Credit Evaluation System For Online Social Network Users

Credit is the cornerstone of maintaining economic and social order in modern society.In Internet era,online credit also plays a vital role.In recent years,online social networks have developed into an important channel for people to obtain information,disseminate information,make friends and entertain.However,there are a lot of deceitful behaviors or acts of dishonesty on online social networks,which will affect people's daily life and even social order and national stability.Therefore,the credit evaluation for online social network users is the top priority.However,most of the existing researches on credit evaluation focus on traditional offline credit evaluation based on commercial bank credit evaluation,professional credit evaluation and college student credit evaluation,and online credit evaluation based on ecommerce credit evaluation and internet financial credit evaluation.There are few studies on online social network user credit evaluation,which is still not systematic.Aiming at this problem,this study takes Sina Weibo as an example to study the credit evaluation for online social network users,and constructs a credit evaluation index system.Based on the three-dimensional credit theory as the main theoretical basis,this paper expands the connotation of integrity,compliance and performance according to the user behaviors,and then chooses the credit evaluation index and puts forward the quantitative method of the index.AHP based on clustering idea is adopted and twenty experts in related fields are invited to determine the relative importance of two indicators at each level when calculating the weights of indicators.According to the clustering results of expert opinions,the consensus degree among experts and the judgment consistency of each expert are comprehensively considered to assign different weights to each expert,and the final weights of each indicator are obtained.Considering the practical application of the evaluation index system,this paper proposes two system adjustment methods to ensure the robustness of the evaluation index system.One way is to discard the index corresponding to the behavior that doesn`t not happen and enlarge the weight of the index at the same level in proportion.Another way is to use the fuzzy c-means algorithm to predict and fill the corresponding index values of the unhappened behaviors.Finally,the fuzzy comprehensive evaluation method is employed to evaluate the behaviors of typical Weibo users,and Matlab is used to simulate the second method of system adjustment,that is,the index numerical prediction filling based on the fuzzy C-means algorithm,and the simulation result is good.This study not only provides a new perspective for online social network credit evaluation,but also expands the related research on personal credit and enriches the connotation of threedimensional credit theory in the practical application situation.The research attempts to integrate the data of social network behaviors in order to build,evaluate and study the personal credit index system,and therefore constitutes an important expansion and supplement to the study of personal credit in the "Internet + Big Data" environment.This paper has certain guiding significance for the development of many network applications and research results can be used to supplement the traditional offline real world credit,be applied to more life fields related to credit,and contribute to the ecological governance of Internet credit.