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数据分析与知识发现  2018, Vol. 2 Issue (7): 26-33     https://doi.org/10.11925/infotech.2096-3467.2017.1067
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
社交媒体环境下用户信任度评估与传播影响力研究*
景东, 张大勇()
哈尔滨工业大学互动媒体设计与装备服务创新重点实验室 哈尔滨 150001
Assessing Trust-Based Users’ Influence in Social Media
Jing Dong, Zhang Dayong()
Key Laboratory of Interactive Media Design and Equipment Services Innovation, Harbin Institute of Technology, Harbin 150001,China
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摘要 

目的】通过研究社交媒体用户信任度评估和传播影响力, 发现推动或制约信息传播的关键因素, 为促进社交媒体健康有序发展提供服务。【方法】根据网络信任特点, 提出一种基于直接信任和间接信任的综合评估指标, 该指标综合考虑个体的局部影响力和全局调控能力。【结果】SIR模型评估实验结果表明, 综合评估指标值最大的个体发起的传播能在较短的传播时间内达到最大的范围。【局限】数据来源不够广泛, 可能导致研究偏差。【结论】所构建的综合评估指标能够更为准确地度量网络中每个个体的信任水平。

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景东
张大勇
关键词 社交媒体直接信任综合信任传播影响力    
Abstract

[Objective] The paper studies the impacts of trust on social media users’ influence to detect factors affecting information dissemination, which could benefit the development of social media. [Methods] We proposed a comprehensive evaluation index based on the direct and indirect trust, as well as the local and global influence of each individual user of social media. [Results] Simulations based on SIR model showed that original message from individuals with the highest comprehensive index value could reach the largest number of users. [Limitations] The collected data was not comprehensive, which might yield biased results. [Conclusions] The proposed index could effectively measure the trust level of each individual in social media.

Key wordsSocial Media    Direct Trust    Comprehensive Trust    Influence
收稿日期: 2017-10-26      出版日期: 2018-08-15
ZTFLH:  分类号: G203  
基金资助:*本文系国家社会科学基金项目“社交媒体突发公共事件的协同应急机制研究”(项目编号: 14CXW045)、教育部人文社会科学基金项目“微博突发公共事件传播路径的实时分析及趋势预测”(项目编号: 13YJC860013)和中央高校基本科研业务费专项资金资助项目“我国网络文化分级监管体系构建与集成策略研究”(项目编号: HIT.HSS.201119)的研究成果之一
引用本文:   
景东, 张大勇. 社交媒体环境下用户信任度评估与传播影响力研究*[J]. 数据分析与知识发现, 2018, 2(7): 26-33.
Jing Dong,Zhang Dayong. Assessing Trust-Based Users’ Influence in Social Media. Data Analysis and Knowledge Discovery, 2018, 2(7): 26-33.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2017.1067      或      http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2018/V2/I7/26
排序 直接信任值前10名节点 间接信任值前10名节点 综合信任值前10名节点
节点编号 标准化值 节点编号 标准化值 节点编号 标准化值
1 ID6 0.124 ID502 0.012 ID119 1.103
2 ID782 0.123 ID34 0.011 ID78 0.831
3 ID96 0.119 ID71 0.010 ID202 0.813
4 ID111 0.117 ID1187 0.009 ID76 0.705
5 ID2272 0.116 ID366 0.008 ID502 0.690
6 ID23 0.115 ID12 0.008 ID2002 0.684
7 ID1187 0.114 ID993 0.007 ID117 0.683
8 ID3012 0.113 ID801 0.006 ID336 0.647
9 ID76 0.111 ID876 0.006 ID2370 0.626
10 ID139 0.109 ID432 0.005 ID769 0.610
  网络中前10名节点信任度水平
  不同初始传播节点下用户状态随时间变化情况(SIR仿真)
  单位时间处于感染状态节点数
  累积感染节点总数
[1] 都平平, 郭琪, 李雨珂, 等. 基于社交媒体的网络学科信息交互推广服务[J]. 图书情报工作, 2014, 58(2): 84-90.
