With the development of Internet technology,WeChat public platform,as a new social media,has gradually become the main media carrier of public opinions in colleges and universities.The comprehensive communication mode and characteristics are of great significance.Therefore,this study selects the hot spots of public opinions in colleges and universities on WeChat public platform in 2016-2018 as the research object.This study used big data and web crawler technology to collect the reports on public opinion events in colleges and universities on WeChat public platform during 2016-2018,and selected the hot public opinion reports with a reading amount of more than 100,000 times.The research mainly analyzes the main modes and characteristics of hot spots of public opinion communication in universities on the WeChat public platform,and puts forward reasonable strategies for public opinion guidance.The analysis found that the initial communication mode of hot public opinion on WeChat public platform was in the form of "multi-point radiation",the middle stage was dominated by the wavy trend,and the late stage of public opinion communication gradually formed the field of "segmented" communication.As the subjects of agenda setting in WeChat public platform are increasingly diversified,individuals or we media teams can influence the development of public opinion by setting agendas,leading to increased emotional expression in various stages of public opinion communication in colleges and universities,and weakened effect of setting agendas for official accounts of authoritative media.Study in colleges and universities and government agencies to WeChat public platform of the public opinion guide work defects and deficiencies,this research combines WeChat propagation characteristics of the university public opinion in the public platform,from the early warning,guide public opinion management and user’s moral accomplishment and so on proposed the optimization strategy,need to colleges and universities with relevant government departments to use big data technology construction of early warning process more perfect. |