design/methodology/approach
A text-mining approach was used to explore experts’ thoughts as published by the two leading English-language newspapers in China and Japan from January to March 2020. This study analyzes the Opinion section, which mainly comprises editorial and the op-ed section. The current study groups all editorial discussions and highlights into ten major aspects, which cover health, economy, politics, culture and others.
Findings
Within the first three months, the media in both China and Japan shifted their focus from health and preparedness to the economy, politics and social welfare. Governance and social welfare were key concerns in China’s news media, while, in contrast, global politics received the highest level of attention from experts in Japan’s news media. Environment and technologies aspects did not receive much attention by the expert’s columns.
Originality/value
At the initial stage of a world crisis, how leading nations and initially affected nations deal with the problem, how media play their role and guide mass population with experts’ thoughts are highlighted here. The understanding developed in this study can provide guidance to news media in other countries in playing effective roles in the management of this health crisis and catastrophes.
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20
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Moore JB, Harris JK, Hutti ET. 'Falsehood flies, and the truth comes limping after it': social media and public health. Curr Opin Psychiatry 2021; 34:485-490. [PMID: 34175868 PMCID: PMC8384694 DOI: 10.1097/yco.0000000000000730] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW To highlight the various uses of social media by public health practitioners and organizations, with special emphasis on how social media has been successfully applied and where applications have struggled to achieve the desired effects. RECENT FINDINGS Social media has been used effectively in improving the timeliness and accuracy of public health surveillance. Social media has also been used to communicate information between public health organizations and reinforce consistent messaging about enduring threats to public health. It has been applied with some success to coordinate of disaster response and for keeping the public informed during other emergency situations. However, social media has also been weaponized against the public health community to spread disinformation and misinformation, and the public health community has yet to devise a successful strategy to mitigate this destructive use of social media. SUMMARY Social media can be an effective tool for public health practitioners and organizations who seek to disseminate information on a daily basis, rapidly convey information in emergent situations, and battle misinformation. Social media has been uniquely valuable and distinctly destructive when it comes to protecting and improving public health.
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Affiliation(s)
- Justin B Moore
- Department of Implementation Science, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Jenine K Harris
- George Warren Brown School of Social Work, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Ellen T Hutti
- George Warren Brown School of Social Work, Washington University in St. Louis, St. Louis, Missouri, USA
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21
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Yeung AWK, Kletecka-Pulker M, Eibensteiner F, Plunger P, Völkl-Kernstock S, Willschke H, Atanasov AG. Implications of Twitter in Health-Related Research: A Landscape Analysis of the Scientific Literature. Front Public Health 2021; 9:654481. [PMID: 34307273 PMCID: PMC8299201 DOI: 10.3389/fpubh.2021.654481] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Accepted: 06/09/2021] [Indexed: 01/07/2023] Open
Abstract
Background: Twitter, representing a big social media network, is broadly used for the communication of health-related information. In this work, we aimed to identify and analyze the scientific literature on Twitter use in context of health by utilizing a bibliometric approach, in order to obtain quantitative information on dominant research topics, trending themes, key publications, scientific institutions, and prolific researchers who contributed to this scientific area. Methods: Web of Science electronic database was searched to identify relevant papers on Twitter and health. Basic bibliographic data was obtained utilizing the "Analyze" function of the database. Full records and cited references were exported to VOSviewer, a dedicated bibliometric software, for further analysis. A term map and a keyword map were synthesized to visualize recurring words within titles, abstracts and keywords. Results: The analysis was based on the data from 2,582 papers. The first papers were published in 2009, and the publication count increased rapidly since 2015. Original articles and reviews were published in a ratio of 10.6:1. The Journal of Medical Internet Research was the top journal, and the United States had contributions to over half (52%) of these publications, being the home-country of eight of the top ten most productive institutions. Keyword analysis identified six topically defined clusters, with professional education in healthcare being the top theme cluster (consisting of 66 keywords). The identified papers often investigated Twitter together with other social media, such as YouTube and Facebook. Conclusions: A great diversity of themes was found in the identified papers, including: professional education in healthcare, big data and sentiment analysis, social marketing and substance use, physical and emotional well-being of young adults, and public health and health communication. Our quantitative analysis outlines Twitter as both, an increasingly popular data source, and a highly versatile tool for health-related research.
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Affiliation(s)
- Andy Wai Kan Yeung
- Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong, China
| | - Maria Kletecka-Pulker
- Ludwig Boltzmann Institute for Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria.,Institute for Ethics and Law in Medicine, University of Vienna, Vienna, Austria
| | - Fabian Eibensteiner
- Ludwig Boltzmann Institute for Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria.,Division of Pediatric Nephrology and Gastroenterology, Department of Pediatrics and Adolescent Medicine, Comprehensive Center for Pediatrics, Medical University of Vienna, Vienna, Austria
| | - Petra Plunger
- Ludwig Boltzmann Institute for Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria
| | - Sabine Völkl-Kernstock
- Ludwig Boltzmann Institute for Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria
| | - Harald Willschke
- Ludwig Boltzmann Institute for Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria.,Department of Anaesthesia, Intensive Care Medicine and Pain Medicine, Medical University Vienna, Vienna, Austria
| | - Atanas G Atanasov
- Ludwig Boltzmann Institute for Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria.,Institute of Genetics and Animal Biotechnology of the Polish Academy of Sciences, Magdalenka, Poland.,Institute of Neurobiology, Bulgarian Academy of Sciences, Sofia, Bulgaria.,Department of Pharmaceutical Sciences, University of Vienna, Vienna, Austria
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22
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Turning value into action: Healthcare workers using digital media advocacy to drive change. PLoS One 2021; 16:e0250875. [PMID: 33914809 PMCID: PMC8084157 DOI: 10.1371/journal.pone.0250875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 04/15/2021] [Indexed: 12/02/2022] Open
Abstract
Background The standard method of sharing information in academia is the scientific journal. Yet health advocacy requires alternative methods to reach key stakeholders to drive change. The purpose of this study was to analyze the impact of social media and public narrative for advocacy in matters of firearm-related injury and death. Study design The movement This Is Our Lane was evaluated through the #ThisIsOurLane and #ThisIsMyLane hashtags. Sources were assessed from November 2018 through March 2019. Analyses specifically examined message volume, time course, global engagement, and content across Twitter, scientific literature, and mass media. Twitter data were analyzed via Symplur Signals. Scientific literature reviews were performed using PubMed, EMBASE, Web of Science, and Google Scholar. Mass media was compiled using Access World News/Newsbank, Newspaper Source, and Google. Results A total of 507,813 tweets were shared using #ThisIsOurLane, #ThisIsMyLane, or both (co-occurrence 21–39%). Fifteen scientific items and n = 358 mass media publications were published during the study period; the latter included articles, blogs, television interviews, petitions, press releases, and audio interviews/podcasts. Peak messaging appeared first on Twitter on November 10th, followed by mass media on November 12th and 20th, and scientific publications during December. Conclusions Social media enables clinicians to quickly disseminate information about a complex public health issue like firearms to the mainstream media, scientific community, and general public alike. Humanized data resonates with people and has the ability to transcend the barriers of language, culture, and geography. Showing society the reality of caring for firearm-related injuries through healthcare worker stories via digital media appears to be effective in shaping the public agenda and influencing real-world events.