doi: 10.13266/j.issn.0252-3116.2014.02.014
[1] (Du Pingping, Guo Qi, Li Yuke, et al.Interactive Promotion Service of Discipline Information Based on Social Media Networks[J]. Library and Information Service, 2014, 58(2): 84-90. )
doi: 10.13266/j.issn.0252-3116.2014.02.014
[2] Kuhlman C J, Kumar V S A, Ravi S S. Controlling Opinion Propagation in Online Networks[J]. Computer Networks, 2013, 57: 2121-2132.
doi: 10.1016/j.comnet.2012.11.025
[3] Oh O, Eom C, Rao H R.Role of Social Media in Social Change: An Analysis of Collective Sense Making During the 2011 Egypt Revolution[J]. Information Systems Research, 2015, 26(1): 210-223.
doi: 10.1287/isre.2015.0565
[4] 胡吉明. 社会网络环境下的信息传播机制[J]. 情报科学, 2013, 33(1): 15-18.
[4] (Hu Jiming.Mechanism of Information Dissemination Under Socialization Networking Environment[J]. Information Science, 2013, 33(1): 15-18. )
[5] 李栋, 徐志明, 李生, 等. 在线社会网络中信息扩散[J]. 计算机学报, 2014, 37(1): 189-206.
doi: 10.3724/SP.J.1016.2014.00189
[5] (Li Dong, Xu Zhiming, Li Sheng, et al.A Survey on Information Diffusion in Online Social Networks[J]. Chinese Journal of Computers, 2014, 37(1): 189-206. )
doi: 10.3724/SP.J.1016.2014.00189
[6] Szell M, Grauwin S, Ratti C.Contraction of Online Response to Major Events[J]. PLoS ONE, 2014, 9(2): e89052.
doi: 10.1371/journal.pone.0089052 pmid: 3935844
[7] Kalai A, Zayani C A, Amous I, et al.Social Collaborative Service Recommendation Approach Based on User’s Trust and Domain-Specific Expertise[J]. Future Generation Computer Systems, 2018, 80: 355-367.
doi: 10.1016/j.future.2017.05.036
[8] 翟学伟. 信任的本质及其文化[J]. 社会, 2014, 34(1): 1-26.
[8] (Zhai Xuewei.The Essence of Trust and Its Culture[J]. Chinese Journal of Sociology, 2014, 34(1): 1-26. )
[9] Yen Y F, Tseng J F, Wang H K.The Effect of Internal Social Capital on Knowledge Sharing[J]. Knowledge Management Research & Practice, 2015, 13(2): 214-224.
doi: 10.1057/kmrp.2013.43
[10] Oliveira T, Alhinho M, Rita P, et al.Modelling and Testing Consumer Trust Dimensions in E-Commerce[J]. Computers in Human Behavior, 2017, 71: 153-164.
doi: 10.1016/j.chb.2017.01.050
[11] Corritore C L, Kracher B, Wiedenbeck S.On-line Trust: Concepts, Evolving Themes, A Model[J]. International Journal of Human-Computer Studies, 2003, 58(6): 737-758.
doi: 10.1016/S1071-5819(03)00041-7
[12] Beldad A, De Jong M, Steehouder M.How Shall I Trust the Faceless and the Intangible? A Literature Review on the Antecedents of Online Trust[J]. Computers in Human Behavior, 2010, 26(5): 857-869.
doi: 10.1016/j.chb.2010.03.013
[13] Roy A, Singhal A, Srivastava J.Formation and Reciprocation of Dyadic Trust[J]. ACM Transactions on Internet Technology, 2017, 17(2): Article No. 15.
[14] 赵竞, 孙晓军, 周宗奎, 等. 网络交往中的人际信任[J]. 心理科学进展, 2013, 21(8): 1493-1501.
doi: 10.3724/SP.J.1042.2013.01493
[14] (Zhao Jing, Sun Xiaojun, Zhou Zongkui, et al.Interpersonal Trust in Online Communication[J]. Advances in Psychological Science, 2013, 21(8): 1493-1501. )
doi: 10.3724/SP.J.1042.2013.01493
[15] Utz S.Show Me Your Friends and I will Tell You What Type of Person You Are: How One’s Profile, Number of Friends, and Type of Friends Influence Impression Formation on Social Network Sites[J]. Journal of Computer-Mediated Communication, 2010, 15(2): 314-335.
doi: 10.1111/jcmc.2010.15.issue-2
[16] Sani N S, Tabriz F N.A New Strategy in Trust-Based Recommender System Using K-Means Clustering[J]. International Journal of Advanced Computer Science and Applications, 2017, 8(9): 152-156.
doi: 10.14569/IJACSA.2017.080922
[17] 詹姆斯·S·科尔曼. 社会理论的基础[M]. 邓方译. 北京: 社会科学文献出版社, 1999: 108.
[17] (Coleman J S.Foundations of Social Theory[M]. Translated by Deng Fang. Beijing: Social Sciences Academic Press, 1999: 108. )
[18] Roghanizad M M, Neufeld D J. Intuition, Risk,the Formation of Online Trust[J]. Computers in Human Behavior, 2015, 50: 489-498.
doi: 10.1016/j.chb.2015.04.025
[19] 陈婷, 朱青, 周梦溪, 等. 社交网络环境下基于信任的推荐算法[J]. 软件学报, 2017, 28(3): 721-731.
doi: 10.13328/j.cnki.jos.005159
[19] (Chen Ting, Zhu Qing, Zhou Mengxi, et al.Trust-Based Recommendation Algorithm in Social Network[J]. Journal of Software, 2017, 28(3): 721-731. )
doi: 10.13328/j.cnki.jos.005159
[20] Lewis K, Kaufman J, Gonzalez M, et al.Tastes, Ties, and Time: A New Social Network Dataset Using Facebook. com[J]. Social Networks, 2008, 30(4): 330-342.
doi: 10.1016/j.socnet.2008.07.002
[21] Aral S, Walker D.Identifying Influential and Susceptible Members of Social Networks[J]. Science, 2012, 337: 337-341.
doi: 10.1126/science.1215842 pmid: 22722253
[22] 马磊. 机会、选择和目标——社会网的形成机制及其对新媒体研究的启示[J]. 社会科学, 2015(9): 184-191.
[22] (Ma Lei.Opportunities, Choices and Goals —— The Formation Mechanism of Social Networks and Its Implications to Social Media Research[J]. Journal of Social Sciences, 2015(9): 184-191. )
[23] Ruan Y, Durresi A.A Survey of Trust Management Systems for Online Social Communities —— Trust Modeling, Trust Inference and Attacks[J]. Knowledge-Based Systems, 2016, 106: 150-163.
doi: 10.1016/j.knosys.2016.05.042
[24] 戴维·诺克, 杨松. 社会网络分析[M]. 李兰译. 上海: 格致出版社, 2012: 112.
[24] (Knoke D, Yang S.Social Network Analysis[M]. Translated by Li Lan. Shanghai: Truth & Wisdom Press, 2012: 112.)
[25] 李勇军, 代亚非. 对等网络信任机制研究[J]. 计算机学报, 2010, 33(3): 390-405.
doi: 10.3724/SP.J.1016.2010.00390
[25] (Li Yongjun, Dai Yafei.Research on Trust Mechanism for Peer-to-Peer Network[J]. Chinese Journal of Computers, 2010, 33(3): 390-405. )
doi: 10.3724/SP.J.1016.2010.00390
[26] 王余斌, 王成良, 文俊浩. 基于用户评论评分与信任度的协同过滤算法[J].计算机应用研究, 2018, 35(5). .
[26] (Wang Yubin, Wang Chengliang, Wen Junhao.Research on Collaborative Filtering Recommendation Algorithm Based on Ratings Reviews and User Trust[J].Application Research of Computers, 2018, 35(5).
[27] 张伟. 复杂开放网络中的多主体意见演化模型及其仿真[J]. 情报杂志, 2015, 34(9): 145-151.
doi: 10.3969/j.issn.1002-1965.2015.09.028
[27] (Zhang Wei.Model and Simulation of Multi-agent Opinions Dynamics in an Open Complex Network[J]. Journal of Intelligence, 2015, 34(9): 145-151. )
doi: 10.3969/j.issn.1002-1965.2015.09.028
[28] Qian D J, Zhang J S, Cochran D.Conjoining Speeds up Information Diffusion in Overlaying Social-Physical Networks[J]. IEEE Journal on Selected Areas in Communications, 2013, 31(6): 1038-1048.
doi: 10.1109/JSAC.2013.130606
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