Please wait a minute...
Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (7): 111-125    DOI: 10.11925/infotech.2096-3467.2021.0140
Current Issue | Archive | Adv Search |
Quantifying and Examining Privacy Paradox of Social Media Users
Zhu Hou(),Fang Qingyan
School of Information Management, Sun Yat-Sen University, Guangzhou 510006, China
Download: PDF (1248 KB)   HTML ( 12
Export: BibTeX | EndNote (RIS)      
Abstract  

[Objective] This paper proposes a new model based on the traditional privacy calculus, aiming to effectively quantify the privacy paradox behaviors of social media users. [Methods] First, we quantified users’ information with the IRT model and grey relational analysis. Then, we built a model from the perspectives of the balanced benefits and risks. Third, we calculated and analyzed the equilibrium solution on social platform with this new model. Finally, we evaluated our model’s performance with some real-world users’ information. [Results] The perceived benefits of most social media users was higher than the perceived risks, which indicated the existence of privacy paradox and was in line with the real world situation. [Limitations] We did not fully examine the perceived benefit framework due to the lack of data. There is no proven standard for merging the proposed model’s two sections. [Conclusions] The proposed model supports the privacy paradox with objective data and lays a foundation for studying users’ privacy behaviors on social media.

Key wordsPrivacy Paradox      Privacy Calculus      IRT Model      Grey Relational Analysis      Entropy Weight Method     
Received: 09 February 2021      Published: 11 August 2021
ZTFLH:  G350  
Fund:Humanities and Social Science Research Youth Project of Ministry of Education(18YJC630272);National Natural Science Foundation of China(71801229);Basic and Applied Basic Research Foundation of Guangdong Province Natural Science Foundation General Project(2021A1515011805)
Corresponding Authors: Zhu Hou     E-mail: zhuhou3@mail.sysu.edu.cn

Cite this article:

Zhu Hou,Fang Qingyan. Quantifying and Examining Privacy Paradox of Social Media Users. Data Analysis and Knowledge Discovery, 2021, 5(7): 111-125.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2021.0140     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2021/V5/I7/111

项目1 项目2 项目i 项目n
用户1
用户2
用户j
用户N
Response Matrix Composed of User j and Item i
The Settings of 11 Privacy Items by 1 000 Weibo Users
序号 隐私项目 敏感度 序号 隐私项目 敏感度
1 认证 0.088 7 简介 0.320
2 所在地 0.141 8 个性域名 0.518
3 性取向 0.921 9 标签 0.330
4 感情状况 0.925 10 教育信息 0.716
5 生日 0.301 11 工作信息 0.741
6 血型 0.948
Sensitivity Statistics for Privacy Items
User Visibility Radar Chart for Privacy Items
PQj) Distribution Scatter Plot
序号 拟合函数 拟合度
1 线性函数 0.918
2 对数函数 0.820
3 倒数函数 0.106
4 二次函数 0.918
5 三次函数 0.962
6 复合函数
7 幂函数
8 S模型
9 增长模型
10 指数模型
11 对数模型
PQj) Curve Fitting Degree
未标准化系数 标准化系数
Beta
t Sig.