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23
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Mutual Influence of Users Credibility and News Spreading in Online Social Networks. FUTURE INTERNET 2021. [DOI: 10.3390/fi13050107] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
A real-time news spreading is now available for everyone, especially thanks to Online Social Networks (OSNs) that easily endorse gate watching, so the collective intelligence and knowledge of dedicated communities are exploited to filter the news flow and to highlight and debate relevant topics. The main drawback is that the responsibility for judging the content and accuracy of information moves from editors and journalists to online information users, with the side effect of the potential growth of fake news. In such a scenario, trustworthiness about information providers cannot be overlooked anymore, rather it more and more helps in discerning real news from fakes. In this paper we evaluate how trustworthiness among OSN users influences the news spreading process. To this purpose, we consider the news spreading as a Susceptible-Infected-Recovered (SIR) process in OSN, adding the contribution credibility of users as a layer on top of OSN. Simulations with both fake and true news spreading on such a multiplex network show that the credibility improves the diffusion of real news while limiting the propagation of fakes. The proposed approach can also be extended to real social networks.
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24
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Alnazzawi N. Building a semantically annotated corpus for chronic disease complications using two document types. PLoS One 2021; 16:e0247319. [PMID: 33735207 PMCID: PMC7971867 DOI: 10.1371/journal.pone.0247319] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Accepted: 02/04/2021] [Indexed: 11/19/2022] Open
Abstract
Narrative information in electronic health records (EHRs) contains a wealth of information related to patient health conditions. In addition, people use Twitter to express their experiences regarding personal health issues, such as medical complaints, symptoms, treatments, lifestyle, and other factors. Both genres of text include different types of health-related information concerning disease complications and risk factors. Knowing detailed information about controlling disease risk factors has a great impact on modifying these risks and subsequently preventing disease complications. Text-mining tools provide efficient solutions to extract and integrate vital information related to disease complications hidden in the large volume of the narrative text. However, the development of text-mining tools depends on the availability of an annotated corpus. In response, we have developed the PrevComp corpus, which is annotated with information relevant to the identification of disease complications, underlying risk factors, and prevention measures, in the context of the interaction between hypertension and diabetes. The corpus is unique and novel in terms of the very specific topic in the biomedical domain and as an integration of information from both EHRs and tweets collected from Twitter. The annotation scheme was designed with guidance by a domain expert, and two further domain experts performed the annotation, resulting in a high-quality annotation, with agreement rate F-scores as high as 0.60 and 0.75 for EHRs and tweets, respectively.
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Affiliation(s)
- Noha Alnazzawi
- Department of Computer Science and Engineering, Royal Commission for Jubail and Yanbu, Yanbu University College, Yanbu Industrial City, Saudi Arabia
- * E-mail:
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25
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Prieto Santamaría L, Tuñas JM, Fernández Peces-Barba D, Jaramillo A, Cotarelo M, Menasalvas E, Conejo Fernández A, Arce A, Gil de Miguel A, Rodríguez González A. Influenza and Measles-MMR: two case study of the trend and impact of vaccine-related Twitter posts in Spanish during 2015-2018. Hum Vaccin Immunother 2021; 18:1-16. [PMID: 33662222 PMCID: PMC9128558 DOI: 10.1080/21645515.2021.1877597] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Social media, and in particularly Twitter, can be a resource of enormous value to retrieve information about the opinion of general population to vaccines. The increasing popularity of this social media has allowed to use its content to have a clear picture of their users on this topic. In this paper, we perform a study about vaccine-related messages published in Spanish during 2015-2018. More specifically, the paper has focused on two specific diseases: influenza and measles (and MMR as its vaccine). By also including an analysis about the sentiment expressed on the published tweets, we have been able to identify the type of messages that are published on Twitter with respect these two pathologies and their vaccines. Results showed that in contrary on popular opinions, most of the messages published are non-negative. On the other hand, the analysis showed that some messages attracted a huge attention and provoked peaks in the number of published tweets, explaining some changes in the observed trends.
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Affiliation(s)
- Lucia Prieto Santamaría
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, Spain.,Escuela Técnica Superior de Ingenieros Informáticos, Universidad Politécnica de Madrid, Boadilla del Monte, Spain
| | - Juan Manuel Tuñas
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, Spain
| | | | | | - Manuel Cotarelo
- Global Medical and Scientific Affairs, MSD España, Madrid, Spain
| | - Ernestina Menasalvas
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, Spain.,Escuela Técnica Superior de Ingenieros Informáticos, Universidad Politécnica de Madrid, Boadilla del Monte, Spain
| | | | | | - Angel Gil de Miguel
- Departamento de Especialidades Médicas y Salud Pública, Facultad de Ciencias de la Salud, Universidad Rey Juan Carlos, Madrid, Spain
| | - Alejandro Rodríguez González
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, Spain.,Escuela Técnica Superior de Ingenieros Informáticos, Universidad Politécnica de Madrid, Boadilla del Monte, Spain
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26
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Zhuang Z, Cao P, Zhao S, Han L, He D, Yang L. The shortage of hospital beds for COVID-19 and non-COVID-19 patients during the lockdown of Wuhan, China. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:200. [PMID: 33708827 PMCID: PMC7940947 DOI: 10.21037/atm-20-5248] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Background The 76-day lockdown of Wuhan city has successfully contained the first wave of the coronavirus disease 2019 (COVID-19) outbreak. However, to date few studies have evaluated the hospital bed shortage for COVID-19 during the lockdown and none for non-COVID-19 patients, although such data are important for better preparedness of the future outbreak. Methods We built a compartmental model to estimate the daily numbers of hospital bed shortage for patients with mild, severe and critical COVID-19, taking account of underreport and diagnosis delay. Results The maximal daily shortage of inpatient beds for mild, severe and critical COVID-19 patients was 43,960 (95% confidence interval: 35,246, 52,929), 2,779 (1,395, 4,163) and 196 (143, 250) beds in early February 2020. An earlier or later lockdown would have greatly increased the shortage of hospital beds in Wuhan. The overwhelmed healthcare system might have delayed the provision of health care to both COVID-19 and non-COVID-19 patients during the lockdown. The second wave in Wuhan could have occurred in June 2020 if social distancing measures had waned in early March 2020. The hospital bed shortage was estimated much smaller in the potential second wave than in the first one. Conclusions Our findings suggest that the timing and strength of lockdown is important for the containment of the COVID-19 outbreaks. The healthcare needs of non-COVID-19 patients in the pandemic warrant more investigations.
