A Study on the Knowledge Structure of Cancer Survivors based on Social Network Analysis

Kwon, Sun Young; Bae, Ka Ryeong

doi:10.4040/jkan.2016.46.1.50

Cited by 23 publications

(10 citation statements)

References 15 publications

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“…This study identified the key research topics and analyzed keywords and research trends by performing text network analysis and topic modeling based on the identified 187 articles on the resilience of dementia caregivers, from 1999 to 2020. A knowledge structure is a contextual structure between research concepts [16]. Visualizing the knowledge structure helps understanding the subject, shows the development process, and is regarded as the theoretical basis of the academic field [22].…”

Section: Discussionmentioning

confidence: 99%

“…Text network analysis corresponds to quantitative content analysis and is useful for discovering key topics based on the relationships between word co-occurrences in large-scale texts, interpreting the meaning of potential contexts, and visualizing their knowledge structures [15]. The knowledge structure is a contextual structure comprising research concepts that can show the boundaries or development process of knowledge [16]. Major research concepts discovered through text network analysis are regarded as the theoretical basis of academic fields that include the core topics of knowledge structures [16].…”

Section: Introductionmentioning

confidence: 99%

“…The knowledge structure is a contextual structure comprising research concepts that can show the boundaries or development process of knowledge [16]. Major research concepts discovered through text network analysis are regarded as the theoretical basis of academic fields that include the core topics of knowledge structures [16]. In addition, analyzing the trend of research that changes over time enables systematic understanding and serves as a compass that suggests future research directions [17].…”

Section: Introductionmentioning

confidence: 99%

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An Identification of the Knowledge Structure on the Resilience of Caregivers of People with Dementia using a Text Network Analysis

Kim

Chang

2021

J Korean Gerontol Nurs

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The purpose of this study is to identify the knowledge structure of the concept of resilience of dementia caregivers and to understand core topics and the trends in dementia caregiver resilience research over time. Methods: This study is a quantitative content analysis using text network analysis on the studies on resilience of caregivers of people with dementia. We searched the available literature in two scientific databases, PubMed and the Web of Science. Term frequency, the number of co-occurrence, and centrality were analyzed, and Latent Dirichlet Allocation (LDA) analysis was conducted with Net Miner program. Results: A total 187 articles were identified. The core keywords found were "care", "family", "support", "burden", "intervention", "health", "stress", "relationship", "symptom", "depression", "partner", and "quality of life". Four main topics were identified through the LDA analysis, such as "support from family and social resources", "hardships to overcome", "building strength to overcome adversity", and "coping with negative emotions". There was only one study before 2000, but the number of studies steadily increased to 50 for the years 2011~2015, and 111 for the years 2016~2020. Conclusion: This is the first study to analyze the knowledge structure and research trend of resilience research of caregivers of people with dementia through social network analysis and topic modeling. This study provides a scientific basis of the future studies in Korea. Moreover, it is meaningful in that it brought a comprehensive understanding of the studies by comprehensively analyzing the studies of the resilience of caregivers of people with dementia.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Identification of the Knowledge Structure on the Resilience of Caregivers of People with Dementia using a Text Network Analysis

Kim

Chang

2021

J Korean Gerontol Nurs

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“…It can be used for analyzing users’ thought patterns based on content posted on social media, using text-mining techniques, and is, therefore, useful for understanding the context of connections between networked content [ 10 ]. Furthermore, such analyses and visualizations have the advantage of facilitating a grasp of the knowledge structure of the phenomenon of interest, and showing the direction [ 11 ]. Therefore, by analyzing and categorizing the connectivity of big data-based collection, analysis, and processing, the characteristics and structure of the contents related to the diet behaviors of adolescents—a phenomenon of interest—are identified.…”

Section: Introductionmentioning

confidence: 99%

Network analysis based on big data in social media of Korean adolescents’ diet behaviors

