在线社交媒体信息冗余现象建模与实证研究

秦烁; 吕欣; 孟凡辉; 胡延庆

doi:10.11959/j.issn.2096-0271.2018050

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在线社交媒体信息冗余现象建模与实证研究

专题：大数据创新实践 | 更新时间：2024-06-03

- 在线社交媒体信息冗余现象建模与实证研究
- Modeling and empirical research of information redundancy on online social media
- 大数据 2018年4卷第5期页码：2018050
- 作者机构：
  
  1. 国防科技大学系统工程学院，湖南长沙 410073
  2. 中南大学商学院，湖南长沙 410083
  3. 卡罗林斯卡研究所公共卫生科学系，瑞典斯德哥尔摩 17177
  4. 中山大学数据科学与计算机学院，广东广州 510006
- 作者简介：
  
  [ "秦烁（1995-），女，国防科技大学系统工程学院硕士生，主要研究方向为复杂网络传播动力学。" ]
  [ "吕欣（1984-），男，国防科技大学系统工程学院副教授，主要研究方向为大数据挖掘、人类行为动力学分析。" ]
  [ "孟凡辉（1993-），男，中山大学数据科学与计算机学院硕士生，主要研究方向为计算传播学。" ]
  [ "胡延庆（1980-），男，博士，中山大学数据科学与计算机学院副教授，主要研究方向为复杂系统理论。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(71522014);国家自然科学基金资助项目;The National Natural Science Foundation of China(71771213);国家自然科学基金资助项目;The National Natural Science Foundation of China(61773412);国家自然科学基金资助项目;The National Natural Science Foundation of China(U1711265)
- DOI：10.11959/j.issn.2096-0271.2018050
  中图分类号： N941.3
- 网络首发：2018-09，
  
  纸质出版：2018-09-15
- 稿件说明：
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秦烁, 吕欣, 孟凡辉, 等. 在线社交媒体信息冗余现象建模与实证研究[J]. 大数据, 2018,4(5):2018050.

Shuo QIN, Xin LU, Fanhui MENG, et al. Modeling and empirical research of information redundancy on online social media[J]. Big Data Research, 2018, 4(5): 2018050.
秦烁, 吕欣, 孟凡辉, 等. 在线社交媒体信息冗余现象建模与实证研究[J]. 大数据, 2018,4(5):2018050. DOI： 10.11959/j.issn.2096-0271.2018050.

Shuo QIN, Xin LU, Fanhui MENG, et al. Modeling and empirical research of information redundancy on online social media[J]. Big Data Research, 2018, 4(5): 2018050. DOI： 10.11959/j.issn.2096-0271.2018050.

摘要

为深入了解社交媒体中的信息冗余现象，定义了一种以个体好友信息发布的重复情况度量信息冗余程度的指标。推导个体在传播过程中出现冗余现象的概率，分析了信息传播率、网络密度、集群系数对信息冗余的影响；通过新浪微博的实际数据观察了冗余信息的扩散特点，并从营销的角度探讨了信息冗余在广告推广和产品营销方面的意义。研究结果为进一步了解在线社交媒体的信息扩散特征提供了新的视角。

Abstract

To learn more about redundancy phenomenon

a measurement to quantify this redundancy was proposed and the probability of individual redundancy in the process of information diffusion was deduced.Using simulated networks

how information propagation rate

network density

clustering coefficient affect information redundancy was investigated.The diffusion characteristics of redundant information through the actual data of Sina Weibo was observed and the perspective of information redundancy in advertising promotion and product marketing was discussed from marketing.The findings provide a new perspective for further understanding of the information diffusion characteristics of online social media.

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references

JIN X , JIN C , HUANG J , et al . Coupling effect of nodes popularity and similarity on social network persistence [J ] . Scientific Reports , 2017 ( 7 ):42956.

ANGELA E , ANNE M . The problem of information overload in business organizations:a review of the literature [J ] . International Journal of Information Management , 2000 , 20 ( 1 ): 17 - 28 .

BAKSHY E , ROSENN I , MARLOW C , et al . The role of soc ial networks in information diffusion [C ] // The 21st International Conference on World Wide Web,April 16-20,2012,Lyon,France . New York:ACM Press , 2012 : 519 - 528 .

GUILLE A , . Information diffusion in online social networks [C ] // The 2013 SIGMOD/PODS Ph.D.Symposium,June 23,2013,New York,USA . New York:ACM Press , 2013 : 31 - 36 .

LI P , LI W , WANG H , et al . Modeling of information diffusion in Twitterlike social networks under information overload [J ] . Scientific Worl d Journal , 2014 , 2014 ( 5721 ):914907.

HALBERSTAM Y , KNIGHT B . Homophily,grou p size,and the diffusion of political information in social networks:evidence from Twitter [J ] . Journal of Public Economics , 2016 ( 143 ): 73 - 88 .

GRUHL D , GUHA R , LIBEN-NOWELL D , et al . Information dif fusion through blogspace [C ] // International Conference on World Wide Web,May 17-20,2004,New York,USA . New York:ACM Press , 2004 : 491 - 501 .

ZHANG Z K , LIU C , ZHAN X X , et al . Dynamics of inf ormation diffusion and its applications on complex networks [J ] . Physics Reports , 2016 , 651 : 1 - 34 .

WATTS D J , STROGATZ S H . Collective dynamics of “small-world” networks [J ] . Nature , 1998 , 393 ( 6684 ):440.

BARABASI A L , ALBERT R . Emergence of scaling in random networks [J ] . Science , 1999 , 286 ( 5439 ):509.

NEWMAN M E . The structure of scientific collaboration networks [J ] . Proceedings of the National Academy of Sciences of the United States of America , 2001 , 98 ( 2 ): 404 - 409 .

BARABASI A L , JEONG H , NEDA Z , et al . Evolution of the social network of scientific collaborations [J ] . Physica A Sta tistical Mechanics ＆ Its Applications , 2002 , 311 ( 3 ): 590 - 614 .

MA Y H , LI H J , ZHANG X D . Strength distribution of novel local-world networks [J ] . Physica A Statistical Mechanics ＆ Its Applications , 2009 , 388 ( 21 ): 4669 - 4677 .

HOLME P , KIM B J . Growing scalefree networks with tunable clustering [J ] . Physical Review E Statistical Nonlinear ＆Soft Matter Physics , 2002 , 65 ( 2 ):026107.

LU X . Improving sampl e estimate reliability and validity with linked ego networks [J ] . Methodology , 2012 :arXiv:1205.1971v1.

SALGANIK M J , HECKATHORN D D . Sampling and estimation in hidden populations using respondent-driven sampling [J ] . Sociological Methodology , 2004 , 34 ( 1 ): 193 - 240 .

HECKATHORN D D . Extensions of respondent-driven sampling:analyzing continuous variables and controlling for differential recruitment [J ] . Sociological Methodology , 2007 , 37 ( 1 ): 151 - 207 .

VOLZ E , HECKATHORN D D . Probability based estimation theory for respondent driven sampling [J ] . Journal of Official Statistics , 2008 , 24 ( 1 ): 79 - 97 .

ZHANG Z K , ZHANG C X , HAN X P , et al . Emergence of blind areas in information spreading [J ] . Plos One , 2014 , 9 ( 4 ):e95785.

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