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Affiliation(s)
- Zian Zhuang
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
| | - Peihua Cao
- Clinical Research Center, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Shi Zhao
- JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China.,Shenzhen Research Institute of Chinese University of Hong Kong, Shenzhen, China
| | - Lefei Han
- School of Nursing, Hong Kong Polytechnic University, Hong Kong, China
| | - Daihai He
- Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
| | - Lin Yang
- School of Nursing, Hong Kong Polytechnic University, Hong Kong, China
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27
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Cui H, Kertész J. Attention dynamics on the Chinese social media Sina Weibo during the COVID-19 pandemic. EPJ DATA SCIENCE 2021; 10:8. [PMID: 33552838 PMCID: PMC7856455 DOI: 10.1140/epjds/s13688-021-00263-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Accepted: 01/17/2021] [Indexed: 05/05/2023]
Abstract
UNLABELLED Understanding attention dynamics on social media during pandemics could help governments minimize the effects. We focus on how COVID-19 has influenced the attention dynamics on the biggest Chinese microblogging website Sina Weibo during the first four months of the pandemic. We study the real-time Hot Search List (HSL), which provides the ranking of the most popular 50 hashtags based on the amount of Sina Weibo searches. We show how the specific events, measures and developments during the epidemic affected the emergence of different kinds of hashtags and the ranking on the HSL. A significant increase of COVID-19 related hashtags started to occur on HSL around January 20, 2020, when the transmission of the disease between humans was announced. Then very rapidly a situation was reached where COVID-related hashtags occupied 30-70% of the HSL, however, with changing content. We give an analysis of how the hashtag topics changed during the investigated time span and conclude that there are three periods separated by February 12 and March 12. In period 1, we see strong topical correlations and clustering of hashtags; in period 2, the correlations are weakened, without clustering pattern; in period 3, we see a potential of clustering while not as strong as in period 1. We further explore the dynamics of HSL by measuring the ranking dynamics and the lifetimes of hashtags on the list. This way we can obtain information about the decay of attention, which is important for decisions about the temporal placement of governmental measures to achieve permanent awareness. Furthermore, our observations indicate abnormally higher rank diversity in the top 15 ranks on HSL due to the COVID-19 related hashtags, revealing the possibility of algorithmic intervention from the platform provider. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1140/epjds/s13688-021-00263-0.
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Affiliation(s)
- Hao Cui
- Department of Network and Data Science, Central European University, Quellenstrasse 51, A-1100 Vienna, Austria
| | - János Kertész
- Department of Network and Data Science, Central European University, Quellenstrasse 51, A-1100 Vienna, Austria
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28
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Identifying Polarity in Tweets from an Imbalanced Dataset about Diseases and Vaccines Using a Meta-Model Based on Machine Learning Techniques. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10249019] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Sentiment analysis is one of the hottest topics in the area of natural language. It has attracted a huge interest from both the scientific and industrial perspective. Identifying the sentiment expressed in a piece of textual information is a challenging task that several commercial tools have tried to address. In our aim of capturing the sentiment expressed in a set of tweets retrieved for a study about vaccines and diseases during the period 2015–2018, we found that some of the main commercial tools did not allow an accurate identification of the sentiment expressed in a tweet. For this reason, we aimed to create a meta-model which used the results of the commercial tools to improve the results of the tools individually. As part of this research, we had to deal with the problem of unbalanced data. This paper presents the main results in creating a metal-model from three commercial tools to the correct identification of sentiment in tweets by using different machine-learning techniques and methods and dealing with the unbalanced data problem.
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29
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Bornmann L, Haunschild R, Patel VM. Are papers addressing certain diseases perceived where these diseases are prevalent? The proposal to use Twitter data as social-spatial sensors. PLoS One 2020; 15:e0242550. [PMID: 33216816 PMCID: PMC7678968 DOI: 10.1371/journal.pone.0242550] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Accepted: 11/05/2020] [Indexed: 11/18/2022] Open
Abstract
We propose to use Twitter data as social-spatial sensors. This study deals with the question whether research papers on certain diseases are perceived by people in regions (worldwide) that are especially concerned by these diseases. Since (some) Twitter data contain location information, it is possible to spatially map the activity of Twitter users referring to certain papers (e.g., dealing with tuberculosis). The resulting maps reveal whether heavy activity on Twitter is correlated with large numbers of people having certain diseases. In this study, we focus on tuberculosis, human immunodeficiency virus (HIV), and malaria, since the World Health Organization ranks these diseases as the top three causes of death worldwide by a single infectious agent. The results of the social-spatial Twitter maps (and additionally performed regression models) reveal the usefulness of the proposed sensor approach. One receives an impression of how research papers on the diseases have been perceived by people in regions that are especially concerned by these diseases. Our study demonstrates a promising approach for using Twitter data for research evaluation purposes beyond simple counting of tweets.
