Please wait a minute...
Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (6): 70-78    DOI: 10.11925/infotech.2096-3467.2017.1311
Current Issue | Archive | Adv Search |
Analyzing Growth Trends and Attachment Mode of Social Blog Tags
Ye Guanghui1(), Hu Jinglan1, Xu Jian2, Xia Lixin1
1School of Information Management, Central China Normal University, Wuhan 430079, China
2Center for the Studies of Information Resources, Wuhan University, Wuhan 430072, China
Download: PDF (680 KB)   HTML ( 1
Export: BibTeX | EndNote (RIS)      
Abstract  

[Objective] This study reveals the forming mechanism of network nodes, aiming to examine the growth trend and attachment mode of social blog tags. [Methods] Firstly, we proposed the model of tag growth with the help of statistics and network analysis. Then, we established the categories of tag links and corresponding numbers, as well as summarized the connection rules of newly added tags. Finally, we defined the indicators of degree dependency and examined the probability of tag connection following preferential attachment modes. [Results] The tag growth showed the linear growth pattern and the distribution of tags had one single peak center, the shock left side and the gentle right side, which did not meet the power-law distribution. [Limitations] We did not explain the impacts of users’ tagging behaviors on the network connections. [Conclusions] Neither the “new tag-old tag” nor the “old tag-old tag” models are not fully compliant with the preferential attachment mode.

Key wordsSocial Blog Tag      Growth Trend      Attachment Mode      Social Network      Preferential Attachment      Degree Dependency     
Received: 22 December 2017      Published: 11 July 2018
ZTFLH:  G350  

Cite this article:

Ye Guanghui,Hu Jinglan,Xu Jian,Xia Lixin. Analyzing Growth Trends and Attachment Mode of Social Blog Tags. Data Analysis and Knowledge Discovery, 2018, 2(6): 70-78.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.1311     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2018/V2/I6/70

时刻 年份 非重复标签数量(个)
0 2006 2 057
1 2007 2 686
2 2008 4 069
3 2009 5 488
4 2010 6 967
5 2011 7 779
6 2012 8 397
7 2013 8 838
8 2014 9 145
9 2015 9 465
10 2016 9 737
配对样本 样本数 相关
系数
相关
显著度p1
t t检验
显著度p2
对1-2013&2014 988 0.926 0.000 -19.410 0.000
对2-2014&2015 988 0.961 0.000 -16.516 0.000
对3-2015&2016 988 0.976 0.000 -13.982 0.000
标签增长(Ⅰ) 标签增长(Ⅱ) 标签增长(Ⅲ) 标签增长(Ⅰ-Ⅲ)
不变 33.7% 33.2% 42.7% 24.5%
变大 66.3% 66.8% 57.3% 75.5%
2013 2014 2015 2016
2013 Pearson 相关性 1 0.926** 0.843** 0.777**
显著性(双侧) 0.000 0.000 0.000
2014 Pearson 相关性 0.926** 1 0.961** 0.913**
显著性(双侧) 0.000 0.000 0.000
2015 Pearson 相关性 0.843** 0.961** 1 0.976**
显著性(双侧) 0.000 0.000 0.000
2016 Pearson 相关性 0.777** 0.913** .976** 1
显著性(双侧) 0.000 0.000 .000
年度 标签增长阶段 Pearson相关性 显著性(双侧)
2013 (Ⅰ) -0.094** 0.003
2014 (Ⅱ) -0.100** 0.002
2015 (Ⅲ) -0.049 0.124
2016 (Ⅰ-Ⅲ) -0.146** 0.000
标签关系
时间
新标签-新标签 新标签-旧标签 旧标签-旧标签
2013-2014 4 611 17 616 31 060
2013-2015 3 372 18 140 59 821
2013-2016 4 038 21 213 88 045
标签连接概率
时间
新标签-新
标签
新标签-旧标签 旧标签-旧标签
建立 强化
2013-2014 0.029 0.971 0.153 0.847
2013-2015 0.026 0.974 0.481 0.519
2013-2016 0.050 0.950 0.321 0.679
均值 0.035 0.965 0.318 0.682

标签连接模式
时间
新标签-旧标签 旧标签-旧标签
优先连接 非优先
连接
优先连接 非优先
连接
2013-2014 7 439 10 120 14 294 38 993
2013-2015 5 730 12 354 21 758 59 575
2013-2016 6 233 14 856 25 582 87 714
均值 6 467 12 443 20 545 62 094
[1] 张恒婷. 社交网络图像垃圾标签去除研究[D]. 北京: 华北电力大学, 2012.
[1] (Zhang Hengting.Research on Filtering Tag Spam of Social Network Images[D]. Beijing: North China Electric Power University, 2012.)
[2] 王贤兵. 社会标注可信度评价方法研究[D]. 武汉: 华中科技大学, 2012.
[2] (Wang Xianbing.Research on Method of Evaluating Confidence of Social Annotations[D]. Wuhan: Huazhong University of Science and Technology, 2012.)
[3] 刘苏祺, 白光伟, 沈航. 基于用户自描述标签的层次分类体系构建方法[J]. 计算机科学, 2016, 43(7): 224-229, 239.
[3] (Liu Suqi, Bai Guangwei, Shen Hang.Taxonomy Construction Based on User Self-describing Tags[J]. Computer Science, 2016, 43(7): 224-229, 239.)
[4] 李纲, 刘广兴, 毛进, 等. 一种基于句法分析的情感标签抽取方法[J]. 图书情报工作, 2014, 58(14): 12-20.
doi: 10.13266/j.issn.0252-3116.2014.14.002
[4] (Li Gang, Liu Guangxing, Mao Jin, et al.A Sentiment Label Extraction Method Based on Dependency Parsing[J]. Library and Information Service, 2014, 58(14): 12-20.)
doi: 10.13266/j.issn.0252-3116.2014.14.002
[5] 宋灵超, 黄崑. 基于社会标签的图像情感分类标注研究[J].图书情报工作, 2016, 60(21): 103-112.
doi: 10.13266/j.issn.0252-3116.2016.21.014
[5] (Song Lingchao, Huang Kun.Research on Image Emotional Annotations Based on Social Tags[J]. Library and Information Service, 2016, 60(21): 103-112.)
doi: 10.13266/j.issn.0252-3116.2016.21.014
[6] 于海鹏, 翟红生. 一种子空间聚类算法在多标签文本分类中应用[J]. 计算机应用与软件, 2014, 31(8): 288-291, 303.
doi: 10.3969/j.issn.1000-386x.2014.08.072
[6] (Yu Haipeng, Zhai Hongsheng.Applying a Subspace Clustering Algorithm in Multi-Label Text Classification[J]. Computer Applications and Software, 2014, 31(8): 288-291, 303.)
doi: 10.3969/j.issn.1000-386x.2014.08.072
[7] 杨尊琦, 赵瑾珺. 新浪微博用户领域分类标签的结构和互动研究[J]. 情报杂志, 2014, 33(4): 122-127.
doi: 10.3969/j.issn.1002-1965.2014.04.022
[7] (Yang Zunqi, Zhao Jinjun.Structure and Interaction: The User Category Tags on the Sina Microblog[J]. Journal of Intelligence, 2014, 33(4): 122-127.)
doi: 10.3969/j.issn.1002-1965.2014.04.022
[8] 叶光辉, 李纲. 社会语义网络结构分析——以MetaFilter为例[J]. 情报理论与实践, 2015, 38(12): 57-63.
doi: 10.16353/j.cnki.1000-7490.2015.12.012
[8] (Ye Guanghui, Li Gang.Structure Analysis on Semantic Social Network Based on MetaFilter[J]. Information Studies: Theory & Application, 2015, 38(12): 57-63.)
doi: 10.16353/j.cnki.1000-7490.2015.12.012
[9] Chen J, Feng S, Liu J.Topic Sense Induction from Social Tags Based on Non-negative Matrix Factorization[J]. Information Sciences, 2014, 280: 16-25.
doi: 10.1016/j.ins.2014.04.048
[10] Pan W, Chen S, Feng Z.Automatic Clustering of Social Tag Using Community Detection[J]. Applied Mathematics & Information Sciences, 2013, 7(2): 675-681.
doi: 10.12785/amis/070235
[11] Chelmis C, Prasanna V K.Social Link Prediction in Online Social Tagging Systems[J]. ACM Transactions on Information Systems, 2013, 31(4): 1-27.
doi: 10.1145/2516891
[12] Naseri S, Bahrehmand A, Ding C, et al.Enhancing Tag-based Collaborative Filtering via Integrated Social Networking Information[C]//Proceedings of IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. IEEE, 2013: 760-764.
[13] 易明, 毛进, 邓卫华. 基于社会化标签网络的细粒度用户兴趣建模[J]. 现代图书情报技术, 2011(4): 35-41.
[13] (Yi Ming, Mao Jin, Deng Weihua.Fine-grained User Preference Modeling Based on Tag Networks[J]. New Technology of Library and Information Service, 2011(4): 35-41.)
[14] Tu H, Wang X.Mining Users’ Interest Graph in Social Networks with Topic Based Tag Propagation[C]//Proceedings of IET International Conference on Smart and Sustainable City. IET, 2014: 282-285.
[15] 易明, 王学东, 邓卫华. 基于社会网络分析的社会化标签网络分析与个性化信息服务研究[J]. 中国图书馆学报, 2010, 36(2): 107-114.
[15] (Yi Ming, Wang Xuedong, Deng Weihua.Social Labeling Network Analysis and Personalized Information Service Research Based on Social Network Analysis[J]. Journal of Library Science in China, 2010, 36(2): 107-114.)
[16] 易明, 毛进, 邓卫华, 等. 社会化标签系统中基于社会网络的知识推送网络演化研究[J]. 中国图书馆学报, 2014, 40(2): 50-66.
[16] (Yi Ming, Mao Jin, Deng Weihua, et al.Evolution of Knowledge Push Network Based on Social Network in Social Tagging System[J]. Journal of Library Science in China, 2014, 40(2): 50-66.)
[17] Ma H, Jia M, Zhang D, et al. Combining Tag Correlation and User Social Relation for Microblog Recommendation[J]. Information Sciences, 2017, 385-386(C): 325-337.
doi: 10.1016/j.ins.2016.12.047
[18] 蔡国永, 林航, 文益民. 社会语义网社区发现标签传递算法研究[J]. 计算机科学, 2013, 40(2): 53-57.
[18] (Cai Guoyong, Lin Hang, Wen Yimin.Study on Label Propagation Based Community Detection Algorithm for Social Semantic Network[J]. Computer Science, 2013, 40(2): 53-57.)
[19] 李栋. 在线社会网络中信息扩散研究[D]. 哈尔滨: 哈尔滨工业大学, 2014.
[19] (Li Dong.Research of Information Diffusion in Online Social Networks[D]. Harbin: Harbin Institute of Technology, 2014.)
[20] 宋莉. 舆情热点事件中“标签式传播”现象研究——以“二代”现象为例[D]. 哈尔滨: 黑龙江大学, 2016.
[20] (Song Li.Study on Label Communication Phenomenon in the Hot Events of Public Opinion——Take “Sencond Genenration Phenomenon” as an Example[D]. Harbin: Heilongjiang University, 2016.)
[21] 查先进, 吕彬. 知识共享视角下的大众标注行为研究——基于标签的实证分析[J]. 图书馆论坛, 2010, 30(6): 76-81.
[21] (Zha Xianjin, Lv Bin.Study on the Behaviour of Social Tagging from the Aspect of Knowledge Sharing: An Empirical Analysis Based on Tags[J]. Library Tribune, 2010, 30(6): 76-81.)
[22] 郑惠中, 左万利. 基于信息增益与语义特征的多标签社交网络用户人格预测[J]. 吉林大学学报: 理学版, 2016, 54(3): 561-568.
doi: 10.13413/j.cnki.jdxblxb.2016.03.28
[22] (Zheng Huizhong, Zuo Wanli.Multi-labeled Social Networks Users Personality Prediction Based on Information Gain and Semantic Features[J]. Journal of Jilin University: Science Edition, 2016, 54(3): 561-568.)
doi: 10.13413/j.cnki.jdxblxb.2016.03.28
[23] 叶光辉, 夏立新, 李纲, 等. 社交博客标签分布的布拉德福定律验证分析[J]. 情报学报, 2018, 37(1): 76-85.
[23] (Ye Guanghui, Xia Lixin, Li Gang, et al.Bradford’s Law Confirmatory Analysis of Social Blog Tag Distribution[J]. Journal of the China Society for Scientific and Technical Information, 2018, 37(1): 76-85.)
[24] 邱均平. 信息计量学[M]. 武汉: 武汉大学出版社, 2007: 43-222.
[24] (Qiu Junping.Informetrics[M]. Wuhan: Wuhan University Press, 2007: 43-222.)
[25] Yule G U.A Mathematical Theory of Evolution, Based on the Conclusions of Dr. J. C. Willis, F. R. S[J]. Philosophical Transactions of the Royal Society of London(Series B), 1925, 213: 21-87.
doi: 10.1098/rstb.1925.0002
[26] 苏芳荔, 李江. 链接分布机制评述——优先连接和均匀连接[J]. 情报杂志, 2010, 29(10): 167-171.
doi: 10.3969/j.issn.1002-1965.2010.10.038
[26] (Su Fangli, Li Jiang.Review on the Mechanism of Link Degree Distribution——Preferential Attachment and Uniform Attachment[J]. Journal of Intelligence, 2010, 29(10): 167-171.)
doi: 10.3969/j.issn.1002-1965.2010.10.038
[1] Peng Guan,Yuefen Wang. Advances in Patent Network[J]. 数据分析与知识发现, 2020, 4(1): 26-39.
[2] Yan Wen,Lijian Ma,Qingtian Zeng,Wenyan Guo. POI Recommendation Based on Geographic and Social Relationship Preferences[J]. 数据分析与知识发现, 2019, 3(8): 30-39.
[3] Liqing Qiu,Wei Jia,Xin Fan. Influence Maximization Algorithm Based on Overlapping Community[J]. 数据分析与知识发现, 2019, 3(7): 94-102.
[4] Xiaolan Wu,Chengzhi Zhang. Analysis of Knowledge Flow Based on Academic Social Networks:
A Case Study of ScienceNet.cn
[J]. 数据分析与知识发现, 2019, 3(4): 107-116.
[5] Xinrui Wang,Yue He. Predicting Stock Market Fluctuations with Social Media Behaviors: Case Study of Sina Finance Blog[J]. 数据分析与知识发现, 2019, 3(11): 108-119.
[6] Jiehua Wu,Jing Shen,Bei Zhou. Classifying Multilayer Social Network Links Based on Transfer Component Analysis[J]. 数据分析与知识发现, 2018, 2(9): 88-99.
[7] Guo Bo,Zhao Junrui,Sun Yu. Analyzing Characteristics and Dynamics of User Behaviors in Social Q&A Community: Case Study of Zhihu.com[J]. 数据分析与知识发现, 2018, 2(4): 48-58.
[8] Wang Feifei,Zhang Shengtai. Analyzing Information Behaviors of Mobile Social Network Users[J]. 数据分析与知识发现, 2018, 2(4): 99-109.
[9] Zhang Ling,Luo Manman,Zhu Lijun. Analyzing Information Dissemination on Social Networks[J]. 数据分析与知识发现, 2018, 2(2): 46-57.
[10] Chen Fen,Fu Xi,He Yuan,Xue Chunxiang. Identifying Weibo Opinion Leaders with Social Network Analysis and Influence Diffusion Model[J]. 数据分析与知识发现, 2018, 2(12): 60-67.
[11] Li Gang,Wang Xiao,Guo Yang. Detecting Relationship Among WeChat Group Members with Co-occurrence of Cooperation[J]. 数据分析与知识发现, 2018, 2(11): 54-63.
[12] Wang Zhongyi,Zhang Heming,Huang Jing,Li Chunya. Studying Knowledge Dissemination of Online Q&A Community with Social Network Analysis[J]. 数据分析与知识发现, 2018, 2(11): 80-94.
[13] Li Zhen,Ding Shengchun,Wang Nan. Identifying Topics of Online Public Opinion[J]. 数据分析与知识发现, 2017, 1(8): 18-30.
[14] Li Fei,Zhang Jian,Wang Zongshui. Review of Social Recommendation with Bibliometrics and Social Network Analysis[J]. 数据分析与知识发现, 2017, 1(6): 22-35.
[15] Wang Xiwei,Zhang Liu,Li Shimeng,Wang Nan’axue. The Dissemination of Online Public Opinion on Social Welfare Issues via New Media: Case Study of “Draw up the Lifeline” in Sina Weibo[J]. 数据分析与知识发现, 2017, 1(6): 93-101.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn