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

CLC Number: 

  • TP393
[1]BINGOL K,ERAVCI B,ETEMOGLU C O,et al.Topic-based influence computation in social networks under resource constraints[J].IEEE Transactions on Services Computing,2016,12(6):970-986.
[2]LIU Q,XIANG B,YUAN N J,et al.An influence propagation view of pagerank[J].ACM Transactions on Knowledge Discovery from Data (TKDD),2017,11(3):1-30.
[3]PENG S,YANG A,CAO L,et al.Social influence modeling using information theory in mobile social networks[J].Information Sciences,2017,379:146-159.
[4]WENG J,LIM E P,JIANG J,et al.Twitterrank:finding topic-sensitive influential twitterers[C]∥Proceedings of the Third ACM International Conference on Web Search and Data Mining.ACM,2010:261-270.
[5]SUN H,ZUO T.Research and Realization of Influence Optimization in Cloud Computing Environment[J].Journal of Chinese Computer Systems,2018,39(1):42-47.
[6]PAGE L,BRIN S,MOTWANI R,et al.The PageRank citation ranking:Bringing order to the Web[J].Stanford Digital Lib-raries Working Paper,1998,9(1):1-14.
[7]YAMAGUCHI Y,TAKAHASHI T,AMAGASA T,et al. Turank:Twitter user ranking based on user-tweet graph analysis[C]∥International Conference on Web Information Systems Engineering.Springer,Berlin,Heidelberg,2010:240-253.
[8]MEEYOUNG C,HAMED H,FABRICIO B,et al.Measuring user influence in twitter:The million follower fallacy[C]∥Fourth International AAAI Conference on Weblogs and Social Media.Menlo Park:AAAIPress,2010:10-17.
[9]LI G L,CHU Y P,FENG J H,et al.Influence maximization on multiple social networks [J].Chinese Journal of Computers,2016,39:643-656.
[10]LIU L,TANG J,HAN J,et al.Mining topic-level influence in heterogeneous networks[C]∥Proceedings of the 19th ACM International Conference on Information and Knowledge Management.ACM,2010:199-208.
[11]MAO J X,LIU Y Q,ZHANG M,et al.Social influence analysis for micro-blog user based on user behavior[J].Chinese Journal of Computers,2014,37(4):791-798.
[12]ZHOU J,ZHANG Y,WANG B,et al.Predicting user influence in microblogs[C]∥2016 First IEEE International Conference on Computer Communication and the Internet (ICCCI).IEEE,2016:292-295.
[13]WU L,YANG B,JIAN M,et al.MPPR:Multi Perspective Page Rank for User Influence Estimation[C]∥2018 IEEE International Conference on Big Data and Smart Computing (BigComp).IEEE,2018:25-29.
[14]BAKSHY E,HOFMAN J M,MASON W A,et al.Everyone’s an influencer:quantifying influence on twitter[C]∥Proceedings of the Fourth ACM International Conference on Web Search and Data Mining.ACM,2011:65-74.
[15]LI C,XIONG F.Social recommendation with multiple influence from direct user interactions[J].IEEE Access,2017,5:16288-16296.
[16]YANG L U,HUAKANG L I,GUOZI S.Distributed microblog crawler system based on P2P[J].Journal of Jiangsu University,2016,37(3):296-301.
[17]WEN Z,KVETON B,VALKO M,et al.Online influence maximization under independent cascade model with semi-bandit feedback[C]∥Advances in Neural Information Processing Systems.2017:3022-3032.
[18]KEMPE D,KLEINBERG J,TARDOS É.Maximizing the spread of influence through a social network[C]∥Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2003:137-146.
[1] LIU Xiao-jie, LV Xiao-qiang, WANG Xiao-ling, ZHANG Wei, ZHAO An. Mining User Interests on Twitter Using Wikipedia Category Graph [J]. Computer Science, 2019, 46(9): 79-84.
[2] CHEN Zhi-xiong, WANG Shi-hui and GAO Rong. Recognition Model of Microblog Opinion Leaders Based on Sentiment Orientation Analysis [J]. Computer Science, 2018, 45(5): 168-175.