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Affiliation(s)
- Lutz Bornmann
- Division for Science and Innovation Studies, Administrative Headquarters of the Max Planck Society, Munich, Germany
- * E-mail:
| | - Robin Haunschild
- Max Planck Institute for Solid State Research, Stuttgart, Germany
| | - Vanash M. Patel
- Department of Surgery and Cancer, Queen Elizabeth the Queen Mother Wing, St. Mary’s Hospital, London, United Kingdom
- Department of Colorectal Surgery, West Hertfordshire NHS Trust, Watford General Hospital, Watford, Hertfordshire, United Kingdom
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30
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Hung M, Lauren E, Hon ES, Birmingham WC, Xu J, Su S, Hon SD, Park J, Dang P, Lipsky MS. Social Network Analysis of COVID-19 Sentiments: Application of Artificial Intelligence. J Med Internet Res 2020; 22:e22590. [PMID: 32750001 PMCID: PMC7438102 DOI: 10.2196/22590] [Citation(s) in RCA: 109] [Impact Index Per Article: 27.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 07/30/2020] [Accepted: 08/03/2020] [Indexed: 11/23/2022] Open
Abstract
Background The coronavirus disease (COVID-19) pandemic led to substantial public discussion. Understanding these discussions can help institutions, governments, and individuals navigate the pandemic. Objective The aim of this study is to analyze discussions on Twitter related to COVID-19 and to investigate the sentiments toward COVID-19. Methods This study applied machine learning methods in the field of artificial intelligence to analyze data collected from Twitter. Using tweets originating exclusively in the United States and written in English during the 1-month period from March 20 to April 19, 2020, the study examined COVID-19–related discussions. Social network and sentiment analyses were also conducted to determine the social network of dominant topics and whether the tweets expressed positive, neutral, or negative sentiments. Geographic analysis of the tweets was also conducted. Results There were a total of 14,180,603 likes, 863,411 replies, 3,087,812 retweets, and 641,381 mentions in tweets during the study timeframe. Out of 902,138 tweets analyzed, sentiment analysis classified 434,254 (48.2%) tweets as having a positive sentiment, 187,042 (20.7%) as neutral, and 280,842 (31.1%) as negative. The study identified 5 dominant themes among COVID-19–related tweets: health care environment, emotional support, business economy, social change, and psychological stress. Alaska, Wyoming, New Mexico, Pennsylvania, and Florida were the states expressing the most negative sentiment while Vermont, North Dakota, Utah, Colorado, Tennessee, and North Carolina conveyed the most positive sentiment. Conclusions This study identified 5 prevalent themes of COVID-19 discussion with sentiments ranging from positive to negative. These themes and sentiments can clarify the public’s response to COVID-19 and help officials navigate the pandemic.
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Affiliation(s)
- Man Hung
- College of Dental Medicine, Roseman University of Health Sciences, South Jordan, UT, United States.,Department of Orthopaedics, University of Utah, Salt Lake City, UT, United States.,George E Wahlen Department of Veterans Affairs Medical Center, Salt Lake City, UT, United States.,Department of Occupational Therapy & Occupational Science, Towson University, Towson, MD, United States.,David Eccles School of Business, University of Utah, Salt Lake City, UT, United States.,Department of Educational Psychology, University of Utah, Salt Lake City, UT, United States.,Division of Public Health, University of Utah, Salt Lake City, UT, United States
| | - Evelyn Lauren
- Department of Biostatistics, Boston University, Boston, MA, United States
| | - Eric S Hon
- Department of Economics, University of Chicago, Chicago, IL, United States
| | - Wendy C Birmingham
- Department of Psychology, Brigham Young University, Provo, UT, United States
| | - Julie Xu
- College of Nursing, University of Utah, Salt Lake City, UT, United States
| | - Sharon Su
- College of Dental Medicine, Roseman University of Health Sciences, South Jordan, UT, United States
| | - Shirley D Hon
- Department of Electrical & Computer Engineering, University of Utah, Salt Lake City, UT, United States.,School of Computing, University of Utah, Salt Lake City, UT, United States.,International Business Machines Corporation, Poughkeepsie, NY, United States
| | - Jungweon Park
- College of Dental Medicine, Roseman University of Health Sciences, South Jordan, UT, United States
| | - Peter Dang
- College of Dental Medicine, Roseman University of Health Sciences, South Jordan, UT, United States
| | - Martin S Lipsky
- College of Dental Medicine, Roseman University of Health Sciences, South Jordan, UT, United States
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31
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Aiken EL, McGough SF, Majumder MS, Wachtel G, Nguyen AT, Viboud C, Santillana M. Real-time estimation of disease activity in emerging outbreaks using internet search information. PLoS Comput Biol 2020; 16:e1008117. [PMID: 32804932 PMCID: PMC7451983 DOI: 10.1371/journal.pcbi.1008117] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Revised: 08/27/2020] [Accepted: 07/01/2020] [Indexed: 11/18/2022] Open
Abstract
Understanding the behavior of emerging disease outbreaks in, or ahead of, real-time could help healthcare officials better design interventions to mitigate impacts on affected populations. Most healthcare-based disease surveillance systems, however, have significant inherent reporting delays due to data collection, aggregation, and distribution processes. Recent work has shown that machine learning methods leveraging a combination of traditionally collected epidemiological information and novel Internet-based data sources, such as disease-related Internet search activity, can produce meaningful "nowcasts" of disease incidence ahead of healthcare-based estimates, with most successful case studies focusing on endemic and seasonal diseases such as influenza and dengue. Here, we apply similar computational methods to emerging outbreaks in geographic regions where no historical presence of the disease of interest has been observed. By combining limited available historical epidemiological data available with disease-related Internet search activity, we retrospectively estimate disease activity in five recent outbreaks weeks ahead of traditional surveillance methods. We find that the proposed computational methods frequently provide useful real-time incidence estimates that can help fill temporal data gaps resulting from surveillance reporting delays. However, the proposed methods are limited by issues of sample bias and skew in search query volumes, perhaps as a result of media coverage.
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Affiliation(s)
- Emily L. Aiken
- School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, United States of America
| | - Sarah F. McGough
- Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Maimuna S. Majumder
- Department of Healthcare Policy, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Gal Wachtel
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts, United States of America
| | - Andre T. Nguyen
- Booz Allen Hamilton, Columbia, Maryland, United States of America
- University of Maryland, Baltimore County, Baltimore, Maryland, United States of America
| | - Cecile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Mauricio Santillana
- School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, United States of America
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts, United States of America
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, United States of America
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32
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Roche R, Manzi J, Bard K. A Double Bind for the Ties that Bind: A Pilot Study of Mental Health Challenges among Female US Army Officers and Impact on Family Life. JOURNAL OF VETERANS STUDIES 2020. [DOI: 10.21061/jvs.v6i1.164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
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33
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Correia RB, Wood IB, Bollen J, Rocha LM. Mining Social Media Data for Biomedical Signals and Health-Related Behavior. Annu Rev Biomed Data Sci 2020; 3:433-458. [PMID: 32550337 PMCID: PMC7299233 DOI: 10.1146/annurev-biodatasci-030320-040844] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Social media data have been increasingly used to study biomedical and health-related phenomena. From cohort-level discussions of a condition to population-level analyses of sentiment, social media have provided scientists with unprecedented amounts of data to study human behavior associated with a variety of health conditions and medical treatments. Here we review recent work in mining social media for biomedical, epidemiological, and social phenomena information relevant to the multilevel complexity of human health. We pay particular attention to topics where social media data analysis has shown the most progress, including pharmacovigilance and sentiment analysis, especially for mental health. We also discuss a variety of innovative uses of social media data for health-related applications as well as important limitations of social media data access and use.
