Computer Science ›› 2020, Vol. 47 ›› Issue (1): 96-101.doi: 10.11896/jsjkx.181202253

• Database & Big Data & Data Science • Previous Articles     Next Articles

Method of Weibo User Influence Calculation Integrating Users’ Own Factors and Interaction Behavior

WANG Xin-sheng,MA Shu-zhang   

  1. (Department of Computer Science and Communication Engineering,Jiangsu University,Zhenjiang,Jiangsu 212013,China)
  • Received:2018-12-03 Published:2020-01-19
  • About author:WANG Xin-sheng,born in 1972,Ph.D,associate professor,is member of China Computer Federation(CCF).His main research interests include wireless sensor network and social network.
  • Supported by:
    This work was supported by the China Postdoctoral Science Foundation (2015M571688) and Research Fund for Advanced Technical Talents of Jiangsu University (12JDG104).

Abstract: Weibo users with high-impact play an important role in commodity marketing and social publicity guidance,so mining high-impact users becomes a hot research issue in Weibo social networks.As for the problems of incomplete behavior analysis of interaction behavior and user’s own factors in calculation of micro-blog user influence,the micro-blog user influence based on user’s self-factors and interactiont computing model was proposed.This method considers the direct influence and indirect influe-nce of Weibo users.In the user’s direct influence calculation phase,the initial influence of the user is calculated by analyzing the user’s own factors such as the number of fans of Weibo users,user activity,and recent microblog quality.Then the user interaction behavior is analyzed,such as the user’s microblog visibility rate,microblog user interaction coefficient,so as to calculate the user communication ability.Finally,by combining the initial influence with the user communication ability,the user’s direct influe-nce is cakulated based on the improved PageRank algorithm.In the calculation of user indirect influence phase,through the analysis of the connection structure of the user network diagram and according to the different connection paths of non-adjacent users,the indirect impact of the user is divided into three categories:simple path,repeated path and complex path,then the user indirect influence is calculated.The experimental results show that the proposed algorithm is 14.8% and 8.3% higher than the PageRank algorithm and the MR-UIRank algorithm in terms of the user ranking accuracy.

Key words: Direct influence, Indirect influence, Interaction behavior, Microblog, PageRank, Self-factor

CLC Number: 

  • TP393
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