[3] WANG Rong-bing, AN Wei-kai, FENG Yong and XU Hong-yan. Important Micro-blog User Recommendation Algorithm Based on Label and PageRank [J]. Computer Science, 2018, 45(2): 276-279.
[4] XIA Chong-huan, LI Hua-kang, SUN Guo-zi. Microblogging Malicious User Identification Based on Behavior Characteristic Analysis [J]. Computer Science, 2018, 45(12): 111-116.
[5] CHANG Jia-wei, DAI Mu-hong. Personalized Recommendation Algorithm Based on PageRank and Spectral Method [J]. Computer Science, 2018, 45(11A): 398-401.
[6] HUANG Xian-ying, YANG Lin-feng, LIU Xiao-yang. Information Dissemination and Mathematical Modeling of Microblog under Graded Opinion Leader [J]. Computer Science, 2018, 45(11): 261-266.
[7] QI Yu-dong, HE Cheng and YUAN Wei. Algorithm of Importance Ranking for Influencing Factors of Website Service Quality Based on PageRank [J]. Computer Science, 2017, 44(Z11): 80-83.
[8] WANG Zhen-fei, LIU Kai-li, ZHENG Zhi-yun and WANG Fei. Research on Evolution Model of Microblog Topic Based on Time Sequence [J]. Computer Science, 2017, 44(8): 270-273, 279.
[9] WANG Zhen-fei, ZHU Jing-yang, ZHENG Zhi-yun and SONG Yu. Analysis of Microblog Community Users’ Influence Based on R-C Model [J]. Computer Science, 2017, 44(3): 254-258, 282.
[10] WANG Zhen-fei, ZHANG Li-ying, ZHANG Xing-jin and LI Lun. Research on Temporal Perception-oriented Microblog Propagation Model [J]. Computer Science, 2017, 44(2): 275-278, 289.
[11] HUANG Lei, LI Shou-shan and ZHOU Guo-dong. Emotion Recognition of Chinese Microblogs with Syntactic Information [J]. Computer Science, 2017, 44(2): 244-249.
[12] XU Yan-fei, LIU Yuan and WU Wen-peng. Research and Application of Social Network Data Acquisition Technology [J]. Computer Science, 2017, 44(1): 277-282.
[13] CUI Wei-na. New Method of Microblog Classification Based on Feature Weighted Language Model [J]. Computer Science, 2016, 43(Z11): 469-471.
[14] ZHU Ming-feng, YE Shi-ren and YE Ren-ming. Extract Summarization Method Based on Lex-PageRank in Chinese Microblog [J]. Computer Science, 2016, 43(9): 261-265.
[15] XUE Zhu-jun, YANG Shu-qiang and SHU Yang-xue. Microblog Text Summarization Based on Entity Relation Network [J]. Computer Science, 2016, 43(9): 77-81.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75, 88 .
[2] XIA Qing-xun and ZHUANG Yi. Remote Attestation Mechanism Based on Locality Principle[J]. Computer Science, 2018, 45(4): 148 -151, 162 .
[3] LI Bai-shen, LI Ling-zhi, SUN Yong and ZHU Yan-qin. Intranet Defense Algorithm Based on Pseudo Boosting Decision Tree[J]. Computer Science, 2018, 45(4): 157 -162 .
[4] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[5] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[6] LIU Qin. Study on Data Quality Based on Constraint in Computer Forensics[J]. Computer Science, 2018, 45(4): 169 -172 .
[7] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99, 116 .
[8] ZHOU Yan-ping and YE Qiao-lin. L1-norm Distance Based Least Squares Twin Support Vector Machine[J]. Computer Science, 2018, 45(4): 100 -105, 130 .
[9] WANG Zhen-wu, LV Xiao-hua and HAN Xiao-hui. Survey of Terrain LOD Technology Based on Quadtree Segmentation[J]. Computer Science, 2018, 45(4): 34 -45 .
[10] CUI Qiong, LI Jian-hua, WANG Hong and NAN Ming-li. Resilience Analysis Model of Networked Command Information System Based on Node Repairability[J]. Computer Science, 2018, 45(4): 117 -121, 136 .