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Affiliation(s)
- Rion Brattig Correia
- Instituto Gulbenkian de Cincia, 2780-156 Oeiras, Portugal
- Center for Social and Biomedical Complexity, Luddy School of Informatics, Computing & Engineering, Indiana University, Bloomington, Indiana 47408, USA
- CAPES Foundation, Ministry of Education of Brazil, 70040 Braslia DF, Brazil
| | - Ian B Wood
- Center for Social and Biomedical Complexity, Luddy School of Informatics, Computing & Engineering, Indiana University, Bloomington, Indiana 47408, USA
| | - Johan Bollen
- Center for Social and Biomedical Complexity, Luddy School of Informatics, Computing & Engineering, Indiana University, Bloomington, Indiana 47408, USA
| | - Luis M Rocha
- Instituto Gulbenkian de Cincia, 2780-156 Oeiras, Portugal
- Center for Social and Biomedical Complexity, Luddy School of Informatics, Computing & Engineering, Indiana University, Bloomington, Indiana 47408, USA
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34
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Measuring objective and subjective well-being: dimensions and data sources. INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS 2020. [DOI: 10.1007/s41060-020-00224-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
AbstractWell-being is an important value for people’s lives, and it could be considered as an index of societal progress. Researchers have suggested two main approaches for the overall measurement of well-being, the objective and the subjective well-being. Both approaches, as well as their relevant dimensions, have been traditionally captured with surveys. During the last decades, new data sources have been suggested as an alternative or complement to traditional data. This paper aims to present the theoretical background of well-being, by distinguishing between objective and subjective approaches, their relevant dimensions, the new data sources used for their measurement and relevant studies. We also intend to shed light on still barely unexplored dimensions and data sources that could potentially contribute as a key for public policing and social development.
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35
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Ma J, Tse YK, Sato Y, Zhang M, Lu Z. Exploring the social broadcasting crisis communication: insights from the mars recall scandal. ENTERP INF SYST-UK 2020. [DOI: 10.1080/17517575.2020.1765023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
- Jie Ma
- Newcastle Business School, Northumbria University Newcastle upon Tyne, Newcastle upon Tyne, UK
| | - Ying Kei Tse
- Cardiff Business School, Cardiff University Cathays, Cardiff, UK
| | - Yuji Sato
- Graduate School of Management, Chukyo University, Nagoya, Aichi, Japan
| | - Minhao Zhang
- School of Economics, Finance and Management, University of Bristol Howard House, Bristol, UK
| | - Zhou Lu
- School of Economics, Tianjin University of Commerce Tianjin, Tianjin, China
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36
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Abstract
With the spiraling pandemic of the Coronavirus Disease 2019 (COVID-19), it has becoming inherently important to disseminate accurate and timely information about the disease. Due to the ubiquity of Internet connectivity and smart devices, social sensing is emerging as a dynamic AI-driven sensing paradigm to extract real-time observations from online users. In this paper, we propose CovidSens, a vision of social sensing-based risk alert systems to spontaneously obtain and analyze social data to infer the state of the COVID-19 propagation. CovidSens can actively help to keep the general public informed about the COVID-19 spread and identify risk-prone areas by inferring future propagation patterns. The CovidSens concept is motivated by three observations: (1) people have been actively sharing their state of health and experience of the COVID-19 via online social media, (2) official warning channels and news agencies are relatively slower than people reporting their observations and experiences about COVID-19 on social media, and (3) online users are frequently equipped with substantially capable mobile devices that are able to perform non-trivial on-device computation for data processing and analytics. We envision an unprecedented opportunity to leverage the posts generated by the ordinary people to build a real-time sensing and analytic system for gathering and circulating vital information of the COVID-19 propagation. Specifically, the vision of CovidSens attempts to answer the questions: How to distill reliable information about the COVID-19 with the coexistence of prevailing rumors and misinformation in the social media? How to inform the general public about the latest state of the spread timely and effectively, and alert them to remain prepared? How to leverage the computational power on the edge devices (e.g., smartphones, IoT devices, UAVs) to construct fully integrated edge-based social sensing platforms for rapid detection of the COVID-19 spread? In this vision paper, we discuss the roles of CovidSens and identify the potential challenges in developing reliable social sensing-based risk alert systems. We envision that approaches originating from multiple disciplines (e.g., AI, estimation theory, machine learning, constrained optimization) can be effective in addressing the challenges. Finally, we outline a few research directions for future work in CovidSens.
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37
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DRFS: Detecting Risk Factor of Stroke Disease from Social Media Using Machine Learning Techniques. Neural Process Lett 2020. [DOI: 10.1007/s11063-020-10279-8] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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38
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Collins OC, Duffy KJ. Mathematical Analyses on the Effects of Control Measures for a Waterborne Disease Model with Socioeconomic Conditions. J Comput Biol 2020; 28:19-32. [PMID: 32471315 DOI: 10.1089/cmb.2019.0352] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Waterborne diseases are present major health problems to humanity especially in rural communities where many individuals belong to the lower socioeconomic classes (SECs). The impacts of introducing waterborne disease control measures for such communities are investigated by considering a waterborne disease model. The model is extended by introducing treatment of infected individuals and water purification as control measures. The possible benefits of considering these control measures for the various SECs are investigated. Further analyses show how different degrees of control impact the rate at which waterborne diseases are spread across SECs. The disease control model is validated by using it to study the cholera outbreak in Haiti.
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Affiliation(s)
| | - Kevin Jan Duffy
- Institute of Systems Science, Durban University of Technology, Durban, South Africa
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39
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Shahid F, Ony SH, Albi TR, Chellappan S, Vashistha A, Islam ABMAA. Learning from Tweets: Opportunities and Challenges to Inform Policy Making During Dengue Epidemic. ACTA ACUST UNITED AC 2020. [DOI: 10.1145/3392875] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
Social media platforms are widely used by people to report, access, and share information during outbreaks and epidemics. Although government agencies and healthcare institutions in developed regions are increasingly relying on social media to develop epidemic forecasts and outbreak response, there is a limited understanding of how people in developing regions interact on social media during outbreaks and what useful insights this dataset could offer during public health crises. In this work, we examined 28,688 tweets to identify public health issues during dengue epidemic in Bangladesh and found several insights, such as irregularities in dengue diagnosis and treatment, shortage of blood supply for Rh negative blood groups, and high local transmission of dengue during Eid-ul-Adha, that impact disease preparedness and outbreak response. We discuss the opportunities and challenges in analyzing tweets and outline how government agencies and healthcare institutions can use social media health data to inform policy making during public health crises.
