Research And Implementation Of Propagation Model Of Information On Social Network Based On Behavior Characteristics Of Users

Nowadays social networks is an important network platform for people to obtain information.The ihformation on it is floding.Some of them are good,but some of them are rumors.So it is necessary to study the rule of information spreading over social network.These rules will help us to make better use of social networks,and also conducive to stop rumors early and guide public opinion.The main work of this paper is as follows:1.Present a model of calculating reading probability.In social networks,users often only browse information on their home pages.So in a time peroid,users have different probabilitise of reading one information.Based on statistical learning method,we take into account user's behavior characteristics how they use social networks,and use two functions to simulate the probability of user.2.Propose a propagation model of information on social network,which is based on reading probability and forwarding discriminant.The information is possible to be forwarded after users have read it.Combined with the reading probability model and based on the analysis of existing forwarding discriminant method,we propose the RTP model.In the forwarding discriminant method,first we take into account user's behavior characteristics concerned with forwarding behavior.Then we use the existing classification algorithm to calculate result.Finally,the experiment shows that the model is reasonable.3.Design and implement a system to forecast the dissemination scale of microblog.We implement data storage,data analysis,reading probability,forwarding discriminant and display interface modules.When a user input all needed information,our system can predict the spread scale of the microblog.