Research on the theme clustering and evolution of public opinion on the"4.29"particularly major house collapse incident
To mitigate the impact of public opinion during building collapse emergencies,this paper provides emergency authorities with practical guidance for regulating and guiding public opinion using the theme clustering and evolution research model.Focusing on the"4.29"particularly major house collapse incident in Changsha,a Python crawler collected netizen comments on Sina Weibo within 8 days of the incident.The Term Frequency-Inverse Document Frequency(TF-IDF)algorithm is used to extract keywords,determine co-occurrence frequency,and describe the co-occurrence network relationship.Public opinion topics are analyzed through cluster analysis using the coupling confusion degree and K-Means clustering algorithm.The applied analysis method identifies correlations among public opinion themes,explores their changing trends over time,and reveals the evolution law of public opinion themes during the house collapse event.The results suggest a divided public opinion on the collapse event,with both an aggregation effect and a series effect.The seven themes of public opinion that are clustered can be grouped into three primary themes.T1 is dedicated to rescue and rehabilitation,T2 to investigation and inquiry,and T3 to security and prevention.These themes allow for speculation about the incident's cause and suggest potential management strategies.Using rumor propagation and official press conferences as key points,the evolution of public opinion can be divided into early,middle,and late stages,each with distinctive characteristics.During the initial stage,T1 is dominant,followed by T2 and T3 in the middle,and T1 once again towards the end.An analysis of public opinion dynamics during chemical explosions indicates that T1,T2,and T3 are crucial factors in their evolution.It is crucial to meet the public's information requirements concerning these topics for stable public opinion.On the contrary,it is easy to create mistrust in officials and lead to the spread of widespread rumors,which presents greater challenges in managing public opinion.
safe social engineeringbuilding collapsenetwork public opinion analysisclusteringevolution law
NETL
NSTL
万方数据
