Analysis of Korean Mothers' Adolescent Issues in Prior- and Post- COVID-19 Period: A Text Mining Approach
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Purpose: This study aims to identify factors that should be considered for data-driven youth policy development by analyzing various adolescent-related issues raised in online platforms during prior- and post-COVID-19 periods, using text mining techniques. Design/methodology/approach: A total of 464,590 posts from January 2018 to April 2021 on the "82cook" website were collected. The data were analyzed by text mining techniques, including term frequency-inverse document frequency (TF-IDF), latent dirichlet allocation (LDA), and network analyses, to identify the most prominent concerns faced by young people in South Korea. Findings: The study results showed that the most frequently discussed issues among mothers of adolescent children were 'educational environment,' 'physical health,' 'mental health,' 'studying abroad,' and 'child abuse.' As parents and children spend more time together during and early post-COVID-19 periods, 'child abuse' has emerged as a major issue. The distribution results by topic in the prior- and post-COVID-19 periods showed that education (44% and 41%, respectively) and health and well-being (25% and 28%, respectively) ranked the two most-frequently discussed issues among mothers. Research limitations/implications: The limitation of this study is that the subjectivity of the researcher may have biased the setting of the topic names about the youth, and the classification results themselves cannot be considered perfect, as the algorithm automatically classified them. Originality/value: This paper provides academic originality and value by leveraging text mining to analyze the issues related to young people, thereby offering implications for policymakers, surpassing the informational value of traditional methods such as surveys or expert panels.
Abstract
Purpose: This study aims to identify factors that should be considered for data-driven youth policy development by analyzing various adolescent-related issues raised in online platforms during prior- and post-COVID-19 periods, using text mining techniques. Design/methodology/approach: A total of 464,590 posts from January 2018 to April 2021 on the "82cook" website were collected. The data were analyzed by text mining techniques, including term frequency-inverse document frequency (TF-IDF), latent dirichlet allocation (LDA), and network analyses, to identify the most prominent concerns faced by young people in South Korea. Findings: The study results showed that the most frequently discussed issues among mothers of adolescent children were 'educational environment,' 'physical health,' 'mental health,' 'studying abroad,' and 'child abuse.' As parents and children spend more time together during and early post-COVID-19 periods, 'child abuse' has emerged as a major issue. The distribution results by topic in the prior- and post-COVID-19 periods showed that education (44% and 41%, respectively) and health and well-being (25% and 28%, respectively) ranked the two most-frequently discussed issues among mothers. Research limitations/implications: The limitation of this study is that the subjectivity of the researcher may have biased the setting of the topic names about the youth, and the classification results themselves cannot be considered perfect, as the algorithm automatically classified them. Originality/value: This paper provides academic originality and value by leveraging text mining to analyze the issues related to young people, thereby offering implications for policymakers, surpassing the informational value of traditional methods such as surveys or expert panels.
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