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

Overview

Journal J Med Internet Res

Publisher JMIR Publications

Specialty Medical Informatics

Date 2021 Nov 10

PMID 34757320

Citations 8

Authors

Liang Chen

Pianpian Wang

Xin Ma

Xiaohui Wang

Affiliations

Soon will be listed here.

Abstract

Background: Cancer ranks among the most serious public health challenges worldwide. In China-the world's most populous country-about one-quarter of the population consists of people with cancer. Social media has become an important platform that the Chinese public uses to express opinions.

Objective: We investigated cancer-related discussions on the Chinese social media platform Weibo (Sina Corporation) to identify cancer topics that generate the highest levels of user engagement.

Methods: We conducted topic modeling and regression analyses to analyze and visualize cancer-related messages on Weibo and to examine the relationships between different cancer topics and user engagement (ie, the number of retweets, comments, and likes).

Results: Our results revealed that cancer communication on Weibo has generally focused on the following six topics: social support, cancer treatment, cancer prevention, women's cancers, smoking and skin cancer, and other topics. Discussions about social support and cancer treatment attracted the highest number of users and received the greatest numbers of retweets, comments, and likes.

Conclusions: Our investigation of cancer-related communication on Weibo provides valuable insights into public concerns about cancer and can help guide the development of health campaigns in social media.

Citing Articles

Improving topic modeling performance on social media through semantic relationships within biomedical terminology.

Xin Y, Grabowska M, Gangireddy S, Krantz M, Kerchberger V, Dickson A PLoS One. 2025; 20(2):e0318702.

PMID: 39982945 PMC: 11845042. DOI: 10.1371/journal.pone.0318702.

Jeong W, Song E, Jeong E, Oh K, Lee H, Jun J Healthc Inform Res. 2024; 30(4):398-408.

PMID: 39551926 PMC: 11570664. DOI: 10.4258/hir.2024.30.4.398.

Topic modeling and content analysis of people's anxiety-related concerns raised on a computer-mediated health platform.

Liu Y, Shan Y, Sun S, Ji M, Zhou S, You Y Sci Rep. 2024; 14(1):27520.

PMID: 39528679 PMC: 11555215. DOI: 10.1038/s41598-024-79164-x.

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.

Da C, Duan Y, Ji Z, Chen J, Xia H, Weng Y Support Care Cancer. 2024; 32(5):314.

PMID: 38683417 DOI: 10.1007/s00520-024-08513-3.

Empowerment-Led Guided Self-Help Intervention for Symptom Burden in Breast Cancer Women Treated With Ovarian Function Suppression: A Randomized Trial Protocol.

Li Y, Chen Y, Wang S, Dai Z, Cui J, Xing Y World J Oncol. 2024; 15(2):325-336.

PMID: 38545479 PMC: 10965257. DOI: 10.14740/wjon1817.

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