Analysis And Evaluation For The Credibility Of The Content In Social Medium

For the past few years,the rapid development of social medium expands the ways of getting information for people.People can get information from social medium anywhere anytime.However,when social medium brings us lots of benefits,it also promotes the spreading of fake information and cause serious side effect.So helping users to discover fake information becomes vital important.Compared with traditional medium,the user generating content and openness of social media makes the information in social media more confounding,the dissemination network more complex and brings new challenges to judge content's credibility.To solve the problem,existed researches ignored user's topic factor,lacking consideration of user's find-grained credibility,what's more,they also ignored the disturbance of herd mentality.This thesis will consider both sides to evaluate the credibility of content in social medium.The main research work are below:Firstly,create a model to evaluate the credibility of content in social media.Based on the public dataset of Sina microblog,analyze the trend of topic and herd behavior and then treat user's behavior of posting or reposting content as voting.Use the idea of generative model and Bayesian network to establish a model for the generation of a vote with user's topic factor,herd mentality factor and the credibility of content.Secondly,solve the model.On one hand,using the idea of Gibbs sampling,infer the joint probability distribution and transition probability distribution of all latent variables to achieve the sampling rules for latent variables.On the other hand,use the relationship between prior distribution and likelihood function to estimate the distribution of content's credibility,user's topic and herd behavior.Finally,design experiments to verify our model.Use our model to analyze the credibility of content in the dataset and how topic factor and herd mentality factor influence the judgment of content's credibility.Verify our model's effect through comparing to existed models and the result shows that our model is more effective.