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Public perception on 'healthy ageing' in the past decade: An unsupervised machine learning of 63,809 Twitter posts
The World Health Organization (WHO) started the initiative on healthy ageing from 2016 to 2020, which has now continued into the United Nations (UN) Decade of Healthy Ageing 2021–2030. Research into healthy ageing and healthy ageing communities have emphasized that the concept of healthy ageing enco...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9898637/ https://www.ncbi.nlm.nih.gov/pubmed/36747557 http://dx.doi.org/10.1016/j.heliyon.2023.e13118 |
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author | Ng, Qin Xiang Lee, Dawn Yi Xin Yau, Chun En Lim, Yu Liang Liew, Tau Ming |
author_facet | Ng, Qin Xiang Lee, Dawn Yi Xin Yau, Chun En Lim, Yu Liang Liew, Tau Ming |
author_sort | Ng, Qin Xiang |
collection | PubMed |
description | The World Health Organization (WHO) started the initiative on healthy ageing from 2016 to 2020, which has now continued into the United Nations (UN) Decade of Healthy Ageing 2021–2030. Research into healthy ageing and healthy ageing communities have emphasized that the concept of healthy ageing encompasses a plurality of views and has multiple dimensions. Anchored in a transdisciplinary approach, the present report thus aimed to investigate public perceptions of healthy ageing via a deep analysis of social media posts on Twitter. Original tweets, containing the terms “Healthy Ageing” OR “healthy aging” OR “healthyageing” OR “healthyaging”, and posted in English between 1 January 2012 and 30 June 2022 were extracted. Bidirectional Encoder Representations from Transformers (BERT) Named Entity Recognition was applied to select for individual users. Topic modelling, specifically BERTopic was used to generate interpretable topics and descriptions pertaining to the concept of healthy ageing. Subsequently, manual thematic analysis was performed by the study investigators, with independent reviews of the topic labels and themes. A total of 63,809 unique tweets were analyzed and clustered semantically into 16 topics. The public perception of healthy ageing could be broadly grouped into three themes: (1) healthy diet and lifestyle, (2) maintaining normal bodily functions and (3) preventive care. While most perceptions dovetail WHO's definition, there are some points regarding skin appearances, beauty and aging that should be closely considered in the design of initiatives in the UN Decade of Healthy Ageing and beyond. |
format | Online Article Text |
id | pubmed-9898637 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-98986372023-02-05 Public perception on 'healthy ageing' in the past decade: An unsupervised machine learning of 63,809 Twitter posts Ng, Qin Xiang Lee, Dawn Yi Xin Yau, Chun En Lim, Yu Liang Liew, Tau Ming Heliyon Research Article The World Health Organization (WHO) started the initiative on healthy ageing from 2016 to 2020, which has now continued into the United Nations (UN) Decade of Healthy Ageing 2021–2030. Research into healthy ageing and healthy ageing communities have emphasized that the concept of healthy ageing encompasses a plurality of views and has multiple dimensions. Anchored in a transdisciplinary approach, the present report thus aimed to investigate public perceptions of healthy ageing via a deep analysis of social media posts on Twitter. Original tweets, containing the terms “Healthy Ageing” OR “healthy aging” OR “healthyageing” OR “healthyaging”, and posted in English between 1 January 2012 and 30 June 2022 were extracted. Bidirectional Encoder Representations from Transformers (BERT) Named Entity Recognition was applied to select for individual users. Topic modelling, specifically BERTopic was used to generate interpretable topics and descriptions pertaining to the concept of healthy ageing. Subsequently, manual thematic analysis was performed by the study investigators, with independent reviews of the topic labels and themes. A total of 63,809 unique tweets were analyzed and clustered semantically into 16 topics. The public perception of healthy ageing could be broadly grouped into three themes: (1) healthy diet and lifestyle, (2) maintaining normal bodily functions and (3) preventive care. While most perceptions dovetail WHO's definition, there are some points regarding skin appearances, beauty and aging that should be closely considered in the design of initiatives in the UN Decade of Healthy Ageing and beyond. Elsevier 2023-01-21 /pmc/articles/PMC9898637/ /pubmed/36747557 http://dx.doi.org/10.1016/j.heliyon.2023.e13118 Text en © 2023 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Research Article Ng, Qin Xiang Lee, Dawn Yi Xin Yau, Chun En Lim, Yu Liang Liew, Tau Ming Public perception on 'healthy ageing' in the past decade: An unsupervised machine learning of 63,809 Twitter posts |
title | Public perception on 'healthy ageing' in the past decade: An unsupervised machine learning of 63,809 Twitter posts |
title_full | Public perception on 'healthy ageing' in the past decade: An unsupervised machine learning of 63,809 Twitter posts |
title_fullStr | Public perception on 'healthy ageing' in the past decade: An unsupervised machine learning of 63,809 Twitter posts |
title_full_unstemmed | Public perception on 'healthy ageing' in the past decade: An unsupervised machine learning of 63,809 Twitter posts |
title_short | Public perception on 'healthy ageing' in the past decade: An unsupervised machine learning of 63,809 Twitter posts |
title_sort | public perception on 'healthy ageing' in the past decade: an unsupervised machine learning of 63,809 twitter posts |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9898637/ https://www.ncbi.nlm.nih.gov/pubmed/36747557 http://dx.doi.org/10.1016/j.heliyon.2023.e13118 |
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