Aesthetic Plastic Surgery Issues During the COVID-19 Period Using Topic Modeling

Sanghoo Yoon, Young A Kim
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Abstract

This study investigates media coverage of cosmetic surgery in South Korea from 2014 to 2023 using text mining techniques applied to news articles from BigKinds. It focuses on assessing the prevalence of objective information and the societal impacts of capital-driven misinformation.  The research methodology involved optimal topic modeling through perplexity, likelihood, BIC, and similarity measures, identifying five themes within the cosmetic surgery news corpus. Further analysis included quantitative topic recognition via fuzzy clustering by period, sentiment analysis, and network analysis utilizing n-gram techniques to explore relationships between key terms. Findings reveal five main topics covered in cosmetic surgery news: Consumer Psychology, Cosmetic Surgery Market, Cosmetic Companies and Technologies, Side Effects and Incidents, and the Tourism Industry. The period from 2014 to 2016 saw significant coverage, particularly on medical tourism and surgical side effects, while in 2017, attention shifted to the surgical process and market stability. From 2018 onward, news coverage expanded, especially in May, focusing on cosmetic technology and related industries amid increased outdoor activities. With the COVID-19 pandemic in 2020, there was a resurgence in coverage of the cosmetic surgery market. In 2023, post-pandemic, there was an uptick in articles related to cosmetic surgery technology industries and support funds. The core words in cosmetic surgery news were spreading around "plastic surgery," "China," and "Botulinum". The study sheds light on the potential influence of capital on media portrayals of cosmetic surgery and the resulting societal consequences of misinformation.
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利用主题建模研究 COVID-19 期间的美容整形问题
本研究利用 BigKinds 新闻文章的文本挖掘技术,调查了 2014 年至 2023 年韩国媒体对整容手术的报道。研究重点是评估客观信息的普遍性以及资本驱动的错误信息对社会的影响。 研究方法包括通过perplexity、likelihood、BIC和相似性度量进行最佳主题建模,在整容手术新闻语料库中确定五个主题。进一步的分析包括通过按时期进行模糊聚类的定量主题识别、情感分析,以及利用 n-gram 技术进行网络分析,以探索关键术语之间的关系。研究结果揭示了整容手术新闻中涉及的五大主题:消费者心理、整容手术市场、整容公司和技术、副作用和事故以及旅游业。2014 年至 2016 年期间,报道量较大,尤其是医疗旅游和手术副作用,而 2017 年,关注点转向手术过程和市场稳定性。从2018年起,新闻报道范围扩大,尤其是5月份,在户外活动增多的情况下,重点关注美容技术及相关产业。2020 年,随着 COVID-19 的大流行,美容整形市场的报道再度兴起。疫情过后的 2023 年,与整容技术产业和支持基金相关的文章有所增加。整容新闻的核心词围绕 "整容"、"中国 "和 "肉毒杆菌 "展开。这项研究揭示了资本对媒体描述整容手术的潜在影响,以及错误信息造成的社会后果。
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来源期刊
International Journal on Advanced Science, Engineering and Information Technology
International Journal on Advanced Science, Engineering and Information Technology Agricultural and Biological Sciences-Agricultural and Biological Sciences (all)
CiteScore
1.40
自引率
0.00%
发文量
272
期刊介绍: International Journal on Advanced Science, Engineering and Information Technology (IJASEIT) is an international peer-reviewed journal dedicated to interchange for the results of high quality research in all aspect of science, engineering and information technology. The journal publishes state-of-art papers in fundamental theory, experiments and simulation, as well as applications, with a systematic proposed method, sufficient review on previous works, expanded discussion and concise conclusion. As our commitment to the advancement of science and technology, the IJASEIT follows the open access policy that allows the published articles freely available online without any subscription. The journal scopes include (but not limited to) the followings: -Science: Bioscience & Biotechnology. Chemistry & Food Technology, Environmental, Health Science, Mathematics & Statistics, Applied Physics -Engineering: Architecture, Chemical & Process, Civil & structural, Electrical, Electronic & Systems, Geological & Mining Engineering, Mechanical & Materials -Information Science & Technology: Artificial Intelligence, Computer Science, E-Learning & Multimedia, Information System, Internet & Mobile Computing
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