Research On Prediction Method Of Weibo Popularity Based On Social Network

With the rapid development of the Internet in China,social networking platforms such as Weibo have become an indispensable part of our daily life and an important carrier for news and topic communication.Because Weibo is characterized by suddenness and unpredictability,prediction of Weibo popularity is conducive to the government's monitoring of public opinions and the early warning of public opinion events.Therefore,it is of great research value and significance to use social data to predict the popularity of Weibo in advance.On the basis of summarizing the research status and sorting out relevant theories,we study the prediction technology of Weibo,and puts forward the prediction model of Weibo popularity based on meta-learning and the prediction model of topic popularity based on influence.In the research of popularity prediction in Weibo,we firstly establish emotional characteristics by Bi-LSTM,establish correct results algorithm,improve the accuracy of sentiment classification,and construct multidimensional features based on user information and Weibo information,and designed the neural network based on meta-learning,optimize the training time,finally realize the popularity prediction of forwards,comments and likes of a small number of new samples of Weibo.Secondly,in the research of prediction of topic popularity,k-means algorithm is adopted in this thesis to cluster the themed Weibo texts after LDA,and the influence model is established based on principal component analysis,so as to redefine the popularity calculation method of topic and realize the topic popularity prediction after clustering.The meta-learning-based prediction model of Weibo popularity proposed in this thesis learned parameters with high generalization ability through quadratic gradient descent of historical samples of Weibo during the meta-training phase,optimize the training process and solved the problem of slow learning due to the differences of new samples.We comprehensively consider the correlation factors between users and Weibo and the past information,establishes the influence model based on principal component analysis,and realizes the definition of Weibo popularity.At the end of this thesis,the effectiveness of the proposed prediction model of Weibo and topic popularity is verified through comparative experiments.The experiment shows that the prediction model of Weibo popularity can solve the problem of slow learning convergence of specific users and improve the prediction accuracy in the case of a small number of new samples.The topic popularity prediction model solves the problem that popularity of Weibo and topic are not objective due to the diversity of Weibo users.