Social Media Coverage of Public Health Issues in China: A Content Analysis of Weibo News Posts

Abstract

Content analysis is a useful tool for better understanding how different portrays of public health issues may affect news dissemination. In this research, we conducted a content analysis on health-related messages published by opinionleading news outlets. We examined generic content attributes, including number of words, sentences, images, links, and published time, as well as content valence features. Our findings show that there are no significant differences in the amount words, sentences and hyperlinks used between the highly forwarded and lowly forwarded news posts when covering a public health issue. Whereas the topic and title length as well as content valence of highly forwarded new posts is different from the lowly forwarded ones. We also discuss ways to improve popularity of health-related news.

Prediction of the Dissemination of Health News on Microblogging Sites Based on Ample Feature Selection and Support Vector Machine

“…The hour the post was published (-inf-9], (9-12], (12)(13)(14)(15), (15)(16)(17)(18)(19), (19-inf) X 9…”

“…In total, 863 microblog posts were collected, all of which related to public health issues. Details on the dataset can be found in the previous research of Pei [6,12].…”

“…where, is the dual form of . Then, the values of w, ε, and can be determined by finding out a solution to formulas (10)~ (12). In the SVM, most of the are zeros, an evidence of the sparseness of the algorithm.…”