Research On Key Technologies Of Microblog Rumor Detection

With the popularity of microblog social media represented by Twitter and Sina Weibo,the authenticity of information on social networks has become more and more serious.The spread of online rumors will not only cause people to panic,but also affect the safety of microblog social media.Therefore,the research of automatic rumor detection for microblog social media is attracting more and more researchers' attention.This article uses the microblog social media platform Twitter as the application background to deeply study the problem of rumor detection.Through research,it is found that most of the traditional feature-based methods are to extract features from false information,the author of the message and the statistical information of the response and perform rumor detection.However,these methods largely ignore the structural information related to message propagation,resulting in high time complexity and low accuracy of rumor detection.Therefore,this paper carried out the key technology research of microblog rumor detection based on the propagation tree structure,and researched the rumor detection method with higher accuracy and efficiency.According to the position characteristics,this paper proposes an optimization algorithm of rumor propagation tree structure based on the same position.The nodes with the same position are merged into super nodes by the way of breadth first,depth first or the combination of breadth first and depth first,so as to reduce the number and height of branches in the propagation tree structure.This algorithm is combined with the top-down recurrent neural network model based on the tree structure.This algorithm solves the problem that the complexity of the propagation tree structure in the existing propagation tree-based rumor detection method leads to the low performance of large-scale rumor detection.Experimental results show that the proposed rumor propagation tree structure optimization algorithm based on the same position improves the performance of rumor detection on the premise of ensuring the accuracy of rumor detection.This paper proposes a gated recurrent unit optimization algorithm based on propagation tree,which reduces the loss of feature information during feature extraction by calculating the feature vector representation of each node.This algorithm solves the problem that when the existing rumor detection method based on propagation tree uses the gated recurrent unit to simulate the interaction process of nodes with large height difference in the propagation tree,the important feature information of the node is lost in the feature extraction process.In addition,this paper also proposes a path selection algorithm based on the propagation tree,which solves the problem of increased calculation time complexity of the gated recursive unit optimization algorithm due to the increase of feature information.The experimental results show that the gated recurrent unit optimization algorithm combined with the path selection algorithm proposed in this paper has greatly improved the accuracy and performance of rumor detection.