B 标准误
j 0.003 0.000 2.969 48.310 0.000
j2 -4.725E-006 0.000 -5.058 -34.304 0.000
j3 3.092E-009 0.000 3.150
常数 0.142 0.007 0.000
PQj) Cubic Function Fitting Coefficient
指标名称 指标含义
粉丝数 表示对该微博主选择关注设置的用户总数量
转发 表示用户将所浏览的信息分享到首页,希望能传递给其他用户
评论 表示用户对某一博主发布的微博内容的看法和意见
点赞 表示用户对某一博主发布的微博内容的赞赏等态度
The Meaning of Interactivity Index and Its Selection Basis
用户名称 粉丝数 前100
转发总数
前100
评论总数
前100
点赞总数
ACui阿崔 3 668 878 247 028 252 885 509 631
R1SE-周震南 8 427 334 17 149 147 938 087 4 811 405
ZAKER 2 040 674 1 185 637 23 773
阿云嘎Musical 2 141 614 1 290 383 1 097 895 3 840 222
陈欧 43 960 871 1 217 188 739 511 9 998 137
宠文铺子 25 155 75 6 648 1 036
郭姐哒 1 839 803 37 469 8 149 218 951
行走的一尾鱼 177 807 3 025 6 536 265 697
今天又懒得加班 695 959 147 293 437 819 3 574 359
金大川 2 146 006 215 219 16 117 1 435 614
康师傅冰红茶冰力十足 1 240 362 1 831 648 248 929 653 552
考研政治徐涛 3 496 055 206 444 248 397 1 403 348
李子柒 16 740 747 4 400 646 4 877 797 23 432 655
马頔-麻油叶 977 301 48 771 52 618 391 087
马玉兰还在害人 650 427 76 895 831 692 1 284 325
…… …… …… …… ……
Part of the User’s Raw Data
用户名称 粉丝数 平均
转发数
平均
评论数
平均
点赞数
ACui阿崔 3 668 878 2 470.28 2 528.85 5 096.31
R1SE-周震南 8 427 334 171 491.50 9 380.87 48 114.05
ZAKER 2 040 674 11.85 6.37 237.73
阿云嘎Musical 2 141 614 12 903.83 10 978.95 38 402.22
陈欧 43 960 871 12 171.88 7 395.11 99 981.37
宠文铺子 25 155 7.50 66.48 103.60
郭姐哒 1 839 803 374.69 814.90 2 189.51
行走的一尾鱼 177 807 302.50 653.60 2 656.97
今天又懒得加班 695 959 1 472.93 4 378.19 35 743.59
金大川 2 146 006 2 152.19 1 611.70 14 356.14
康师傅冰红茶冰力十足 1 240 362 18 316.48 2 489.29 6 535.52
考研政治徐涛 3 496 055 2 064.44 2 483.97 14 033.48
李子柒 16 740 747 44 006.46 48 777.97 234 326.60
马頔-麻油叶 977 301 487.71 526.18 3 910.87
马玉兰还在害人 650 427 768.95 8 316.92 12 843.25
…… …… …… …… ……
Indicator Data for Some Users
用户名称 粉丝数 平均
转发数
平均
评论数
平均
点赞数
ACui阿崔 0.002 561 0.002 705 0.003 708 0.001 346
R1SE-周震南 0.005 882 0.187 762 0.013 757 0.012 704
ZAKER 0.001 424 0.000 013 0.000 009 0.000 063
阿云嘎Musical 0.001 495 0.014 128 0.016 100 0.010 140
陈欧 0.030 685 0.013 327 0.010 845 0.026 399
宠文铺子 0.000 018 0.000 008 0.000 097 0.000 027
郭姐哒 0.001 284 0.000 410 0.001 195 0.000 578
行走的一尾鱼 0.000 124 0.000 331 0.000 958 0.000 702
今天又懒得加班 0.000 486 0.001 613 0.006 420 0.009 438
金大川 0.001 498 0.002 356 0.002 363 0.003 791
康师傅冰红茶冰力十足 0.000 866 0.020 054 0.003 650 0.001 726
考研政治徐涛 0.002 440 0.002 260 0.003 643 0.003 705
李子柒 0.011 685 0.048 182 0.071 531 0.061 871
马頔-麻油叶 0.000 682 0.000 534 0.000 772 0.001 033
马玉兰还在害人 0.000 454 0.000 842 0.012 196 0.003 391
…… …… …… …… ……
Intercept Part of Dimensionless Data
用户名称 |x0(1)-
xi(1)|
|x0(2)-
xi(2)|
|x0(3)-
xi(3)|
|x0(4)-
xi(4)|
ACui阿崔 0.056 706 0.185 057 0.067 823 0.060 525
R1SE-周震南 0.053 385 0 0.057 774 0.049 167
ZAKER 0.057 843 0.187 749 0.071 522 0.061 808
