Overview

This book presents state-of-the-art research methods, results, and applications in social media and health research. It aims to help readers better understand the different aspects of using social web platforms in health research. Throughout the chapters, the benefits, limitations, and best practices of using a variety of social web platforms in health research are discussed with concrete use cases. This is an ideal book for biomedical researchers, clinicians, and health consumers (including patients) who are interested in learning how social web platforms impact health and healthcare research.

Full Product Details

Author:   Jiang Bian ,  Yi Guo ,  Zhe He ,  Xia Hu
Publisher:   Springer Nature Switzerland AG
Imprint:   Springer Nature Switzerland AG
Edition:   1st ed. 2019
Weight:   0.600kg
ISBN:  

9783030147136

ISBN 10:   3030147134
Pages:   272
Publication Date:   11 July 2019
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

Table of Contents

1. A literature Review of Social Media-based Data Mining for Health Outcomes Research.- 2. Social Media-Based Health Interventions: Where Are We Now?.- 3. Quantifying and Visualizing the Research Status of Social Media & Health Research Field.- 4. Social Media in Health Communication.- 5. Consumers' Selection of Sources in Searching for Health Information.- 6. Understanding and Bridging the Language and Terminology Gap between Health Professionals and Consumers using Social Media.- 7. Dissemination of Information on Stigmatized Health Issues on Social Media.- 8. Learning Wellness Profiles of Users on Social Networks: The Case of Diabetes.- 9. Social Media and Psychological Disorder.- 10. Content Analysis of the 2015 #SmearForSmear Campaign Using Deep Learning.- 11. How to Improve Public Health via Mining Social Media Platforms: A Case Study of Human Papillomaviruses (HPV).- 12. Learning Hormonal Therapy Medication Adherence from an Online Breast Cancer Forum.- 13. Ethics in Health Research using Social Media.

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Author Information

Jiang Bian  Dr. Bian is an Assistant Professor of Biomedical Informatics in the Department of Health Outcomes and Biomedical Informatics at the University of Florida.  He is also the Director of Cancer Informatics and eHealth Core for the University of Florida Health Cancer Center.  He has a diverse yet strong multi-disciplinary background and extensive expertise in social media analysis, machine learning, natural language processing, network science, ontology development and evaluation, semantic web technology and software engineering.  Yi Guo  Dr. Yi Guo is an Assistant Professor in the Department of Health Outcomes and Biomedical Informatics in the College of Medicine at University of Florida.  He is a health outcomes researcher and data scientist with expertise in data integration and discovery, multilevel and longitudinal models, health risk prediction models, quality measurement and psychometric analysis, and power and sample size analysis. Zhe He  Dr. Zhe He is an Assistant Professor in the School of Information at the Florida State University. He is an Associate Editor of BMC Medical Informatics and Decision Making. His research lies in biomedical and health informatics, clinical research informatics, knowledge discovery, knowledge representation, and ontology-enhanced data analytics. His research aims to improve the population health and advance biomedical research through the collection, analysis, and application of electronic health data from heterogeneous sources. Xia Hu  Dr. Xia ""Ben"" Hu is currently a tenure-track Assistant Professor at Texas A&M University in the Department of Computer Science and Engineering. Dr. Hu has published nearly 100 papers in several major academic venues, including WWW, SIGIR, KDD, ICDM, SDM, WSDM, IJCAI, AAAI, CIKM, ICWSM, etc. His work on deep collaborative filtering, anomaly detection and knowledge graph have been included in the TensorFlow package, Apple production system and Bing production system, respectively.   

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