Factors Associated With Influential Health-Promoting Messages on Social Media: Content Analysis of Sina Weibo

Qingmao Rao; Zuyue Zhang; Yalan Lv; Yong Zhao; Li Bai; Xiaorong Hou

doi:10.2196/20558

Factors Associated With Influential Health-Promoting Messages on Social Media: Content Analysis of Sina Weibo

JMIR Med Inform. 2020 Oct 9;8(10):e20558. doi: 10.2196/20558.

Authors

Qingmao Rao^{1

2}, Zuyue Zhang^{1

2}, Yalan Lv¹, Yong Zhao³, Li Bai⁴, Xiaorong Hou^{1

2}

Affiliations

¹ College of Medical Informatics, Chongqing Medical University, Chongqing, China.
² Medical Data Science Academy, Chongqing Medical University, Chongqing, China.
³ School of Public Health and Management, Chongqing Medical University, Chongqing, China.
⁴ Hospital of Zigong Mental Health Central, Zigong, China.

PMID: 33034569
PMCID: PMC7584979
DOI: 10.2196/20558

Abstract

Background: Social media is a powerful tool for the dissemination of health messages. However, few studies have focused on the factors that improve the influence of health messages on social media.

Objective: To explore the influence of goal-framing effects, information organizing, and the use of pictures or videos in health-promoting messages, we conducted a case study of Sina Weibo, a popular social media platform in China.

Methods: Literature review and expert discussion were used to determine the health themes of childhood obesity, smoking, and cancer. Web crawler technology was employed to capture data on health-promoting messages. We used the number of retweets, comments, and likes to evaluate the influence of a message. Statistical analysis was then conducted after manual coding. Specifically, binary logistic regression was used for the data analyses.

Results: We crawled 20,799 Sina Weibo messages and selected 389 health-promoting messages for this study. Results indicated that the use of gain-framed messages could improve the influence of messages regarding childhood obesity (P<.001), smoking (P=.03), and cancer (P<.001). Statistical expressions could improve the influence of messages about childhood obesity (P=.02), smoking (P=.002), and cancer (P<.001). However, the use of videos significantly improved the influence of health-promoting messages only for the smoking-related messages (P=.009).

Conclusions: The findings suggested that gain-framed messages and statistical expressions can be successful strategies to improve the influence of messages. Moreover, appropriate pictures and videos should be added as much as possible when generating health-promoting messages.

Keywords: Sina Weibo; framing effects; health communication; health-promoting messages; influence; social media.

©Qingmao Rao, Zuyue Zhang, Yalan Lv, Yong Zhao, Li Bai, Xiaorong Hou. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 09.10.2020.