阿云嘎Musical 0.057 772 0.173 634 0.055 431 0.051 731
陈欧 0.028 582 0.174 435 0.060 686 0.035 472
宠文铺子 0.059 25 0.187 754 0.071 434 0.061 844
郭姐哒 0.057 983 0.187 352 0.070 336 0.061 293
行走的一尾鱼 0.059 143 0.187 431 0.070 573 0.061 169
今天又懒得加班 0.058 781 0.186 149 0.065 111 0.052 433
金大川 0.057 769 0.185 406 0.069 168 0.058 08
康师傅冰红茶冰力十足 0.058 401 0.167 708 0.067 881 0.060 145
考研政治徐涛 0.056 827 0.185 502 0.067 888 0.058 166
李子柒 0.047 582 0.139 58 0 0
马頔-麻油叶 0.058 585 0.187 228 0.070 759 0.060 838
马玉兰还在害人 0.058 813 0.186 92 0.059 335 0.058 48
…… …… …… …… ……
Intercept the Difference Between a Partial Reference Sequence and a Comparison Sequence
参考值/最值 粉丝数 平均
转发数
平均
评论数
平均
点赞数
max | x 0 ( j ) - x i ( j ) | 0.059 267 0.187 762 0.071 531 0.061 87
min | x 0 ( j ) - x i ( j ) | 0.000 00 0.000 00 0.000 00 0.000 00
maxmax | x 0 ( j ) - x i ( j ) | 0.187 762
minmin | x 0 ( j ) - x i ( j ) | 0.000 00
Grey Correlation Degree of Each Index Data
用户名称 Vi(1) Vi(2) Vi(3) Vi(4)
ACui阿崔 0.623 432 0.336 565 0.580 575 0.608 013
R1SE-周震南 0.637 493 1 0.619 042 0.656 290
ZAKER 0.618 762 0.333 349 0.567 590 0.603 003
阿云嘎Musical 0.619 050 0.350 937 0.628 758 0.644 733
陈欧 0.766 605 0.349 889 0.607 379 0.725 773
宠文铺子 0.613 078 0.333 343 0.567 893 0.602 866
郭姐哒 0.618 191 0.333 820 0.571 689 0.605 005
行走的一尾鱼 0.613 505 0.333 726 0.570 866 0.605 487
今天又懒得加班 0.614 958 0.335 253 0.590 478 0.641 639
金大川 0.619 062 0.336 146 0.575 786 0.617 795
康师傅冰红茶冰力十足 0.616 493 0.358 888 0.580 366 0.609 513
考研政治徐涛 0.622 933 0.336 030 0.580 338 0.617 449
李子柒 0.663 643 0.402 126 1 1
马頔-麻油叶 0.615 750 0.333 967 0.570 218 0.606 783
马玉兰还在害人 0.614 83 0.334 333 0.612 738 0.616 175
…… …… …… …… ……
Indicator Benefit Quantity of Part Users
指标 粉丝数 平均转发数 平均评论数 平均点赞数
ej 0.787 59 0.681 36 0.793 85 0.813 92
hj 0.212 41 0.318 64 0.206 15 0.186 08
wj 0.230 06 0.345 12 0.223 28 0.201 54
Information Entropy Value, Difference Coefficient and Information Entropy Weight of Each Index
用户名称 r i
ACui阿崔 0.511 8
R1SE-周震南 0.762 3
ZAKER 0.505 7
阿云嘎Musical 0.533 9
陈欧 0.579 0
宠文铺子 0.504 4
郭姐哒 0.507 0
行走的一尾鱼 0.505 8
今天又懒得加班 0.518 3
金大川 0.511 5
康师傅冰红茶冰力十足 0.518 1
考研政治徐涛 0.513 3
李子柒 0.716 3
马頔-麻油叶 0.506 5
马玉兰还在害人 0.517 8
…… ……
Intercept the Perceived Benefit of Some Users
Statistical Histogram of the User’s Perceived Risk
Pie Chart of Microblog Interactive Behavior Index Weight
Line Chart of Microblog User Perceived Benefit
Equilibrium Function Value Distribution of Microblog Users
[1] Li Z R, Lv T J, Zhang X H, et al. The Effects of Personal Characteristics and Interpersonal Influence on Privacy Information Diffusion in SNS[C]// Proceedings of 2013 IEEE International Conference on Service Operations and Logistics, and Informatics, Service Operations and Logistics, and Informatics. 2013: 413-418.
