Please wait a minute...
New Technology of Library and Information Service  2013, Vol. 29 Issue (9): 74-81    DOI: 10.11925/infotech.1003-3513.2013.09.12
Current Issue | Archive | Adv Search |
Research on Microblog Ranking Strategy with the Social Relations
Tang Xiaobo, Fang Xiaoke
School of Information Management, Wuhan University, Wuhan 430072, China
Export: BibTeX | EndNote (RIS)      
Abstract  The emergence of social media makes the environment of retrieving changed. Since the shortcomings of retrieving ranking in microblog, this paper analyzes the microblogging social network relationship, and proposes microblogging ranking strategy with the social relations. That means, social strength is added to the traditional PageRank ranking algorithm, and some related indicators including people popularity, information popularity, information quality, the time factor and some others are considered. The experimental results show that AVG has a higher accuracy, and it can obtain more social relationships compared with conventional ranking algorithm.
Key wordsSocial relations      Microblogging      PageRank      Ranking     
Received: 03 June 2013      Published: 27 September 2013



Cite this article:

Tang Xiaobo, Fang Xiaoke. Research on Microblog Ranking Strategy with the Social Relations. New Technology of Library and Information Service, 2013, 29(9): 74-81.

URL:     OR

[1] Java A, Song X, Finin T, et al. Why We Twitter: Understanding Micoblogging Usage and Communities[C]. In: Proceedings of the 9th WEBKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis, San Jose, California, USA. New York: ACM, 2007:56-65.
[2] Khonsari K K, Nayeri Z A, Fathalian A, et al. Social Network Analysis of Iran’s Green Movement Opposition Groups Using Twitter[C].In: Proceedings of 2010 International Conference on Advances in Social Networks Analysis and Mining. 2010:414-415.
[3] Song J, Lee S, Kim J, et al. Spam Filtering in Twitter Using Sender-Receiver Relationship[C]. In: Proceedings of the 14th International Conference on Recent Advances in Intrusion Detection (RAID’11). Berlin,Heidelberg: Springer-Verlag,2011:301-317.
[4] Dong A, Zhang R, Kolari P, et al. Time is of the Essence: Improving Recency Ranking Using Twitter Data[C]. In: Proceedings of the 19th International Conference on World Wide Web (WWW’10). New York: ACM, 2010:331-340.
[5] Morris M R, Jaime T, Panovich K. A Comparison of Information Seeking Using Search Engines and Social Networks[C]. In: Proceedings of the 4th International AAAI Conference on Weblogs and Social Media. 2010:291-294.
[6] Shuai X, Liu X, Bollen J. Improving News Ranking by Community Tweets[C].In: Proceedings of the 21st International Conference Companion on World Wide Web (WWW’12), Lyon, France. New York: ACM, 2012:1227-1232.
[7] Salton G, Wong A, Yang C S. A Vector Space Model for Automatic Indexing[J]. Communications of the ACM, 1975, 18(11):613-620.
[8] Lv Y, Zhai C. Positional Language Model for Information Retrieval[C]. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’09). New York: ACM, 2009: 299-306.
[9] Weng J, Lim E P, Jiang J, et al. TwitterRank: Finding Topic-sensitive Influential Twitters[C]. In: Proceedings of the 3rd ACM International Conference on Web Search and Data Mining. 2010: 261-270.
[10] Yamaguchi Y, Takahashi T, Amagasa T, et al. TURank: Twitter User Ranking Based on User-Tweet Graph Analysis[C]. In: Proceedings of Web Information Systems Engineering (WISE 2010). Berlin,Heidelberg: Spring-Verlag, 2010:240-253.
[11] Gupta A, Kumaraguru P. Credibility Ranking of Tweets During High Impact Events[C]. In: Proceedings of the 1st Workshop on Privacy and Security in Online Social Media (PSOSM’12). New York: ACM, 2012.
[12] Ravikumar S, Balakrishnan R, Kambhampati S. Ranking Tweets Considering Trust and Relevance[C]. In: Proceedings of the 9th International Workshop on Information Integration on the Web (IIWeb’12). New York: ACM, 2012.
[13] Vosecky J, Leung K W, Ng W. Searching for Quality Microblog Posts: Filtering and Ranking Based on Content Analysis and Implicit Links[C]. In: Proceedings of the 17th International Conference on Database Systems for Advanced Applications (DASFAA’12). Berlin,Heidelberg: Spring-Verlag,2012:397-413.
[14] Chang Y, Dong A, Kolari P, et al. Improving Recency Ranking Using Twitter Data[J]. ACM Transactions on Intelligent Systems and Technology, 2013,4(1):4-24.
