Analysis and Prediction of Factors Influencing Online Public Opinion Trends in Sudden Incidents
This study focuses on the Weibo platform to investigate the dissemination of online public opinion during sudden incidents.It aims to identify and quantify key factors influencing opinion trends and develop an accurate prediction model using machine learning techniques.A total of 27 422 related Weibo posts were collected through web crawling,and multidimensional features such as content characteristics and user interaction behaviors were extracted.Clustering algorithms were applied to classify the content of the posts,and the random forest algorithm was used to predict the number of reposts.The results indicate that comment count and like count are the most significant factors influencing public opinion dissemination.The prediction model achieved an accuracy of 87％,providing a scientific basis for optimizing public opinion response strategies.
NETL
NSTL
万方数据