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40
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Daughton AR, Chunara R, Paul MJ. Comparison of Social Media, Syndromic Surveillance, and Microbiologic Acute Respiratory Infection Data: Observational Study. JMIR Public Health Surveill 2020; 6:e14986. [PMID: 32329741 PMCID: PMC7210500 DOI: 10.2196/14986] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Revised: 09/27/2019] [Accepted: 02/09/2020] [Indexed: 11/30/2022] Open
Abstract
Background Internet data can be used to improve infectious disease models. However, the representativeness and individual-level validity of internet-derived measures are largely unexplored as this requires ground truth data for study. Objective This study sought to identify relationships between Web-based behaviors and/or conversation topics and health status using a ground truth, survey-based dataset. Methods This study leveraged a unique dataset of self-reported surveys, microbiological laboratory tests, and social media data from the same individuals toward understanding the validity of individual-level constructs pertaining to influenza-like illness in social media data. Logistic regression models were used to identify illness in Twitter posts using user posting behaviors and topic model features extracted from users’ tweets. Results Of 396 original study participants, only 81 met the inclusion criteria for this study. Of these participants’ tweets, we identified only two instances that were related to health and occurred within 2 weeks (before or after) of a survey indicating symptoms. It was not possible to predict when participants reported symptoms using features derived from topic models (area under the curve [AUC]=0.51; P=.38), though it was possible using behavior features, albeit with a very small effect size (AUC=0.53; P≤.001). Individual symptoms were also generally not predictable either. The study sample and a random sample from Twitter are predictably different on held-out data (AUC=0.67; P≤.001), meaning that the content posted by people who participated in this study was predictably different from that posted by random Twitter users. Individuals in the random sample and the GoViral sample used Twitter with similar frequencies (similar @ mentions, number of tweets, and number of retweets; AUC=0.50; P=.19). Conclusions To our knowledge, this is the first instance of an attempt to use a ground truth dataset to validate infectious disease observations in social media data. The lack of signal, the lack of predictability among behaviors or topics, and the demonstrated volunteer bias in the study population are important findings for the large and growing body of disease surveillance using internet-sourced data.
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Affiliation(s)
- Ashlynn R Daughton
- Analytics, Intelligence and Technology, Los Alamos National Laboratory, Los Alamos, NM, United States
| | - Rumi Chunara
- Biostatistics, School of Global Public Health, New York University, New York, NY, United States.,Computer Science and Engineering, Tandon School of Engineering, New York University, Brooklyn, NY, United States
| | - Michael J Paul
- Information Science Department, University of Colorado Boulder, Boulder, CO, United States
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41
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Hay-David AGC, Herron JBT, Gilling P, Miller A, Brennan PA. Reducing medical error during a pandemic. Br J Oral Maxillofac Surg 2020; 58:581-584. [PMID: 32312585 PMCID: PMC7151369 DOI: 10.1016/j.bjoms.2020.04.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Accepted: 04/02/2020] [Indexed: 12/20/2022]
Abstract
On 30 January 2020, the WHO declared the coronavirus disease 2019 (COVID-19) a public health emergency of international concern. By 11 March 2020, it was designated a pandemic owing to its rapid worldwide spread. In this short article we provide some information that might be useful and help equip colleagues to reduce medical error during a pandemic. We advocate a systems-based approach, rather than an individual’s sole responsibility, and, look at ways to provide safer healthcare.
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Affiliation(s)
| | - J B T Herron
- Faculty of Health Sciences and Wellbeing Sunderland University, Chester Road, Sunderland, SR1 3SD, UK
| | - P Gilling
- c/o Queen Alexandra Hospital, Portsmouth, PO6 3LY, UK
| | - A Miller
- St John of God Hospital Subiaco and President of the Western Australian branch of the Australian Medical Association, Australia
| | - P A Brennan
- Maxillofacial Unit, Queen Alexandra Hospital, Portsmouth, PO6 3LY, UK
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42
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Barros JM, Duggan J, Rebholz-Schuhmann D. The Application of Internet-Based Sources for Public Health Surveillance (Infoveillance): Systematic Review. J Med Internet Res 2020; 22:e13680. [PMID: 32167477 PMCID: PMC7101503 DOI: 10.2196/13680] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2019] [Revised: 09/18/2019] [Accepted: 11/26/2019] [Indexed: 12/30/2022] Open
Abstract
Background Public health surveillance is based on the continuous and systematic collection, analysis, and interpretation of data. This informs the development of early warning systems to monitor epidemics and documents the impact of intervention measures. The introduction of digital data sources, and specifically sources available on the internet, has impacted the field of public health surveillance. New opportunities enabled by the underlying availability and scale of internet-based sources (IBSs) have paved the way for novel approaches for disease surveillance, exploration of health communities, and the study of epidemic dynamics. This field and approach is also known as infodemiology or infoveillance. Objective This review aimed to assess research findings regarding the application of IBSs for public health surveillance (infodemiology or infoveillance). To achieve this, we have presented a comprehensive systematic literature review with a focus on these sources and their limitations, the diseases targeted, and commonly applied methods. Methods A systematic literature review was conducted targeting publications between 2012 and 2018 that leveraged IBSs for public health surveillance, outbreak forecasting, disease characterization, diagnosis prediction, content analysis, and health-topic identification. The search results were filtered according to previously defined inclusion and exclusion criteria. Results Spanning a total of 162 publications, we determined infectious diseases to be the preferred case study (108/162, 66.7%). Of the eight categories of IBSs (search queries, social media, news, discussion forums, websites, web encyclopedia, and online obituaries), search queries and social media were applied in 95.1% (154/162) of the reviewed publications. We also identified limitations in representativeness and biased user age groups, as well as high susceptibility to media events by search queries, social media, and web encyclopedias. Conclusions IBSs are a valuable proxy to study illnesses affecting the general population; however, it is important to characterize which diseases are best suited for the available sources; the literature shows that the level of engagement among online platforms can be a potential indicator. There is a necessity to understand the population’s online behavior; in addition, the exploration of health information dissemination and its content is significantly unexplored. With this information, we can understand how the population communicates about illnesses online and, in the process, benefit public health.
