Research On Information Transmission Model And Trend Prediction In Social Networks

With the development of network technology,the social network platform has broken the inherent information transmission mode and quickly become a key channel for people to obtain external information in their daily life.Users are free to express their views,receive information,disseminate information,or interact with others.The behavior of users to publish original posts generates basic data information,which is an important part of social network information.Users' forwarding behavior makes this basic data information spread effectively in social networks.Thumb up and comments on original posts increase the information load of original posts,which can effectively enhance the influence of original posts.The information content of an event can have a huge impact on the whole society with the spread of social network platforms.Disseminating positive information is positive for the whole society.Spreading negative and negative information will easily lead to panic and uneasiness in the society and bring about serious adverse effects on the society.Therefore,it is necessary to conduct a comprehensive and in-depth study on the laws of information transmission in social networks.This paper makes a systematic analysis of the communication trend by deeply exploring the rules of user behavior and the characteristics of posts in social network,establishing the information communication model in social network.The main research content of this paper is divided into four parts: data collection of social network platform information;Analysis of user behavior and Posting characteristics in social network;Establishment of node influence evaluation algorithm in social network;The establishment of information communication model in social network.The research content of the four parts runs through the research process of the whole article.First of all,data collection on social network platforms is carried out,and data sets are statistically analyzed using statistical methods.From the aspects of time feature,post type feature and interaction quantity feature,the user behavior and post character in social network are systematically analyzed.Secondly,the obtained key information is normalized to calculate the weight of each influence component factor of nodes in the social network.The PCA algorithm is used to establish the influence evaluation algorithm of nodes in social network and realize the purpose of calculating the influence of nodes quantitatively.Secondly,in combination with the infectious disease transmission model,the classical model is compared with the information transmission model,the state transition process of each node is improved,and the seir-pro model in line with the social network structure is established.Comprehensive the above results,to analyze the trend of social network information transmission system,compares the different conditions,the speed and effect of information dissemination,got the key nodes in the process of information transmission,realize the comprehensive study of information dissemination trend.Won a large number of social network information data,analyzing the characteristics of user behavior and posts the key rule,set up a social impact assessment of each node algorithm,finally achieved the establishment of a social network information transmission model.The key nodes of information communication in social network are deeply studied and the core strategy of information communication control is given.