Research On Internet Rumor Detection Of Public Health Emergencies

In this era of information explosion and Internet technology booming,people can get all kinds of information from different channels.Smart phones,iPad and other electronic products make the information exchange between people easier.People don’t need to stay in front of the computer to browse and upload all kinds of information.In this network environment,the real world and the virtual world have been intertwined,sometimes people can not distinguish.There are also various social media platforms,which provide people with a platform for information exchange and sharing.At the same time of information exchange,there are also a large number of rumors,especially the Internet rumors in public health emergencies,which are related to everyone’s life,affect the stability and harmony of the whole society,and even affect the global environment.Therefore,how to effectively realize the detection and recognition of network rumors has become a hot spot in the research neighborhood of network rumors.In the research and analysis of the relevant literature on rumor detection of the predecessors,I learned that the research on rumor detection is mainly based on the rumor identification of a social media platform,but there are obvious differences in the content of rumors,such as rumors about political events,entertainment events,and economic events.The research works on rumors under different events are limited,so this paper proposes to take the Internet rumors in public health emergencies as the research object.In terms of detection methods,the traditional network rumor detection mainly adopts artificial recognition method and machine learning classification method.The focus of machine learning method is to manually select rumor features and construct feature engineering.The network model based on deep learning can realize the deep feature extraction.This article proposes a method based on deep learning to detect public health emergencies.The main research work is to build a rumor detection model based on CNN BLSTM fusion using the collected Internet rumor data in public health emergencies.The specific contents are as follows:Firstly,by collecting relevant research literature on rumors,detection or identification of online rumors,online rumors about public health emergencies and related deep learning detection methods.Secondly,it defines the basic meaning of rumors,Internet rumors and public health emergencies,and introduces the characteristics of Internet rumors and the detection methods of Internet rumors in public health emergencies.Thirdly,it analyzes the conventional model of online rumor detection for public health emergencies,and puts forward the detection model constructed in this article with its shortcomings.After preprocessing the rumor data,we extract and learn the features of online rumor text through CNN network and bilstm network,and automatically discover all kinds of feature information in the text data,so as to achieve the purpose of online rumor detection of public health emergencies.Finally,through comparative experiments,the validity and accuracy of the network rumor detection model designed in this article for public health emergencies is verified.