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Affiliation(s)
- Joana M Barros
- Insight Centre for Data Analytics, National University of Ireland Galway, Galway, Ireland.,School of Computer Science, National University of Ireland Galway, Galway, Ireland
| | - Jim Duggan
- School of Computer Science, National University of Ireland Galway, Galway, Ireland
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43
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Khatibi A, Belém F, Couto da Silva AP, Almeida JM, Gonçalves MA. Fine-grained tourism prediction: Impact of social and environmental features. Inf Process Manag 2020. [DOI: 10.1016/j.ipm.2019.102057] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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44
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Nebeker C. mHealth Research Applied to Regulated and Unregulated Behavioral Health Sciences. THE JOURNAL OF LAW, MEDICINE & ETHICS : A JOURNAL OF THE AMERICAN SOCIETY OF LAW, MEDICINE & ETHICS 2020; 48:49-59. [PMID: 32342758 DOI: 10.1177/1073110520917029] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Behavioral scientists are developing new methods and frameworks that leverage mobile health technologies to optimize individual level behavior change. Pervasive sensors and mobile apps allow researchers to passively observe human behaviors "in the wild" 24/7 which supports delivery of personalized interventions in the real-world environment. This is all possible because these technologies contain an incredible array of sensors that allow applications to constantly record user location and can contextualize current environmental conditions through barometers, thermometers, and ambient light sensors and can also capture audio and video of the user and their surroundings through multiple integrated high-definition cameras and microphones. These tools are a game changer in behavioral health research and, not surprisingly, introduce new ethical, regulatory/legal and social implications described in this article.
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Affiliation(s)
- Camille Nebeker
- Camille Nebeker, Ed.D, M.S., is an associate professor in the University of California, San Diego Department of Family Medicine and Public Health, with a primary appointment in Behavioral Medicine and a secondary appointment in Global Health
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45
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Nebeker C, Dunseath SE, Linares-Orozco R. A retrospective analysis of NIH-funded digital health research using social media platforms. Digit Health 2020; 6:2055207619901085. [PMID: 32030195 PMCID: PMC6977220 DOI: 10.1177/2055207619901085] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Accepted: 12/26/2019] [Indexed: 11/17/2022] Open
Abstract
Objective Social network platforms are increasingly used in digital health research. Our study aimed to 1. qualify and quantify the use of social media platforms in health research supported by the National Institutes of Health (NIH) and document changes occurring between 2011 and 2017 and 2. examine whether institutions hosting these studies provided public-facing guidelines on how to conduct ethical social media health research. Methods The NIH RePORTER (Research Portfolio Online Reporting Tools) database was searched to identify research utilizing Instagram, Pinterest, Facebook, or Twitter. Studies included used social media for observational research, recruitment, intervention delivery or to assess social media as an effective research tool. Abstracts were qualitatively analyzed to describe the population and health topic by year. Websites of organizations receiving funding for this research were searched to identify whether guidance or policy existed. Results Studies (n = 105) were organized by population targeted and health focus. Main "Health" themes were labeled: 1. substance use, 2. disease/diagnosis, 3. psychiatry/mental health, and 4. weight and physical activity. The populations most involved included adolescents and young adults, and men who have sex with men. The number of research studies using social media increased approximately 590% between 2011 and 2017. Studies were linked to 56 organizations of which 21% (n = 12) provided some accessible guidance with 79% (n = 44) offering no guidance specific to social media health research. Conclusions Social media research is conducted with vulnerable populations that are traditionally difficult to reach. There is a compelling need for resources designed to support ethical and responsible social media-enabled research to enable this research to be carried out safely.
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Affiliation(s)
- Camille Nebeker
- Department of Family Medicine and Public Health, School of Medicine, University of California, San Diego, USA
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46
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Samaras L, García-Barriocanal E, Sicilia MA. Syndromic surveillance using web data: a systematic review. INNOVATION IN HEALTH INFORMATICS 2020. [PMCID: PMC7153324 DOI: 10.1016/b978-0-12-819043-2.00002-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
During the recent years, a lot of debate is taken place about the evolution of Smart Healthcare systems. Particularly, how these systems can help people improve human conditions of health, by taking advantages of the new Information and Communication Technologies (ICT), regarding early prediction and efficient treatment. The purpose of this study is to provide a systematic review of the current literature available that focuses on information systems on syndromic surveillance using web data. All published items concern articles, books, reviews, reports, conference announcements, and dissertations. We used a variation of PRISMA Statements methodology to conduct a systematic review. The review identifies the relevant published papers from the year 2004 to 2018, systematically includes and explores them to extract similarities, gaps, and conclusions on the research that has been done so far. The results presented concern the year, the examined disease, the web data source, the geographic location/country, and the data analysis method used. The results show that influenza is the most examined infectious disease. The internet tools most used are Twitter and Google. Regarding the geographical areas explored in the published papers, the most examined country is the United States, since many scientists come from this country. There is a significant growth of articles since 2009. There are also various statistical methods used to correlate the data retrieved from the internet to the data from national authorities. The conclusion of all researches is that the Web can be a useful tool for the detection of serious epidemics and for a creation of a syndromic surveillance system using the Web, since we can predict epidemics from web data before they are officially detected in population. With the advance of ICT, Smart Healthcare can benefit from the monitoring of epidemics and the early prediction of such a system, improving national or international health strategies and policy decision. This can be achieved through the provision of new technology tools to enhance health monitoring systems toward the new innovations of Smart Health or eHealth, even with the emerging technologies of Internet of Things. The challenges and impacts of an electronic system based on internet data include the social, medical, and technological disciplines. These can be further extended to Smart Healthcare, as the data streaming can provide with real-time information, awareness on epidemics and alerts for both patients or medical scientists. Finally, these new systems can help improve the standards of human life.
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47
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Melvin S, Jamal A, Hill K, Wang W, Young SD. Identifying Sleep-Deprived Authors of Tweets: Prospective Study. JMIR Ment Health 2019; 6:e13076. [PMID: 31808747 PMCID: PMC6925390 DOI: 10.2196/13076] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Revised: 03/04/2019] [Accepted: 03/22/2019] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Social media data can be explored as a tool to detect sleep deprivation. First-year undergraduate students in their first quarter were invited to wear sleep-tracking devices (Basis; Intel), allow us to follow them on Twitter, and complete weekly surveys regarding their sleep. OBJECTIVE This study aimed to determine whether social media data can be used to monitor sleep deprivation. METHODS The sleep data obtained from the device were utilized to create a tiredness model that aided in labeling the tweets as sleep deprived or not at the time of posting. Labeled data were used to train and test a gated recurrent unit (GRU) neural network as to whether or not study participants were sleep deprived at the time of posting. RESULTS Results from the GRU neural network suggest that it is possible to classify the sleep-deprivation status of a tweet's author with an average area under the curve of 0.68. CONCLUSIONS It is feasible to use social media to identify students' sleep deprivation. The results add to the body of research suggesting that social media data should be further explored as a potential source for monitoring health.
