Research On Information Association And Its Visualization Based On Social Platform

With the development of the Internet,various types of social platforms appear and play an indispensable role in people's work and life.More and more people choose to move more daily or work exchanges to social platforms.People publish ideas or share information on social platforms,and the sheer volume of data generated by these social behaviors makes social networks a vehicle for personal information and feelings.This article divides the data generated by the social platform into two levels,one is the social group level and the other is the social individual level.Social individuals constitute a social group,that is,social group data is composed of social individual data.Therefore,this paper focuses on the analysis of the relevancy between the two levels of data and on the visualization of social group data.For the association analysis based on social colony data,since the association between users can be mapped to the similarity between users,this article uses the social network topology formed by the group data of the social platform to determine the similarity between the users of the social platform to This is a correlation analysis.Specifically,we analyze the relevancy of social group data from two aspects.First,we analyze the similarity of users based on social content,that is,through the analysis of social data of social users,we can establish social user model and determine user based on user model The second is based on the social network topology user similarity analysis,that is,through the user's social relationship data to establish social network topology model,and social network structure from the aspect of the user's similarity analysis.In the group data analysis,the visual analysis method is used to display the social network topology.The work involves optimizing the d3-force force placement algorithm based on the FR layout algorithm to obtain better visual effects.For the correlation analysis based on social individual data,the analysis of social individual data is also an important research content because social individual data is a unit that forms social group data.To some extent,the social individual data can reflect the individual characteristic information of the user.Experiments show that associating the individual data of the user with the latent characteristics of the individual personality is a feasible method for determining the personality characteristics of the user.This paper correlates the data generated by social users with the user's personality tendencies,extracts and scans the eigenvalues,and establishes the personality predisposition prediction model by machine learning method.Experiments show that the extraction and screening of user personality eigenvalues have significantly improved the accuracy of the prediction results,and there is a close relationship between users' social features and user personality characteristics.