TY - JOUR
T1 - Public Discourse and Sentiment Toward Dementia on Chinese Social Media
T2 - Machine Learning Analysis of Weibo Posts
AU - Kong, Dexia
AU - Chen, Anfan
AU - Zhang, Jingwen
AU - Xiang, Xiaoling
AU - Lou, W. Q.Vivian
AU - Kwok, Timothy
AU - Wu, Bei
N1 - Funding Information:
DK, JZ, and AC contributed to the study’s design. AC collected Weibo data. DK, JZ, and AC contributed to data analysis. DK and AC contributed to the design and drawing of figures. All authors contributed to the writing and revising of the manuscript.
Publisher Copyright:
©Dexia Kong, Anfan Chen, Jingwen Zhang, Xiaoling Xiang, W Q Vivian Lou, Timothy Kwok, Bei Wu.
PY - 2022/9/1
Y1 - 2022/9/1
N2 - Background: Dementia is a global public health priority due to rapid growth of the aging population. As China has the world’s largest population with dementia, this debilitating disease has created tremendous challenges for older adults, family caregivers, and health care systems on the mainland nationwide. However, public awareness and knowledge of the disease remain limited in Chinese society. Objective: This study examines online public discourse and sentiment toward dementia among the Chinese public on a leading Chinese social media platform Weibo. Specifically, this study aims to (1) assess and examine public discourse and sentiment toward dementia among the Chinese public, (2) determine the extent to which dementia-related discourse and sentiment vary among different user groups (ie, government, journalists/news media, scientists/experts, and the general public), and (3) characterize temporal trends in public discourse and sentiment toward dementia among different user groups in China over the past decade. Methods: In total, 983,039 original dementia-related posts published by 347,599 unique users between 2010 and 2021, together with their user information, were analyzed. Machine learning analytical techniques, including topic modeling, sentiment analysis, and semantic network analyses, were used to identify salient themes/topics and their variations across different user groups (ie, government, journalists/news media, scientists/experts, and the general public). Results: Topic modeling results revealed that symptoms, prevention, and social support are the most prevalent dementia-related themes on Weibo. Posts about dementia policy/advocacy have been increasing in volume since 2018. Raising awareness is the least discussed topic over time. Sentiment analysis indicated that Weibo users generally attach negative attitudes/emotions to dementia, with the general public holding a more negative attitude than other user groups. Conclusions: Overall, dementia has received greater public attention on social media since 2018. In particular, discussions related to dementia advocacy and policy are gaining momentum in China. However, disparaging language is still used to describe dementia in China; therefore, a nationwide initiative is needed to alter the public discourse on dementia. The results contribute to previous research by providing a macrolevel understanding of the Chinese public’s discourse and attitudes toward dementia, which is essential for building national education and policy initiatives to create a dementia-friendly society. Our findings indicate that dementia is associated with negative sentiments, and symptoms and prevention dominate public discourse.
AB - Background: Dementia is a global public health priority due to rapid growth of the aging population. As China has the world’s largest population with dementia, this debilitating disease has created tremendous challenges for older adults, family caregivers, and health care systems on the mainland nationwide. However, public awareness and knowledge of the disease remain limited in Chinese society. Objective: This study examines online public discourse and sentiment toward dementia among the Chinese public on a leading Chinese social media platform Weibo. Specifically, this study aims to (1) assess and examine public discourse and sentiment toward dementia among the Chinese public, (2) determine the extent to which dementia-related discourse and sentiment vary among different user groups (ie, government, journalists/news media, scientists/experts, and the general public), and (3) characterize temporal trends in public discourse and sentiment toward dementia among different user groups in China over the past decade. Methods: In total, 983,039 original dementia-related posts published by 347,599 unique users between 2010 and 2021, together with their user information, were analyzed. Machine learning analytical techniques, including topic modeling, sentiment analysis, and semantic network analyses, were used to identify salient themes/topics and their variations across different user groups (ie, government, journalists/news media, scientists/experts, and the general public). Results: Topic modeling results revealed that symptoms, prevention, and social support are the most prevalent dementia-related themes on Weibo. Posts about dementia policy/advocacy have been increasing in volume since 2018. Raising awareness is the least discussed topic over time. Sentiment analysis indicated that Weibo users generally attach negative attitudes/emotions to dementia, with the general public holding a more negative attitude than other user groups. Conclusions: Overall, dementia has received greater public attention on social media since 2018. In particular, discussions related to dementia advocacy and policy are gaining momentum in China. However, disparaging language is still used to describe dementia in China; therefore, a nationwide initiative is needed to alter the public discourse on dementia. The results contribute to previous research by providing a macrolevel understanding of the Chinese public’s discourse and attitudes toward dementia, which is essential for building national education and policy initiatives to create a dementia-friendly society. Our findings indicate that dementia is associated with negative sentiments, and symptoms and prevention dominate public discourse.
KW - Weibo
KW - aging
KW - content analysis
KW - dementia
KW - elderly population
KW - infodemiology
KW - machine learning
KW - public discourse
KW - public discourse
KW - sentiment
KW - sentiment analysis
KW - social media
KW - social support
KW - thematic analysis
KW - topic modeling
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UR - http://www.scopus.com/inward/citedby.url?scp=85137162143&partnerID=8YFLogxK
U2 - 10.2196/39805
DO - 10.2196/39805
M3 - Article
C2 - 36053565
AN - SCOPUS:85137162143
SN - 1439-4456
VL - 24
JO - Journal of medical Internet research
JF - Journal of medical Internet research
IS - 9
M1 - e39805
ER -