IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v13y2021i9p236-d637182.html
   My bibliography  Save this article

Preference for Number of Friends in Online Social Networks

Author

Listed:
  • Fanhui Meng

    (School of Systems Science and Engineering, Sun Yat-Sen University, Guangzhou 510006, China
    These authors contributed equally to this work.)

  • Haoming Sun

    (School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou 510006, China
    These authors contributed equally to this work.)

  • Jiarong Xie

    (School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou 510006, China)

  • Chengjun Wang

    (School of Journalism and Communication, Computational Communication Collaboratory, Nanjing University, Nanjing 210093, China)

  • Jiajing Wu

    (School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou 510006, China)

  • Yanqing Hu

    (Department of Statistics and Data Science, College of Science, Southern University of Science and Technology, Shenzhen 518055, China)

Abstract

Preferences or dislikes for specific numbers are ubiquitous in human society. In traditional Chinese culture, people show special preference for some numbers, such as 6, 8, 10, 100, 200, etc. By analyzing the data of 6.8 million users of Sina Weibo, one of the largest online social media platforms in China, we discover that users exhibit a distinct preference for the number 200, i.e., a significant fraction of users prefer to follow 200 friends. This number, which is very close to the Dunbar number that predicts the cognitive limit on the number of stable social relationships, motivates us to investigate how the preference for numbers in traditional Chinese culture is reflected on social media. We systematically portray users who prefer 200 friends and analyze their several important social features, including activity, popularity, attention tendency, regional distribution, economic level, and education level. We find that the activity and popularity of users with the preference for the number 200 are relatively lower than others. They are more inclined to follow popular users, and their social portraits change relatively slowly. Besides, users who have a stronger preference for the number 200 are more likely to be located in regions with underdeveloped economies and education. That indicates users with the preference for the number 200 are likely to be vulnerable groups in society and are easily affected by opinion leaders.