[2] 莫璐宇. 网站用户注册登录的影响因素和交互体验研究[D]. 杭州:浙江大学, 2019.
[2] (Mo Luyu. Research on the Influencing Factors and Interactive Experience of Website User Registration and Login[D]. Hangzhou: Zhejiang University, 2019.)
[3] 中国互联网信息中心. 第47次中国互联网络发展状况统计报告[R/OL]. [2021-02-03]. http://www.cnnic.cn/hlwfzyj/hlwxzbg/hlwtjbg/202102/t20210203_71361.htm.
[3] (China Internet Network Information Center. Statistical Report of the 47th China Internet Development[R/OL]. [2021-02-03]. http://www.cnnic.cn/hlwfzyj/hlwxzbg/hlwtjbg/202102/t20210203_71361.htm.)
[4] 刘婷, 邓胜利. 国外隐私悖论研究综述[J]. 信息资源管理学报, 2018, 8(2):106-114.
[4] (Liu Ting, Deng Shengli. A Review of Private Paradox Research Abroad[J]. Journal of Information Resources Management, 2018, 8(2):106-114.)
[5] Kokolakis S. Privacy Attitudes and Privacy Behaviour: A Review of Current Research on the Privacy Paradox Phenomenon[J]. Computers & Security, 2017, 64:122-134.
doi: 10.1016/j.cose.2015.07.002
[6] Lee H, Park H, Kim J. Why do People Share Their Context Information on Social Network Services? A Qualitative Study and an Experimental Study on Users’ Behavior of Balancing Perceived Benefit and Risk[J]. International Journal of Human-Computer Studies, 2013, 71(9):862-877.
doi: 10.1016/j.ijhcs.2013.01.005
[7] 唐晶晶. 微信大学生用户的隐私悖论行为研究[D]. 郑州:郑州大学, 2018.
[7] (Tang Jingjing. Research on Privacy Paradox in Weixin College Student User’s Behevior[D]. Zhengzhou: Zhengzhou University, 2018.)
[8] Pavuna A. Privacy Paradox: Empirical Verification of the Phenomenon[J]. Politicka Misao, 2019, 56(1):132-162.
[9] Kim B, Kim D. Understanding the Key Antecedents of Users’ Disclosing Behaviors on Social Networking Sites: The Privacy Paradox[J]. Sustainability, 2020, 12(12):5163.
doi: 10.3390/su12125163
[10] Trepte S, Dienlin T, Reinecke L. Privacy, Self‐Disclosure, Social Support, and Social Network Site Use. Research Report of a Three‐year Panel Study[EB/OL]. [2013-12-11]. http://opus.uni-hohenheim.de/volltexte/2013/889/.
[11] Trepte S, Dienlin T, Reinecke L. From the Gutenberg Galaxy to the Google Galaxy[M]. Uvk Verlag, 2014, 3:225-244.
[12] 李贺, 余璐, 许一明, 等. 解释水平理论视角下的社交网络隐私悖论研究[J]. 情报学报, 2018, 37(1):1-13.
[12] (Li He, Yu Lu, Xu Yiming, et al. Research on Privacy Paradox in Social Network Sites Under the Perspective of Construal Level Theory[J]. Journal of the China Society for Scientific and Technical Information, 2018, 37(1):1-13.)
[13] 朱侯, 王可, 严芷君, 等. 基于隐私计算理论的SNS用户隐私悖论现象研究[J]. 情报杂志, 2017, 36(2):134-139, 125.
[13] (Zhu Hou, Wang Ke, Yan Zhijun, et al. An Analysis of Privacy Paradox Phenomenon in SNS Users Based on Privacy Calculus[J]. Journal of the China Society for Scientific and Technical Information, 2017, 36(2):134-139, 125.)
[14] Davazdahemami B, Hammer B, Kalgotra P, et al. From General to Situational Privacy Concerns: A New Mechanism to Explain Information Disclosure in Social Networks[J]. Communications of the Association for Information Systems, 2020, 47(1):652-677.
doi: 10.17705/1CAIS
[15] 管家娃, 张玥, 朱庆华, 等. 国外社交网站隐私悖论问题研究综述与国内研究建议[J]. 图书情报工作, 2016, 60(22):126-134.
[15] (Guan Jiawa, Zhang Yue, Zhu Qinghua, et al. Review of Foreign Research on the Privacy Paradox in Social Network Sites and Suggestions for Domestic Research[J]. Library and Information Service, 2016, 60(22):126-134.)
[16] Acquisti A, Grossklags J. Privacy and Rationality in Individual Decision Making[J]. IEEE Security & Privacy, 2005, 3(1):26-33.
[17] Barnes S B. A Privacy Paradox: Social Networking in the United States[J]. First Monday, 2016, 11(9):5.
[18] Waldman A E. Cognitive Biases, Dark Patterns, and the ‘Privacy Paradox’[J]. Current Opinion in Psychology, 2020, 31:105-109.
doi: S2352-250X(19)30148-4 pmid: 31590106
[19] Ma L, Ding X Y, Zhang X, et al. Mobile Users’ Self-Disclosure Behaviour on WeChat: Application of Social Cognitive Theory[J]. Mobile Information Systems, 2020(7):1-13.
[20] 谢卫红, 常青青, 李忠顺. 国外网络隐私悖论研究进展——理论整合与述评[J]. 现代情报, 2018, 38(11):136-144.
[20] (Xie Weihong, Chang Qingqing, Li Zhongshun. Advances in Foreign Online Privacy Paradox——The Integration of Theory and Commentary[J]. Journal of Modern Information in China, 2018, 38(11):136-144.)
[21] Lindqvist J, Cranshaw J, Wiese J, et al. I’m the Mayor of My House: Examining Why People Use Foursquare - A Social-Driven Location Sharing Application[C]// Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 2011: 2409-2418.
[22] Seidman G. Self-presentation and Belonging on Facebook: How Personality Influences Social Media Use and Motivations[J]. Personality and Individual Differences, 2013, 54(3):402-407.
doi: 10.1016/j.paid.2012.10.009
[23] Wu P F. The Privacy Paradox in the Context of Online Social Networking: A Self-Identity Perspective[J]. Journal of the Association for Information Science and Technology, 2019, 70(3):207-217.
doi: 10.1002/asi.2019.70.issue-3
[24] Wu Y, Pan L. SG-PAC: A Stochastic Game Approach to Generate Personal Privacy Paradox Access-Control Policies in Social Networks[J]. Computers & Security, 2020, 102:102157.
doi: 10.1016/j.cose.2020.102157
[25] Li H, Luo X, Zhang J, et al. Resolving the Privacy Paradox: Toward a Cognitive Appraisal and Emotion Approach to Online Privacy Behaviors[J]. Information & Management, 2017, 54(8):1012-1022.
doi: 10.1016/j.im.2017.02.005
[26] Metzger M J. Communication Privacy Management in Electronic Commerce[J]. Journal of Computer-Mediated Communication Electronic Edition, 2007, 12(2):335-361.
[27] Liu X D, Min Q F, Han S N. Understanding Users’ Continuous Content Contribution Behaviours on Microblogs: An Integrated Perspective of Uses and Gratification Theory and Social Influence Theory[J]. Behaviour & Information Technology, 2019, 39(1):525-543.
