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KMID : 0379220240440010051
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Journal of Korea Gerontological Society
2024 Volume.44 No. 1 p.51 ~ p.70
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Exploring aging anxiety in the 2030 generation: Topic modeling and semantic network analysis
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Li Lin
An Soon-Tae
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Abstract
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The primary objective of this paper is to explore how the generation of 2030 experiences aging anxiety and to identify the predominant themes through which they encounter it. The study collected 10,000 Instagram posts utilizing the hashtag "old," as this social media platform is primarily used by the generation of 2030. For the analysis, we employed techniques such as Term Frequency (TF) and Term Frequency Inverse Document Frequency (TF-IDF) analysis, LDA(Latent Dirichlet Allocation) topic modeling, and semantic network analysis. Results show that aging anxiety in the 2030 generation is primarily observed in six areas, namely the mental, physical, occupational, social, familial, and parenting domains. The most pronounced aging anxiety for the 2030 generation was identified in the mental domain.
Furthermore, the 2030 generation commonly harbors concerns about midlife social relationships, the fear of facing changes, and apprehensions related to caregiving labor. Based on these research findings, we have outlined the experience of aging anxiety within the 2030 generation and provided strategies to alleviate it.
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KEYWORD
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2030 generation, Instagram, Text mining, Topic Modeling, Semantic Network Analysis, Aging anxiety
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