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Affiliation(s)
- Sara Melvin
- University of California Institute for Prediction Technology, Los Angeles, CA, United States
| | - Amanda Jamal
- Department of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Kaitlyn Hill
- New York University-Winthrop Hospital, Mineola, NY, United States
| | - Wei Wang
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, United States
| | - Sean D Young
- Department of Medicine, University of California, Irvine, Orange, CA, United States.,University of California Institute for Prediction Technology, Irvine, CA, United States
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48
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Jang K, Baek YM. When Information from Public Health Officials is Untrustworthy: The Use of Online News, Interpersonal Networks, and Social Media during the MERS Outbreak in South Korea. HEALTH COMMUNICATION 2019; 34:991-998. [PMID: 29558170 DOI: 10.1080/10410236.2018.1449552] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
Public health officials (PHOs) are responsible for providing trustworthy information during a public health crisis; however, there is little research on how the public behaves when their expectations for such information are violated. Drawing on media dependency theory and source credibility research as our primary theoretical framework, we tested how credibility of information from PHOs is associated with people's reliance on a particular communication channel in the context of the 2015 Middle East Respiratory Syndrome (MERS) outbreak in South Korea. Using nationally representative data (N = 1036) collected during the MERS outbreak, we found that less credible information from PHOs led to more frequent use of online news, interpersonal networks, and social media for acquiring MERS-related information. However, credibility of information from PHOs was not associated with the use of television news or print newspapers. The theoretical and practical implications of our results on communication channels usage are discussed.
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Affiliation(s)
- Kyungeun Jang
- a Graduate School of Communication and Arts , Yonsei University
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49
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Ford E, Boyd A, Bowles JK, Havard A, Aldridge RW, Curcin V, Greiver M, Harron K, Katikireddi V, Rodgers SE, Sperrin M. Our data, our society, our health: A vision for inclusive and transparent health data science in the United Kingdom and beyond. Learn Health Syst 2019; 3:e10191. [PMID: 31317072 PMCID: PMC6628981 DOI: 10.1002/lrh2.10191] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Revised: 02/08/2019] [Accepted: 03/06/2019] [Indexed: 01/28/2023] Open
Abstract
The last 6 years have seen sustained investment in health data science in the United Kingdom and beyond, which should result in a data science community that is inclusive of all stakeholders, working together to use data to benefit society through the improvement of public health and well-being. However, opportunities made possible through the innovative use of data are still not being fully realised, resulting in research inefficiencies and avoidable health harms. In this paper, we identify the most important barriers to achieving higher productivity in health data science. We then draw on previous research, domain expertise, and theory to outline how to go about overcoming these barriers, applying our core values of inclusivity and transparency. We believe a step change can be achieved through meaningful stakeholder involvement at every stage of research planning, design, and execution and team-based data science, as well as harnessing novel and secure data technologies. Applying these values to health data science will safeguard a social licence for health data research and ensure transparent and secure data usage for public benefit.
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Affiliation(s)
- Elizabeth Ford
- Department of Primary Care and Public HealthBrighton and Sussex Medical SchoolBrightonUK
| | - Andy Boyd
- ALSPAC, Population Health Sciences, Bristol Medical SchoolUniversity of BristolBristolUK
| | | | - Alys Havard
- Centre for Big Data Research in HealthUniversity of New South WalesSydneyAustralia
| | | | - Vasa Curcin
- School of Population and Environmental Health Sciences, Faculty of Life Sciences and MedicineKing's College LondonUK
| | - Michelle Greiver
- Department of Family and Community MedicineUniversity of Toronto, North York General HospitalTorontoCanada
| | - Katie Harron
- Great Ormond Street Institute of Child HealthUCLLondonUK
| | - Vittal Katikireddi
- MRC/CSO Social and Public Health Sciences UnitUniversity of GlasgowGlasgowUK
| | - Sarah E. Rodgers
- Health Data Research UKSwansea UniversitySwanseaUK
- Public Health and PolicyUniversity of LiverpoolLiverpoolUK
| | - Matthew Sperrin
- School of Health Sciences, Faculty of Biology, Medicine and HealthUniversity of ManchesterManchesterUK
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50
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Merchant RM, Asch DA, Crutchley P, Ungar LH, Guntuku SC, Eichstaedt JC, Hill S, Padrez K, Smith RJ, Schwartz HA. Evaluating the predictability of medical conditions from social media posts. PLoS One 2019; 14:e0215476. [PMID: 31206534 PMCID: PMC6576767 DOI: 10.1371/journal.pone.0215476] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2018] [Accepted: 04/02/2019] [Indexed: 12/11/2022] Open
Abstract
We studied whether medical conditions across 21 broad categories were predictable from social media content across approximately 20 million words written by 999 consenting patients. Facebook language significantly improved upon the prediction accuracy of demographic variables for 18 of the 21 disease categories; it was particularly effective at predicting diabetes and mental health conditions including anxiety, depression and psychoses. Social media data are a quantifiable link into the otherwise elusive daily lives of patients, providing an avenue for study and assessment of behavioral and environmental disease risk factors. Analogous to the genome, social media data linked to medical diagnoses can be banked with patients’ consent, and an encoding of social media language can be used as markers of disease risk, serve as a screening tool, and elucidate disease epidemiology. In what we believe to be the first report linking electronic medical record data with social media data from consenting patients, we identified that patients’ Facebook status updates can predict many health conditions, suggesting opportunities to use social media data to determine disease onset or exacerbation and to conduct social media-based health interventions.
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Affiliation(s)
- Raina M Merchant
- Penn Medicine Center for Digital Health, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.,Department of Emergency Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.,Penn Medicine Center for Health Care Innovation, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - David A Asch
- Penn Medicine Center for Digital Health, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.,Penn Medicine Center for Health Care Innovation, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.,The Center for Health Equity Research and Promotion-Philadelphia Veterans Affairs Medical Center, Philadelphia, Pennsylvania, United States of America.,The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Patrick Crutchley
- Penn Medicine Center for Digital Health, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.,Positive Psychology Center, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Lyle H Ungar
- Penn Medicine Center for Digital Health, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.,The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.,Positive Psychology Center, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.,Department of Computer and Information Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Sharath C Guntuku
- Penn Medicine Center for Digital Health, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.,Department of Emergency Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.,Penn Medicine Center for Health Care Innovation, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.,Department of Computer and Information Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Johannes C Eichstaedt
- Positive Psychology Center, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Shawndra Hill
- Penn Medicine Center for Digital Health, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.,Microsoft Research, New York, New York, United States of America
| | - Kevin Padrez
- Penn Medicine Center for Digital Health, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Robert J Smith
- Penn Medicine Center for Digital Health, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - H Andrew Schwartz
- Penn Medicine Center for Digital Health, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.,Positive Psychology Center, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.,Department of Computer Science, Stony Brook University, Stony Brook, New York, United States of America
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