Cancer-prevention messages on Chinese social media: A content analysis grounded in the extended parallel process model and attribution theory

Jingyuan SHI, Xiaohui WANG, Tai Quan Peng, Liang Chen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

To elucidate the Chinese public's awareness of cancer and its possible prevention, we investigated cancer-prevention messages presented on Weibo, a Twitter-like Chinese social media platform, with reference to the extended parallel process model (EPPM) and attribution theory. With a sample of 16,654 cancer-related messages, we analyzed whether the messages acknowledged cancer's threat, indicated collective or individual efficacy in preventing cancer, and attributed cancer to known causes. Results revealed that 4,545 of the messages (27.3%) mentioned cancer prevention, 127 (2.8%) described the severity of the threat of cancer, and 1,622 (35.7%) emphasized people's susceptibility to cancer. Relative to messages indicating collective efficacy in cancer prevention (n = 523, 11.5%) and environmental causes of cancer (n = 34, 0.75%), messages indicating individual efficacy (n = 3,647, 79.8%) and individual causes (n = 1,505, 33.3%) were far more prevalent. Our findings illuminate Chinese people's beliefs about cancer prevention as well as indicate potential effects of social media messages on individuals' cancerprevention beliefs and behaviors according to the premises of the EPPM and attribution theory. In closing, we discuss what our findings imply for theory construction and cancerprevention campaigns.

Original languageEnglish
Pages (from-to)1959-1976
Number of pages18
JournalInternational Journal of Communication
Volume13
Publication statusPublished - 2019

Scopus Subject Areas

  • Communication

User-Defined Keywords

  • Attribution theory
  • Cancer-prevention messages
  • Extended parallel process model
  • Social media
  • Weibo

Fingerprint

Dive into the research topics of 'Cancer-prevention messages on Chinese social media: A content analysis grounded in the extended parallel process model and attribution theory'. Together they form a unique fingerprint.

Cite this