This paper is in the following e-collection/theme issue:

Peer-to-Peer Support and Online Communities (636) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1641) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1041) Information Seeking, Information Needs (465) Information Models (28) Emotional, Social, Psychological Support for Cancer (194) Mass Media/Social Media Communication and Campaigns (461) Infoveillance and Social Listening (209)

Published on in Vol 23, No 11 (2021): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/26310, first published .
Cancer Communication and User Engagement on Chinese Social Media: Content Analysis and Topic Modeling Study

Cancer Communication and User Engagement on Chinese Social Media: Content Analysis and Topic Modeling Study

Cancer Communication and User Engagement on Chinese Social Media: Content Analysis and Topic Modeling Study

Authors of this article:

Liang Chen1 Author Orcid Image ;   Pianpian Wang2 Author Orcid Image ;   Xin Ma3 Author Orcid Image ;   Xiaohui Wang4 Author Orcid Image
Article Authors Cited by (14) Tweetations (3) Metrics

Journals

  1. Shan Y, Ji M, Xie W, Lam K, Chow C. Public Trust in Artificial Intelligence Applications in Mental Health Care: Topic Modeling Analysis. JMIR Human Factors 2022;9(4):e38799 View
  2. Xu H, Xiao M, Zeng J, Hao H. Green-Labelled Rice versus Conventional Rice: Perception and Emotion of Chinese Consumers Based on Review Mining. Foods 2022;12(1):87 View
  3. Wu X, Li Z, Xu L, Li P, Liu M, Huang C. COVID-19 Vaccine–Related Information on the WeChat Public Platform: Topic Modeling and Content Analysis. Journal of Medical Internet Research 2023;25:e45051 View
  4. Zenone M, Snyder J, Bélisle-Pipon J, Caulfield T, van Schalkwyk M, Maani N. Advertising Alternative Cancer Treatments and Approaches on Meta Social Media Platforms: Content Analysis. JMIR Infodemiology 2023;3:e43548 View
  5. Ramamoorthy T, Mappillairaju B. Tweet topics on cancer among Indian Twitter users—computational approach using latent Dirichlet allocation topic modelling. Journal of Computational Social Science 2023;6(2):1033 View
  6. Jiménez Sánchez L, Moreno Á, Zeler I. Comunicación para la prevención de cáncer de piel: un análisis del uso de Facebook para la comunicación de salud en España. Redmarka. Revista de Marketing Aplicado 2023;27(1):78 View
  7. Ma Z, Ma R, Zhao X, Wang X. Stories that engage the audience: An investigation of popular breast cancer narratives on social media. Telematics and Informatics 2023;85:102048 View
  8. Jiménez-Sánchez L, Moreno Á. Comunicación para prevenir el cáncer de piel: un análisis del uso estratégico de la red social Twitter en España. Revista de Ciencias de la Comunicación e Información 2023;28:190 View
  9. Shah A, Lee K, Hidayat A, Falchook A, Muhammad W. A text analytics approach for mining public discussions in online cancer forum: Analysis of multi-intent lung cancer treatment dataset. International Journal of Medical Informatics 2024;184:105375 View
  10. Da C, Duan Y, Ji Z, Chen J, Xia H, Weng Y, Zhou T, Yuan C, Cai T. Assessing the needs of patients with breast cancer and their families across various treatment phases using a Latent Dirichlet Allocation model: a text-mining approach to online health communities. Supportive Care in Cancer 2024;32(5) View
  11. Zhao K, Li X, Li J. Cancer Prevention and Treatment on Chinese Social Media: Machine Learning-Based Content Analysis Study (Preprint). Journal of Medical Internet Research 2024 View
  12. Xu H, Tian C. Attitudes and preferences of the Chinese public towards products made from recycled materials: A text mining approach. Resources, Conservation & Recycling Advances 2024;24:200234 View
  13. Liu Y, Shan Y, Sun S, Ji M, Zhou S, You Y, Liu H, Shen Y. Topic modeling and content analysis of people’s anxiety-related concerns raised on a computer-mediated health platform. Scientific Reports 2024;14(1) View
  14. Jeong W, Song E, Jeong E, Oh K, Lee H, Jun J. Cancer-related Keywords in 2023: Insights from Text Mining of a Major Consumer Portal. Healthcare Informatics Research 2024;30(4):398 View