摘要：

Abstract:

【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