Evidence map›Paper›PMID 36053565›Full record

ArticleJournal of medical Internet research2022

Public Discourse and Sentiment Toward Dementia on Chinese Social Media: Machine Learning Analysis of Weibo Posts.

Dexia Kong, Anfan Chen, Jingwen Zhang, Xiaoling Xiang, W Q Vivian Lou, Timothy Kwok, Bei Wu

Open access · goldAbstract read
In one paragraph

Article in Journal of medical Internet research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
27.5field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Observational
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Patients' perspectives on irritable bowel syndrome: a qualitative analysis based on social media in China.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2023
    Article
  12. Article
  13. Article
  14. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 5 institutions in 2 countries.

Dexia KongDepartment of Social Work, Chinese University of Hong Kong, Hong Kong, Hong Kong.ORCID 0000-0002-5311-1906
Anfan ChenSchool of Journalism and Communication, The Chinese University of Hong Kong, Hong Kong, Hong Kong.ORCID 0000-0002-7406-0415
Jingwen ZhangDepartment of Communication, University of California, Davis, Davis, CA, United States.ORCID 0000-0003-1733-6857
Xiaoling XiangSchool of Social Work, University of Michigan, Ann Arbor, MI, United States.ORCID 0000-0002-4926-4707
W Q Vivian LouDepartment of Social Work & Social Administration, Sau Po Centre on Ageing, University of Hong Kong, Hong Kong, Hong Kong.ORCID 0000-0001-7000-1306
Timothy KwokFaculty of Medicine, Chinese University of Hong Kong, Hong Kong, Hong Kong.ORCID 0000-0001-9253-3549
Bei WuRory Meyers College of Nursing, New York University, New York, NY, United States.ORCID 0000-0002-6891-244X
Chinese University of Hong Kong · HKNew York University · USUniversity of California, Davis · USUniversity of Hong Kong · HKUniversity of Michigan · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDementia 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.

objectiveThis 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.

methodsIn 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).

resultsTopic 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.

conclusionsOverall, 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. The development of strategies to address unfavorable perceptions of dementia requires policy and public health attention. The results further reveal that an urgent need exists to increase public knowledge about dementia. Social media platforms potentially could be leveraged for future dementia education interventions to increase dementia awareness and promote positive attitudes.

Indexed as

Machine LearningSocial MediaAgedAttitudeChinaDementiaHumansLanguageagingcontent analysisdementiaelderly populationinfodemiologymachine learningpublic discoursesentimentsentiment analysissocial mediasocial supportthematic analysistopic modelingWeibo

Identifiers

PMID36053565
PMCPMC9482068
OpenAlexW4294275727

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.