Suggested Citation

  • Fanhui Meng & Haoming Sun & Jiarong Xie & Chengjun Wang & Jiajing Wu & Yanqing Hu, 2021. "Preference for Number of Friends in Online Social Networks," Future Internet, MDPI, vol. 13(9), pages 1-13, September.
  • Handle: RePEc:gam:jftint:v:13:y:2021:i:9:p:236-:d:637182
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/13/9/236/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1999-5903/13/9/236/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Weng, Pei-Shih, 2018. "Lucky issuance: The role of numerological superstitions in irrational return premiums," Pacific-Basin Finance Journal, Elsevier, vol. 47(C), pages 79-91.
    2. Matthew Spradling & Jeremy Straub & Jay Strong, 2021. "Protection from ‘Fake News’: The Need for Descriptive Factual Labeling for Online Content," Future Internet, MDPI, vol. 13(6), pages 1-26, May.
    3. Steven C. Bourassa & Vincent S. Peng, 1999. "Hedonic Prices and House Numbers: The Influence of Feng Shui," International Real Estate Review, Global Social Science Institute, vol. 2(1), pages 79-93.
    4. repec:cup:judgdm:v:16:y:2021:i:4:p:1060-1071 is not listed on IDEAS
    5. Shum, Matthew & Sun, Wei & Ye, Guangliang, 2014. "Superstition and “lucky” apartments: Evidence from transaction-level data," Journal of Comparative Economics, Elsevier, vol. 42(1), pages 109-117.
    6. Jain, Gaurav & Gaeth, Gary J. & Nayakankuppam, Dhananjay & Levin, Irwin P., 2020. "Revisiting attribute framing: The impact of number roundedness on framing," Organizational Behavior and Human Decision Processes, Elsevier, vol. 161(C), pages 109-119.
    7. Matteo Cinelli & Emanuele Brugnoli & Ana Lucia Schmidt & Fabiana Zollo & Walter Quattrociocchi & Antonio Scala, 2020. "Selective exposure shapes the Facebook news diet," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-17, March.
    8. Jiarong Xie & Fanhui Meng & Jiachen Sun & Xiao Ma & Gang Yan & Yanqing Hu, 2021. "Detecting and modelling real percolation and phase transitions of information on social media," Nature Human Behaviour, Nature, vol. 5(9), pages 1161-1168, September.
    9. Yanqing Hu & Shenggong Ji & Yuliang Jin & Ling Feng & H. Eugene Stanley & Shlomo Havlin, 2018. "Local structure can identify and quantify influential global spreaders in large scale social networks," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 115(29), pages 7468-7472, July.
    10. Uwe Hassler & Marc‐Oliver Pohle, 2022. "Unlucky Number 13? Manipulating Evidence Subject to Snooping," International Statistical Review, International Statistical Institute, vol. 90(2), pages 397-410, August.
    11. Elena B. Pokryshevskaya & Evgeny A. Antipov, 2015. "A study of numerological superstitions in the apartments market," Economics Bulletin, AccessEcon, vol. 35(1), pages 83-88.
    12. Eric J. Hamerman & Gita V. Johar, 2013. "Conditioned Superstition: Desire for Control and Consumer Brand Preferences," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 40(3), pages 428-443.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wen-Chieh Wu & Yu-Chun Ma & Steven C. Bourassa, 2018. "Folk Customs and Home Improvement Decisions," International Real Estate Review, Global Social Science Institute, vol. 21(3), pages 317-341.
    2. Dmitry Burakov, 2018. "Do discounts mitigate numerological superstitions? Evidence from the Russian real estate market," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 13(5), pages 467-470, September.
    3. Woei-Chyuan Wong & Nur Adiana Hiau Abdullah & Hock-Eam Lim, 2019. "The Value Of Chinese Superstitions In Malaysia: Evidence From Car Plate Auctioning," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 64(01), pages 115-137, March.
    4. repec:cup:judgdm:v:13:y:2018:i:5:p:467-470 is not listed on IDEAS
    5. Elena B. Pokryshevskaya & Evgeny A. Antipov, 2015. "A study of numerological superstitions in the apartments market," Economics Bulletin, AccessEcon, vol. 35(1), pages 83-88.
    6. Tao Chen & Andreas Karathanasopoulos & Stanley Iat-Meng Ko & Chia Chun Lo, 2020. "Lucky lots and unlucky investors," Review of Quantitative Finance and Accounting, Springer, vol. 54(2), pages 735-751, February.
    7. repec:cup:judgdm:v:10:y:2015:i:6:p:590-592 is not listed on IDEAS
    8. Sun, Jiachen & Feng, Ling & Du, Mingwei & Ma, Xiao & Fan, Zhengping & Gloor, Peter & Hu, Yanqing, 2021. "Ultra-efficient information detection on large-scale online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    9. Nicole M. Fortin & Andrew J. Hill & Jeff Huang, 2014. "Superstition In The Housing Market," Economic Inquiry, Western Economic Association International, vol. 52(3), pages 974-993, July.
    10. Evgeny A. Antipov & Elena B. Pokryshevskaya, 2015. "Are buyers of apartments superstitious? Evidence from the Russian real estate market," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 10(6), pages 590-592, November.
    11. Kwong Wing Chau & Danika Wright & Ervi Liusman, 2018. "The cost of a lucky price," ERES eres2018_240, European Real Estate Society (ERES).
    12. Brad R. Humphreys & Adam Nowak & Yang Zhou, 2016. "Cultural Superstitions and Residential Real Estate Prices: Transaction-level Evidence from the US Housing Market," Working Papers 16-27, Department of Economics, West Virginia University.
    13. Jan Fidrmuc & J. D. Tena, 2015. "Friday the 13th: The Empirics of Bad Luck," Kyklos, Wiley Blackwell, vol. 68(3), pages 317-334, August.
    14. repec:cup:judgdm:v:11:y:2016:i:3:p:243-259 is not listed on IDEAS
    15. Tong V. Wang & Rogier J. D. Potter van Loon & Martijn J. van den Assem & Dennie van Dolder, 2016. "Number preferences in lotteries," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 11(3), pages 243-259, May.
    16. Xue Guo & Hu Zhang & Tianhai Tian, 2019. "Multi-Likelihood Methods for Developing Stock Relationship Networks Using Financial Big Data," Papers 1906.08088, arXiv.org.
    17. Lan, Hao & Moreira, Fernando & Zhao, Sheng, 2023. "Can a house resale restriction policy curb speculation? Evidence from a quasi-natural experiment in China," International Review of Economics & Finance, Elsevier, vol. 83(C), pages 841-859.
    18. Liang, Yuan & Qi, Mingze & Huangpeng, Qizi & Duan, Xiaojun, 2023. "Percolation of interlayer feature-correlated multiplex networks," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    19. Yu, Senbin & Gao, Liang & Xu, Lida & Gao, Zi-You, 2019. "Identifying influential spreaders based on indirect spreading in neighborhood," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 418-425.
    20. Agarwal, Sumit & He, Jia & Liu, Haoming & Png, I. P. L. & Sing, Tien Foo & Wong, Wei-Kang, 2016. "Superstition, Conspicuous Spending, and Housing Markets: Evidence from Singapore," IZA Discussion Papers 9899, Institute of Labor Economics (IZA).
    21. Terence tai-leung Chong & Angela Fung & Wing-ting Lee & Ka-lai Man, 2009. "Hedonic pricing models for metropolitan bus services," Economics Bulletin, AccessEcon, vol. 29(2), pages 630-637.
    22. De Paola, Maria & Gioia, Francesca & Scoppa, Vincenzo, 2014. "Overconfidence, omens and gender heterogeneity: Results from a field experiment," Journal of Economic Psychology, Elsevier, vol. 45(C), pages 237-252.
    23. Liu, Yun & Zhang, Yifei & Chen, Xin & Yang, Yuxin, 2021. "Superstition and farmers’ life insurance spending," Economics Letters, Elsevier, vol. 206(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:13:y:2021:i:9:p:236-:d:637182. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.