A Prediction Model for the Effectiveness of Public Opinion Dissemination about Accident Di-sasters Based on Information Portrait
[Purpose/significance]To accurately portray public opinion information on accident disasters,to realize the early classifica-tion and identification of highly disseminated information,and to make precise guidance measures.[Method/process]Taking the micro-blogging data of the self-built house collapse in Changsha as an example,we firstly use the entropy weight method to evaluate the in-formation dissemination effect,secondly,use K-Modes clustering to construct an information portrait of the highly disseminated infor-mation and finally build a classification prediction model based on the XGBoost algorithm and compare the prediction effect of differ-ent models.[Result/conclusion]Based on the information portrait,we can classify public opinion information on accident disasters into five categories:"highly disseminated-official accident rescue information","highly disseminated-official accident penalty informa-tion","highly disseminated-self-media emotional information","highly disseminated-official accident loss information"and"lowly disseminated information."Meanwhile,the XGBoost algorithm has the best prediction performance compared with other algorithms,with an accuracy rate of 93.94％.[Innovation/limitation]We propose a method for predicting the effect of online public opinion infor-mation dissemination based on portraits to realize the problem of accurate prediction of public opinion information on accident disas-ters;we will add multiple public opinion events as datasets and combine them with deep learning algorithms to further improve the model effect.
accident disastersinformation dissemination effectinformation portraitprediction modelonline public opinion
NETL
NSTL
万方数据
