IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i6p2217-d331812.html
   My bibliography  Save this article

Research on User Influence Model Integrating Personality Traits under Strong Connection

Author

Listed:
  • Chunhua Ju

    (Department of Modern Business Research Center, Zhejiang Gongshang University, Hangzhou 310018, China
    School of Management Science & Engineering, Zhejiang Gongshang University, Hangzhou 310018, China)

  • Qiuyang Gu

    (Department of Modern Business Research Center, Zhejiang Gongshang University, Hangzhou 310018, China
    School of Management Science & Engineering, Zhejiang Gongshang University, Hangzhou 310018, China)

  • Yi Fang

    (School of Management Science & Engineering, Zhejiang Gongshang University, Hangzhou 310018, China)

  • Fuguang Bao

    (Department of Modern Business Research Center, Zhejiang Gongshang University, Hangzhou 310018, China
    School of Management Science & Engineering, Zhejiang Gongshang University, Hangzhou 310018, China)

Abstract

User influence has always been a major topic in the field of social networking. At present, most of the research focuses on three aspects: topological structure, social-behavioral dimension, and topic dimension and most of them ignore the difference between the audience. These models do not consider the impact of personality differences on user influences. To meet this need, this paper introduces the personality traits factor and proposes a user influence model which integrates personality traits (IPUIM) under a strong connection. The user influence measurement is constructed through the information dimension, structural dimension, and user behavioral dimension. The personality report of the user group is obtained by means of NEO-PI-R (The big five personality inventory, Chinese edition) and machine learning method, and it is integrated into the user influence model. The experiment proves that the model proposed in this paper has good accuracy and applicability in measuring user influence, and can effectively identify the key opinion leaders of different personality trait clusters.

Suggested Citation

  • Chunhua Ju & Qiuyang Gu & Yi Fang & Fuguang Bao, 2020. "Research on User Influence Model Integrating Personality Traits under Strong Connection," Sustainability, MDPI, vol. 12(6), pages 1-15, March.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:6:p:2217-:d:331812
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/6/2217/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/6/2217/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Frenzen, Jonathan & Nakamoto, Kent, 1993. "Structure, Cooperation, and the Flow of Market Information," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 20(3), pages 360-375, December.
    2. Dina Mayzlin, 2006. "Promotional Chat on the Internet," Marketing Science, INFORMS, vol. 25(2), pages 155-163, 03-04.
    3. H Andrew Schwartz & Johannes C Eichstaedt & Margaret L Kern & Lukasz Dziurzynski & Stephanie M Ramones & Megha Agrawal & Achal Shah & Michal Kosinski & David Stillwell & Martin E P Seligman & Lyle H U, 2013. "Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-16, September.
    4. Duncan J. Watts & Peter Sheridan Dodds, 2007. "Influentials, Networks, and Public Opinion Formation," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 34(4), pages 441-458, May.
    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. Sinan Aral & Dylan Walker, 2011. "Creating Social Contagion Through Viral Product Design: A Randomized Trial of Peer Influence in Networks," Management Science, INFORMS, vol. 57(9), pages 1623-1639, February.
    2. Reckmann, Tobias, 2017. "Intellectual Structure and Emancipation of Word of Mouth Research: A Bibliometric Analysis of a Multidisciplinary Research Field," EconStor Preprints 179913, ZBW - Leibniz Information Centre for Economics.
    3. Pescher, Christian & Spann, Martin, 2014. "Relevance of actors in bridging positions for product-related information diffusion," Journal of Business Research, Elsevier, vol. 67(8), pages 1630-1637.
    4. Yogesh V. Joshi & Andres Musalem, 2021. "When Consumers Learn, Money Burns: Signaling Quality via Advertising with Observational Learning and Word of Mouth," Marketing Science, INFORMS, vol. 40(1), pages 168-188, January.
    5. Stephen, Andrew T. & Lehmann, Donald R., 2016. "How word-of-mouth transmission encouragement affects consumers' transmission decisions, receiver selection, and diffusion speed," International Journal of Research in Marketing, Elsevier, vol. 33(4), pages 755-766.
    6. Amy Pei & Dina Mayzlin, 2022. "Influencing Social Media Influencers Through Affiliation," Marketing Science, INFORMS, vol. 41(3), pages 593-615, May.
    7. Scarpi, Daniele, 2010. "Does Size Matter? An Examination of Small and Large Web-Based Brand Communities," Journal of Interactive Marketing, Elsevier, vol. 24(1), pages 14-21.
    8. Edgardo Arturo Ayala Gaytán, 2009. "Social network externalities and price dispersion in online markets," Ensayos Revista de Economia, Universidad Autonoma de Nuevo Leon, Facultad de Economia, vol. 0(2), pages 1-28, November.
    9. René Algesheimer & Sharad Borle & Utpal M. Dholakia & Siddharth S. Singh, 2010. "The Impact of Customer Community Participation on Customer Behaviors: An Empirical Investigation," Marketing Science, INFORMS, vol. 29(4), pages 756-769, 07-08.
    10. Jean M. Twenge & Hannah VanLandingham & W. Keith Campbell, 2017. "The Seven Words You Can Never Say on Television: Increases in the Use of Swear Words in American Books, 1950-2008," SAGE Open, , vol. 7(3), pages 21582440177, August.
    11. Irina Heimbach & Oliver Hinz, 2018. "The Impact of Sharing Mechanism Design on Content Sharing in Online Social Networks," Information Systems Research, INFORMS, vol. 29(3), pages 592-611, September.
    12. Silvio Vismara, 2018. "Information Cascades among Investors in Equity Crowdfunding," Entrepreneurship Theory and Practice, , vol. 42(3), pages 467-497, May.
    13. Inyoung Chae & Andrew T. Stephen & Yakov Bart & Dai Yao, 2017. "Spillover Effects in Seeded Word-of-Mouth Marketing Campaigns," Marketing Science, INFORMS, vol. 36(1), pages 89-104, January.
    14. Khim-Yong Goh & Cheng-Suang Heng & Zhijie Lin, 2013. "Social Media Brand Community and Consumer Behavior: Quantifying the Relative Impact of User- and Marketer-Generated Content," Information Systems Research, INFORMS, vol. 24(1), pages 88-107, March.
    15. Peres, Renana & Muller, Eitan & Mahajan, Vijay, 2010. "Innovation diffusion and new product growth models: A critical review and research directions," International Journal of Research in Marketing, Elsevier, vol. 27(2), pages 91-106.
    16. Vincent Labatut & Jean-Michel Balasque, 2010. "Business-oriented Analysis of a Social Network of University Students," Post-Print hal-00633643, HAL.
    17. Antoine Loeper & Jakub Steiner & Colin Stewart, 2014. "Influential Opinion Leaders," Economic Journal, Royal Economic Society, vol. 124(581), pages 1147-1167, December.
    18. Gallus, Jana & Bhatia, Sudeep, 2020. "Gender, power and emotions in the collaborative production of knowledge: A large-scale analysis of Wikipedia editor conversations," Organizational Behavior and Human Decision Processes, Elsevier, vol. 160(C), pages 115-130.
    19. Kreindler, Gabriel E. & Young, H. Peyton, 2013. "Fast convergence in evolutionary equilibrium selection," Games and Economic Behavior, Elsevier, vol. 80(C), pages 39-67.
    20. Juan Shi & Kin Keung Lai & Ping Hu & Gang Chen, 2018. "Factors dominating individual information disseminating behavior on social networking sites," Information Technology and Management, Springer, vol. 19(2), pages 121-139, June.

    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:jsusta:v:12:y:2020:i:6:p:2217-:d:331812. 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.