{"title":"基于疾病的社会网络分析和网络建模的社会媒体数据的使用:系统综述。","authors":"Thilagavathi Ramamoorthy, Dhivya Karmegam, Bagavandas Mappillairaju","doi":"10.1080/17538157.2021.1905642","DOIUrl":null,"url":null,"abstract":"<p><p>Burden due to infectious and noncommunicable disease is increasing at an alarming rate. Social media usage is growing rapidly and has become the new norm of communication. It is imperative to examine what is being discussed in the social media about diseases or conditions and the characteristics of the network of people involved in discussion. The objective is to assess the tools and techniques used to study social media disease networks using network analysis and network modeling. PubMed and IEEEXplore were searched from 2009 to 2020 and included 30 studies after screening and analysis. Twitter, QuitNet, and disease-specific online forums were widely used to study communications on various health conditions. Most of the studies have performed content analysis and network analysis, whereas network modeling has been done in six studies. Posts on cancer, COVID-19, and smoking have been widely studied. Tools and techniques used for network analysis are listed. Health-related social media data can be leveraged for network analysis. Network modeling technique would help to identify the structural factors associated with the affiliation of the disease networks, which is scarcely utilized. This will help public health professionals to tailor targeted interventions.</p>","PeriodicalId":54984,"journal":{"name":"Informatics for Health & Social Care","volume":"46 4","pages":"443-454"},"PeriodicalIF":2.5000,"publicationDate":"2021-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/17538157.2021.1905642","citationCount":"4","resultStr":"{\"title\":\"Use of social media data for disease based social network analysis and network modeling: A Systematic Review.\",\"authors\":\"Thilagavathi Ramamoorthy, Dhivya Karmegam, Bagavandas Mappillairaju\",\"doi\":\"10.1080/17538157.2021.1905642\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Burden due to infectious and noncommunicable disease is increasing at an alarming rate. Social media usage is growing rapidly and has become the new norm of communication. It is imperative to examine what is being discussed in the social media about diseases or conditions and the characteristics of the network of people involved in discussion. The objective is to assess the tools and techniques used to study social media disease networks using network analysis and network modeling. PubMed and IEEEXplore were searched from 2009 to 2020 and included 30 studies after screening and analysis. Twitter, QuitNet, and disease-specific online forums were widely used to study communications on various health conditions. Most of the studies have performed content analysis and network analysis, whereas network modeling has been done in six studies. Posts on cancer, COVID-19, and smoking have been widely studied. Tools and techniques used for network analysis are listed. Health-related social media data can be leveraged for network analysis. Network modeling technique would help to identify the structural factors associated with the affiliation of the disease networks, which is scarcely utilized. This will help public health professionals to tailor targeted interventions.</p>\",\"PeriodicalId\":54984,\"journal\":{\"name\":\"Informatics for Health & Social Care\",\"volume\":\"46 4\",\"pages\":\"443-454\"},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2021-12-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1080/17538157.2021.1905642\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Informatics for Health & Social Care\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1080/17538157.2021.1905642\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2021/4/20 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"HEALTH CARE SCIENCES & SERVICES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Informatics for Health & Social Care","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1080/17538157.2021.1905642","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2021/4/20 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
Use of social media data for disease based social network analysis and network modeling: A Systematic Review.
Burden due to infectious and noncommunicable disease is increasing at an alarming rate. Social media usage is growing rapidly and has become the new norm of communication. It is imperative to examine what is being discussed in the social media about diseases or conditions and the characteristics of the network of people involved in discussion. The objective is to assess the tools and techniques used to study social media disease networks using network analysis and network modeling. PubMed and IEEEXplore were searched from 2009 to 2020 and included 30 studies after screening and analysis. Twitter, QuitNet, and disease-specific online forums were widely used to study communications on various health conditions. Most of the studies have performed content analysis and network analysis, whereas network modeling has been done in six studies. Posts on cancer, COVID-19, and smoking have been widely studied. Tools and techniques used for network analysis are listed. Health-related social media data can be leveraged for network analysis. Network modeling technique would help to identify the structural factors associated with the affiliation of the disease networks, which is scarcely utilized. This will help public health professionals to tailor targeted interventions.
期刊介绍:
Informatics for Health & Social Care promotes evidence-based informatics as applied to the domain of health and social care. It showcases informatics research and practice within the many and diverse contexts of care; it takes personal information, both its direct and indirect use, as its central focus.
The scope of the Journal is broad, encompassing both the properties of care information and the life-cycle of associated information systems.
Consideration of the properties of care information will necessarily include the data itself, its representation, structure, and associated processes, as well as the context of its use, highlighting the related communication, computational, cognitive, social and ethical aspects.
Consideration of the life-cycle of care information systems includes full range from requirements, specifications, theoretical models and conceptual design through to sustainable implementations, and the valuation of impacts. Empirical evidence experiences related to implementation are particularly welcome.
Informatics in Health & Social Care seeks to consolidate and add to the core knowledge within the disciplines of Health and Social Care Informatics. The Journal therefore welcomes scientific papers, case studies and literature reviews. Examples of novel approaches are particularly welcome. Articles might, for example, show how care data is collected and transformed into useful and usable information, how informatics research is translated into practice, how specific results can be generalised, or perhaps provide case studies that facilitate learning from experience.