Song

Yoo²,

Yang³

et al. 2022

PLoS ONE

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Adolescents are increasingly interested in weight control; hence, proper health education is important for helping them control their weight properly. This study was designed to pick out social media words that express adolescents’ diet behaviors, and identify the associations and types between such words and the behaviors. It used text-mining techniques and semantic network analysis for related big data collected from the Internet on adolescents’ diet behaviors. Text mining was used to extract meaningful information from unstructured text data, whereas semantic network analysis was used to understand the relationships between keywords. The top five keywords were “obesity,” “health,” “exercise,” “eat,” and “increase” in online news, and “exercise,” “eat,” “weight loss,” “obesity,” and “health” in blogs. The betweenness centrality of “appearance” was particularly higher than that of other centralities in online news. As a result of the CONCOR analysis, eight clusters each were identified in online news and blogs. This study’s results will serve as a basis for weight management-related intervention strategies, reflecting the perspectives of adolescents. It also has significance as basic data to provide correct information, and establish desirable weight control in the future.

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“…In the field of adult cancer and cancer survivor care, the number of studies applying a text network analysis to confirm the knowledge structure and overall research trends in the field of interest have increased over the past 10 years [16,[22][23][24][25]. However, few studies that were published in 2017 applied a network analysis method to child and adolescent cancer survivors [15].…”

Section: Introductionmentioning

confidence: 99%

Web-Based Research Trends on Child and Adolescent Cancer Survivors Over the Last 5 Years: Text Network Analysis and Topic Modeling Study

Kim¹,

Kang²,

Han³

et al. 2022

J Med Internet Res

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Background Being diagnosed with cancer during childhood or adolescence can disrupt important periods in an individual’s physical, psychosocial, and spiritual development and potentially reduce the quality of life (QOL) after treatment. Research is urgently required to improve the QOL for child and adolescent cancer survivors, and it is necessary to analyze the trends in prior research reported in international academic journals to identify knowledge structures. Objective This study aims to identify the main keywords based on network centrality, subgroups (clusters) of keyword networks by using a cohesion analysis method, and the main theme of child and adolescent cancer survivor–related research abstracts through topic modeling. This study also aims to label the subgroups by comparing the results of the cohesion and topic modeling. Methods A text network analysis method and topic modeling were used to explore the main trends in child and adolescent cancer survivor research by structuring a network of keyword (semantic morphemes) co-occurrence in the abstracts of articles published in 5 major web-based databases from 2016 to 2020. A total of 1677 child and adolescent cancer survivor–related studies were used for data analyses. Data selection, processing, and analyses were also conducted. Results The top 5 keywords in terms of degree and eigenvector centrality were risk, control interval, radiation, childhood cancer treatment, and diagnosis. Of the 1677 studies used for data analyses, cluster 1 included 780 (46.51%) documents under risk management, cluster 2 contained 557 (33.21%) articles under health-related QOL and supportive care, and cluster 3 consisted of 340 (20.27%) studies under cancer treatment and complications. Conclusions This study is significant in that it confirms the knowledge structure based on the main keywords and cross-disciplinary trends in child and adolescent cancer survivor research published in the last 5 years worldwide. The primary goal of child and adolescent cancer survivor research is to prevent and manage the various aspects of the problems encountered during the transition to a normal life and to improve the overall QOL. To this end, it is necessary to further revitalize the study of the multidisciplinary team approach for the promotion of age-specific health behaviors and the development of intervention strategies with increased feasibility for child and adolescent cancer survivors.

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A Study on the Knowledge Structure of Cancer Survivors based on Social Network Analysis

Cited by 23 publications

References 15 publications

An Identification of the Knowledge Structure on the Resilience of Caregivers of People with Dementia using a Text Network Analysis

An Identification of the Knowledge Structure on the Resilience of Caregivers of People with Dementia using a Text Network Analysis

Network analysis based on big data in social media of Korean adolescents’ diet behaviors

Web-Based Research Trends on Child and Adolescent Cancer Survivors Over the Last 5 Years: Text Network Analysis and Topic Modeling Study

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