[15] 梁秋实,吴一雷,封磊. 基于MapReduce的微博用户搜索排名算法[J]. 计算机应用,2012,32(11):2989-2993.(Liang Qiushi, Wu Yilei, Feng Lei. User Ranking Algorithm for Microblog Search Based on MapReduce[J]. Journal of Computer Applications, 2012,32(11):2989-2993.)
[16] 王璞,董军. 基于时间技术的搜索引擎排名算法[J]. 硅谷, 2012(21): 13-14.(Wang Pu, Dong Jun. Search Engine Ranking Algorithm Based on Time Technology[J]. Silicon, 2012(21):13-14.)
[17] Abdullah I B. Incremental PageRank for Twitter Data Using Hadoop[D]. Edinburgh: University of Edinburgh, 2010.
[18] Kandiah V, Shepelyansky D L. PageRank Model of Opinion Formation on Social Networks[J]. Physica A: Statistical Mechanics and Its Applications, 2012, 391(22):5779-5793.
[19] Cheng F, Zhang X, He B, et al. A Survey of Learning to Rank for Real-Time Twitter Search[C]. In: Proceedings of the 2012 International Conference on Pervasive Computing and the Networked World (ICPCA/SWS’12). Berlin,Heidelberg: Spring-Verlag,2013:150-164.
[20] Barabasi A L, Albert R. Emergence of Scaling in Random Networks[J].Science,1999,286(5439):509-512.
[21] Kim H J, Kim J M. Cyclic Topology in Complex Network[J].Physical Review E, 2005, 72(3): 036109.
[22] Shen Y, Li S, Ye X, et al. Content Mining and Network Analysis of Microblog Spam[J]. Journal of Convergence Information Technology,2010,5(1):135-140.
[23] Brin S, Page L. The Anatomy of a Large-scale Hypertextual Web Search Engine[J]. Computer Networks and ISDN System, 1998,30(1-7):107-117.
[24] Page L, Brin S, Motwani R, et al. The PageRank Citation Ranking: Bringing Order to the Web[EB/OL]. [2013-05-02].
[25] 俞淮,郑倩冰,毛羽刚,等. 基于局部中心度的在线论坛意见领袖发现算法[J]. 计算机技术与发展,2012,22(4):9-11.(Yu Huai, Zheng Qianbing, Mao Yugang, et al. An Algorithm for Online Forum Opinion Leaders Discovery Based on Local Centrality[J]. Computer Technology and Development, 2012,22 (4):9-11.)
[26] 王丹.基于网络论坛的舆论领袖发现技术研究[D].哈尔滨:哈尔滨工业大学,2011.(Wang Dan. Research of Opinion Leader Discovery Technology in BBS[D].Harbin: Harbin Institute of Technology,2011.)
[27] 第一季新浪微博用户量[EB/OL].(2011-06-29).[2013-01-26]. (The Number of Microblogging Users at Sina in the First Quarter [EB/OL].(2011-06-29).[2013-01-26].
[28] 杨冠超. 微博客热点话题发现策略研究[D]. 杭州:浙江大学,2011.(Yang Guanchao. Research of Hot Topic Discovery Strategy on Microblogging Platforms[D]. Hangzhou: Zhejiang University, 2011.)
[29] Magnani M, Montesi D, Rossi L. Conversation Retrieval for Microblogging Sites[J]. Information Retrieval, 2012, 15(3-4):354-372.
[1] Deng Siyi,Le Xiaoqiu. Coreference Resolution Based on Dynamic Semantic Attention[J]. 数据分析与知识发现, 2020, 4(5): 46-53.
[2] Liang Yanping,An Lu,Liu Jing. Topic Resonance of Micro-blogs on Similar Public Health Emergencies[J]. 数据分析与知识发现, 2020, 4(2/3): 122-133.
[3] Ming Yi,Tingting Zhang. Ranking Answer Quality of Popular Q&A Community[J]. 数据分析与知识发现, 2019, 3(6): 12-20.
[4] Qikai Cheng,Jiamin Wang,Wei Lu. Discovering Domain Vocabularies Based on Citation Co-word Network[J]. 数据分析与知识发现, 2019, 3(6): 57-65.
[5] Liu Junwan,Yang Bo,Wang Feifei. Ranking Scholarly Impacts Based on Citations and Academic Similarity[J]. 数据分析与知识发现, 2018, 2(4): 59-70.
[6] Zhou Lixin,Lin Jie. Extracting Product Features with NodeRank Algorithm[J]. 数据分析与知识发现, 2018, 2(4): 90-98.
[7] Hou Jun,Liu Kui,Li Qianmu. Classification Recommendation Based on ESSVM[J]. 数据分析与知识发现, 2018, 2(3): 9-21.
[8] Chen Xiaowei,Shi Yutian. Identifying Key Nodes in Social Network with Improved PageRank Algorithm[J]. 数据分析与知识发现, 2017, 1(8): 68-75.
[9] Ye Guanghui,Xia Lixin. Review of Expert Retrieval and Expert Ranking Studies[J]. 数据分析与知识发现, 2017, 1(2): 1-10.
[10] He Wanying,Yang Jianlin. Ranking Learning Method Based on Random Walk Model[J]. 数据分析与知识发现, 2017, 1(12): 41-48.
[11] Liu Tong,Yang Jingcheng. Evaluating Online Healthcare Consultation Feedbacks Based on Signal Transmission Algorithm[J]. 数据分析与知识发现, 2017, 1(11): 29-36.
[12] Zhang Xiaojuan,Han Yi. Reviews on Temporal Information Retrieval[J]. 数据分析与知识发现, 2017, 1(1): 3-15.
[13] He Jianmin,Yin Shu. Identifying Influential Users in Social Networks[J]. 现代图书情报技术, 2016, 32(4): 20-30.
[14] Yuan Meng, Hongwei Wang. Extracting Product Feature and User Opinion from Chinese Reviews[J]. 现代图书情报技术, 2016, 32(2): 16-24.
[15] Liu Tong,Ni Weijian,Liu Mei. Identifying Terminology from Search Engine Query Logs[J]. 现代图书情报技术, 2016, 32(2): 25-33.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938