[28] 孙保营, 唐晶晶. 移动社交时代“隐私悖论”的困局及破解[J]. 新闻爱好者, 2017(7):13-18.
[28] (Sun Baoying, Tang Jingjing. The Dilemma and Solution of “Privacy Paradox” in Mobile Social Era[J]. Journalism Lover in China, 2017(7):13-18.)
[29] Deuker A. Addressing the Privacy Paradox by Expanded Privacy Awareness - The Example of Context-Aware Services[C]// Proceedings of IFIP International Federation for Information Processing. 2010.
[30] Sun Q Z, Willemsen M C, Knijnenburg B P. Unpacking the Intention-Behavior Gap in Privacy Decision Making for the Internet of Things (IoT) Using Aspect Listing[J]. Computers & Security, 2020, 97:101924.
doi: 10.1016/j.cose.2020.101924
[31] Durnell E, Okabe-Miyamoto K, Howell R T, et al. Online Privacy Breaches, Offline Consequences: Construction and Validation of the Concerns with the Protection of Informational Privacy Scale[J]. International Journal of Human-Computer Interaction, 2020, 36(19):1834-1848.
doi: 10.1080/10447318.2020.1794626
[32] Krasnova H, Veltri N F. Privacy Calculus on Social Networking Sites: Explorative Evidence from Germany and USA[C]// Proceedings of the 43rd Hawaii International Conference on System Sciences. 2010: 1-10.
[33] Awad N F, Krishnan M S. The Personalization Privacy Paradox: An Empirical Evaluation of Information Transparency and the Willingness to Be Profiled Online for Personalization[J]. MIS Quarterly, 2006, 30(1):13-28.
doi: 10.2307/25148715
[34] Liu K, Terzi E. A Framework for Computing the Privacy Scores of Users in Online Social Networks[C]// Proceedings of the 9th IEEE International Conference on Data Mining. 2009: 288-297.
[35] 邓聚龙. 灰理论基础[M]. 武汉: 华中科技大学出版社, 2002.
[35] (Deng Julong. Foundation of Grey Theory[M]. Wuhan: Huazhong University of Science & Technology Press, 2002.)
[36] 程启月. 评测指标权重确定的结构熵权法[J]. 系统工程理论与实践, 2010, 30(7):1225-1228.
[36] (Cheng Qiyue. Structure Entropy Weight Method to Confirm the Weight of Evaluating Index[J]. Systems Engineering-Theory & Practice, 2010, 30(7):1225-1228.)
[37] 朱涵钰, 吴联仁, 吕廷杰. 社交网络用户隐私量化研究:建模与实证分析[J]. 清华大学学报(自然科学版), 2014, 54(3):402-406.
[37] (Zhu Hanyu, Wu Lianren, Lv Tingjie. Research on Quantifying User Privacy on Social Networking Sites[J]. Journal of Tsinghua University(Science and Technology), 2014, 54(3):402-406.)
[38] De S J, Imine A. Enabling Users to Balance Social Benefit and Privacy in Online Social Networks[C]// Proceedings of 2018 16th Annual Conference on Privacy, Security and Trust. DOI: 10.1109/PST.2018.8514202.
doi: 10.1109/PST.2018.8514202
[39] Yang M, Yu Y J, Bandara A K, et al. Adaptive Sharing for Online Social Networks: A Trade-off Between Privacy Risk and Social Benefit[C]// Proceedings of 2014 IEEE 13th International Conference on Trust, Security and Privacy in Computing and Communications. 2014: 45-52.
[40] 王玲. 基于微博用户的行为分析与应用研究[D]. 广州:华南理工大学, 2018.
[40] (Wang Ling. The Study of Behavior Analysis and Application Based on Microblog User[D]. Guangzhou: South China University of Technology, 2018.)
[1] Shi Yutian,Zhu Qinghua,Zhao Yuxiang,Chen Xiaowei. Evaluating the Influence of China’s Webcast Platforms Based on Link Analysis[J]. 数据分析与知识发现, 2017, 1(9): 40-48.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn