scholarly journals Social network analysis of the mental health sub-topic on the MedlinePlus subject directory

2020 ◽  
Vol 25 (4) ◽  
Author(s):  
Yifan Zhu ◽  
◽  
Jin Zhang ◽  
Keyword(s):  
Mental Health ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Domain Experts ◽  
Health Consumer ◽  
System Method ◽  
Health Related ◽  
Subject Directory ◽  
The Subject

Introduction. A subject directory plays an important role in a Web portal and it helps users effectively navigate the portal. This study examines a subject directory system related to Mental Health in the MedlinePlus portal and provides suggestions of optimisation to enhance the subject directory system. Method. A mixed research method combining social network analysis and inferential statistics was applied. Analysis. A structural and a semantic social network were built regarding the selected health topics related to mental health in the MedlinePlus portal. The two networks were compared and the outcomes were evaluated by domain experts. Results. Among the ninety-nine collected health topics related to mental health, three themes were identified through the visualisation analysis regarding grouped health topics. Patterns and characteristics of each theme group were discussed. As a result, fifty-five bidirectional and twenty-three unidirectional edges were identified and recommended to be added to the corresponding health topic pages. The recommended results indicate that the subject directory of specific mental health related topics is well constructed, while health consumer groups related topics might need more improvements. The optimised subject directory has significantly stronger semantic connection, and the results of the recommendations are consistent with the evaluation outcome of two domain experts. Conclusions. The findings of this study can provide ideas of optimising and enhancing the subject directory system to the public health portal creators and health professionals, and benefit health consumers for seeking health information online. The methodologies applied in this study may provide a novel way to investigate and enhance subject directories in general.

scholarly journals Nature-Based Interventions for Mental Health Care: Social Network Analysis as a Tool to Map Social Farms and their Response to Social Inclusion and Community Engagement

2019 ◽  
Vol 16 (18) ◽  
pp. 3501 ◽  
Author(s):  
Marta Borgi ◽  
Mario Marcolin ◽  
Paolo Tomasin ◽  
Cinzia Correale ◽  
Aldina Venerosi ◽  
...  
Keyword(s):  
Mental Health ◽  
Health Care ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Mental Health Care ◽  
Rural Areas ◽  
Social Inclusion ◽  
Local Communities ◽  
Social Farming

Social farming represents a hybrid governance model in which public bodies, local communities, and economic actors act together to promote health and social inclusion in rural areas. Although relational variables are crucial to foster social farm performance, the relational system in which farms are embedded has still not been fully described. Using social network analysis, here we map the nature of the links of a selected sample of social farms operating in Northern Italy. We also explore possible network variations following specific actions taken to potentiate local social farming initiatives. The results show a certain degree of variability in terms of the extension and features of the examined networks. Overall, the actions taken appear to be significant to enlarge and diversify farms’ networks. Social farming has the potential to provide important benefits to society and the environment and to contrast vulnerability in rural areas. Being able to create social and economic networks of local communities, social farming may also represent an innovative way to respond to the cultural shift from institutional psychiatry to community-based mental health care. This study emphasizes the critical role played by network facilitation in diversifying actors, promoting heterogeneous relationships, and, in turn, system complexity.

scholarly journals Big Data Analysis of the Key Attributes Related to Stress and Mental Health in Korean Taekwondo Student Athletes

2022 ◽  
Vol 14 (1) ◽  
pp. 477
Author(s):  
Sung-Un Park ◽  
Jung-Woo Jeon ◽  
Hyunkyun Ahn ◽  
Yoon-Kwon Yang ◽  
Wi-Young So
Keyword(s):  
Mental Health ◽  
Big Data ◽  
Social Network ◽  
Data Analysis ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Correlation Analysis ◽  
Student Athletes ◽  
Big Data Analysis ◽  
Degree Centrality

In the present study, we used big data analysis to examine the key attributes related to stress and mental health among Korean Taekwondo student-athletes. Keywords included “Taekwondo + Student athlete + Stress + Mental health”. Naver and Google databases were searched to identify research published between 1 January 2010 and 31 December 2019. Text-mining analysis was performed on unstructured texts using TEXTOM 4.5, with social network analysis performed using UCINET 6. In total, 3149 large databases (1.346 MB) were analyzed. Two types of text-mining analyses were performed, namely, frequency analysis and term frequency-inverse document frequency analysis. For the social network analysis, the degree centrality and convergence of iterated correlation analysis were used to deduce the node-linking degree in the network and to identify clusters. The top 10 most frequently used terms were “stress”, “Taekwondo”, “health”, “player”, “student”, “mental”, “exercise”, “mental health”, “relieve”, and “child.” The top 10 most frequently occurring results of the TF-IDF analysis were “Taekwondo”, “health”, “player”, “exercise”, “student”, “mental”, “stress”, “mental health”, “child” and “relieve”. The degree centrality analysis yielded similar results regarding the top 10 terms. The convergence of iterated correlation analysis identified six clusters: student, start of dream, diet, physical and mental, sports activity, and adult Taekwondo center. Our results emphasize the importance of designing interventions that attenuate stress and improve mental health among Korean Taekwondo student-athletes.

scholarly journals Research Expertise and the Framework of Harms: Social Network Analysis, Phase One

2020 ◽  
Author(s):  
Margo Hilbrecht ◽  
◽  
David Baxter ◽  
Alexander V. Graham ◽  
Maha Sohail
Keyword(s):  
Public Health ◽  
Mental Health ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Evidence Base ◽  
Public Health Approach ◽  
National Strategy ◽  
The Uk ◽  
Gambling Harms

In 2019, the Gambling Commission announced a National Strategy to Reduce Gambling Harms. Underlying the strategy is the Framework of Harms, outlined in Measuring gambling-related harms: A framework for action. "The Framework" adopts a public health approach to address gambling-related harm in Great Britain across multiple levels of measurement. It comprises three primary factors and nine related subfactors. To advance the National Strategy, all componentsneed to be supported by a strong evidence base. This report examines existing research expertise relevant to the Framework amongacademics based in the UK. The aim is to understand the extent to which the Framework factors and subfactors have been studied in order to identify gaps in expertise and provide evidence for decision making thatisrelevant to gambling harms research priorities. A social network analysis identified coauthor networks and alignment of research output with the Framework. The search strategy was limited to peer-reviewed items and covered the 12-year period from 2008 to 2019. Articles were selected using a Web of Science search. Of the 1417 records identified in the search, the dataset was refined to include only those articles that could be assigned to at least one Framework factor (n = 279). The primary factors and subfactors are: Resources:Work and Employment, Money and Debt, Crime;Relationships:Partners, Families and Friends, Community; and Health:Physical Health, Psychological Distress, and Mental Health. We used Gephi software to create visualisations reflecting degree centrality (number of coauthor networks) so that each factor and subfactor could be assessed for the density of research expertise and patterns of collaboration among coauthors. The findings show considerable variation by framework factor in the number of authors and collaborations, suggesting a need to develop additional research capacity to address under-researched areas. The Health factor subcategory of Mental Health comprised almost three-quarters of all citations, with the Resources factor subcategory of Money and Debt a distant second at 12% of all articles. The Relationships factor, comprised of two subfactors, accounted for less than 10%of total articles. Network density varied too. Although there were few collaborative networks in subfactors such as Community or Work and Employment, all Health subfactors showed strong levels of collaboration. Further, some subfactors with a limited number of researchers such as Partners, Families, and Friends and Money and debt had several active collaborations. Some researchers’ had publications that spanned multiple Framework factors. These multiple-factor researchers usually had a wide range of coauthors when compared to those who specialised (with the exception of Mental Health).Others’ collaborations spanned subfactors within a factor area. This was especially notable forHealth. The visualisations suggest that gambling harms research expertise in the UK has considerable room to grow in order to supporta more comprehensive, locally contextualised evidence base for the Framework. To do so, priority harms and funding opportunities will need further consideration. This will require multi-sector and multidisciplinary collaboration consistent with the public health approach underlying the Framework. Future research related to the present analysis will explore the geographic distribution of research activity within the UK, and research collaborations with harms experts internationally.

scholarly journals The Rise of K-Popper's Political Awareness in Twitter and What It Means for All of Us

2022 ◽  
Vol 19 (2) ◽  
pp. 253
Author(s):  
Dani Fadillah ◽  
Arif Ardy Wibowo ◽  
Nunik Hariyati ◽  
Uspal Jandevi
Keyword(s):  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Political Movements ◽  
Political Awareness ◽  
Political Stance ◽  
The Subject

The Omnibus Law, which was passed on October 5, 2020, has discontented students and workers who protested on the streets. The ratification of the Omnibus Law has an effect on public opinion-raising activities, both those who support and who reject the ratification of the Omnibus Law, are crowded on Twitter social media. The active account of K-Poppers who took part became a line against the opinion of the Omnibus Law, so a question arises whether this is a phenomenon of the rise of political awareness of K-Poppers in Indonesia? This study analyzes the role of K-Poppers in socio-political movements in several countries globally, especially in Indonesia. The type of research used is a case study of the K-Poppers movement in the case of the ratification of the Omnibus Law as the subject in writing this paper. Collecting data using Social Network Analysis (SNA) and observing the activities of K-Poppers in Indonesia when parliament passed the controversial Omnibus Law. The results of this study indicate that Indonesian K-Poppers maximize their function as part of Indonesian citizens to express their political stance. They also showed their political involvement when creating hashtags, organizing other K-Pop crowds, and at the same time providing support to activists who rejected the Omnibus Law.

Social Network Analysis

2016 ◽  
pp. 237-265 ◽  
Author(s):  
Paramita Dey ◽  
Sarbani Roy
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Networking ◽  
Group Formation ◽  
Social Structures ◽  
Node Degree ◽  
Network Characteristics ◽  
Meaningful Information ◽  
The Subject

Social Network Analysis (SNA) looks at how our world is connected. The mapping and measuring of connections and interactions between people, groups, organizations and other connected entities are very significant and have been the subject of a fascinating interdisciplinary topic . Social networks like Twitter, Facebook, LinkedIn are very large in size with millions of vertices and billions of edges. To collect meaningful information from these densely connected graph and huge volume of data, it is important to find proper topology of the network as well as analyze different network parameters. The main objective of this work is to study network characteristics commonly used to explain social structures. In this chapter, we discuss all important aspect of social networking and analyze through a real time example. This analysis shows some distinguished parameters like number of clusters, group formation, node degree distribution, identifying influential leader/seed node etc. which can be used further for feature extraction.

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