%0 Journal Article
%@ 2291-5222
%I JMIR Publications
%V 7
%N 6
%P e13987
%T Using the Extended Parallel Process Model to Examine the Nature and Impact of Breast Cancer Prevention Information on Mobile-Based Social Media: Content Analysis
%A Chen,Liang
%A Yang,Xiaodong
%A Fu,Lunrui
%A Liu,Xiaoming
%A Yuan,Congyi
%+ School of Journalism and Communication, Shandong University, No.57 Shanda South Road, Jinan, 250100, China, 86 531 88361159, XYANG012@e.ntu.edu.sg
%K breast cancer
%K prevention information
%K mobile social media
%K EPPM
%D 2019
%7 24.06.2019
%9 Original Paper
%J JMIR Mhealth Uhealth
%G English
%X Background: With the rise of mobile technology, an increasing number of people use mobile-based social media to access health information. Many scholars have explored the nature of health information on social media; however, the impact of such information on people was understudied. Objective: This study aimed to examine the nature and impact of health information on mobile-based social media. Specifically, we investigated how the levels of threat and efficacy of breast cancer prevention information affect individuals’ engagement with the information, such as readings and likes. Methods: Breast cancer prevention articles posted on a Chinese mobile-based social media platform (ie, WeChat Subscription Account [WeChat SA]) from January 1 to December 31, 2017, were extracted using the Python Web Crawler. We used content analysis and analysis of covariance to analyze our data. Results: The results revealed that the vast majority of titles and main bodies of the articles involved one of the extended parallel process model components: threat or efficacy. Conclusions: Breast cancer prevention information on WeChat SA was well designed. Both threat and efficacy significantly affected the number of readings, whereas only efficacy had a significant effect on the number of likes. Moreover, breast cancer prevention information that contained both high levels of threat and efficacy gained the largest number of readings and likes. 
%M 31237239
%R 10.2196/13987
%U http://mhealth.jmir.org/2019/6/e13987/
%U https://doi.org/10.2196/13987
%U http://www.ncbi.nlm.nih.gov/pubmed/31237239