Research On The Characteristics Of Information Dissemination Based On Propensity Index

The rapid development of the Internet has laid the foundation for the rise of online social networks.The popularity of smart devices has brought the internet into millions of homes.The flourishing of online social networks has brought people closer to each other,making information spread faster,on a larger scale and with greater influence;while providing convenience and enriching daily life,online social networks have also created some problems and drawbacks.For example,online rumours,the spreading of false information and malicious human flesh have caused some mental distress and personal injury to many online users.Therefore,controlling the spread of undesirable information,promoting the spread of positive information and exploring the inner mechanism and laws of information dissemination have become urgent issues to be solved.Therefore,this thesis studies the mechanism and laws of information dissemination from the perspective of Internet users themselves,and the research mainly includes the following two aspects:firstly,the propensity index is proposed,and some attributes of Internet users themselves are abstracted into the propensity index to study information dissemination;secondly,the SI_SI_OI_Ninformation dissemination model based on the propensity index is constructed to simulate the process of information dissemination.The model is validated through simulation experiments and combined with real data to verify the validity and feasibility of the model.The details of the study are as follows:Firstly,the influence of individual propensity indices on information dissemination is studied.In this thesis,different levels of tendency indices are assigned to individuals to quantify their interest,cognitive attitude and willingness to disseminate information,so as to describe information dissemination in reality more realistically.In the process of information dissemination,the propensity index of a node is affected by the influence of a single neighbouring node and the influence of a group of neighbouring nodes,and the influence of neighbouring nodes of different levels(star nodes and ordinary nodes)will be different;grouping neighbouring nodes with the same attitude and calculating the influence of each group separately,the group of neighbouring nodes with more influence tends to have more influence on the propensity index of nodes in the network influence.It was found that different levels of propensity indices would have different effects on the dissemination of information,and the larger the propensity index of an individual,the more rapidly the information tended to spread.In the experimental study of different network conditions,it was found that under the same network size and initial infection density,the larger the network average degree,the stronger the connections between nodes,the larger the propensity index of nodes,and the more frequent the dissemination of information;under the same network average degree and initial infection density,expanding the network size has less impact on information dissemination in networks with the same tier of propensity index.The experimental results validate the correctness of our proposed propensity index-based propagation idea and the propensity index formula.Secondly,this thesis investigated the influence of the types of Internet users’speech on information dissemination in online social networks.Based on the analysis of the comments of Internet users on hot events in current social networks,the new SI_SI_OI_Ninformation dissemination model was constructed by classifying the communication status Internet users and introducing the propensity index on the basis of the classical SI model,in response to the shortcomings of the existing model which is too simple in the analysis of Internet users’views in the process of public opinion dissemination.The new model abstracts Internet users’interest in hot events and attitudes into a propensity index,which more realistically portrays the influence of Internet users’own factors on the spread of events.By classifying the communication statuses of Internet users,it fully reflects the characteristics of divergent opinions and conflicting views prevalent in the process of information dissemination of real events.The experimental results show that under the condition of equal assignment ratio,the simulation experiment results fit well with the actual event spreading situation,the spreading pattern is consistent,and the proportion of reaching the peak of infection density is also the same,further verifying the reliability of the proposed model.Through the analysis of the node tendency index,whether it is a negative or positive event,any event that can produce a great impact on the hearts of Internet users and make them angry will often inspire them to spread the event,making the event have a larger scale of spread and higher heat.