营销科学学报

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原创还是转发——基于社交媒体UGC的交互效用研究

周静 ,沈俏蔚 ,涂平 ,王汉生   

  1. 周静,中国人民大学统计学院,讲师,Email:zhoujing_89@126.com
    沈俏蔚,北京大学光华管理学院市场营销系,教授,Email:qshen@gsm.pku.edu.cn
    涂平,北京大学光华管理学院市场营销系,教授,Email:tuping@gsm.pku.edu.cn
    王汉生,北京大学光华管理学院商务统计与经济计量系,教授,Email:hansheng@pku.edu.cn
  • 出版日期:2017-12-30 发布日期:2018-11-09
  • 基金资助:

    本研究得到国家自然科学基金项目“原创还是转发?社交网络视角下的UGC产生动机研究”(71702185)的资助。

Tweet or Retweet?Interaction Utility Derived from User Generated Content in Social Media

Zhou Jing, Shen Qiaowei, Tu Ping, Wang Hansheng   

  1. Zhou Jing, School of Statistics, Renmin University of China
    Shen Qiaowei, Guanghua School of Management, Peking University
    Tu Ping, Guanghua School of Management, Peking University
    Wang Hansheng,Guanghua School of Management, Peking University
  • Online:2017-12-30 Published:2018-11-09

摘要:

在线用户创造内容(User Generated Content,UGC)已经成为人们在社交平台上进行交流与信息分享的主要方式。高质量的UGC能够吸引更多的广告主以及为平台带来可观的收入。因此,如何鼓励人们在社交平台上贡献优质的内容已成为研究人员关心的重要问题之一。用户为何发帖,产生UGC的动机是什么,已经受到越来越多学者的关注。本文提出了一个全面的效用理论模型用于研究用户的发帖动机。在已有的内在效用和形象效用的基础上,本研究提出了交互效用的概念,具体的,交互效用是指用户通过与社交平台上的好友进行互动而获得的效用。其次,本文在效用最优化的方程中加入了时间约束这一条件,通过该约束条件,可以进一步分析用户是如何分配发帖与阅读他人帖子的时间。最后本文用新浪微博的数据对理论模型进行了实证检验。

关键词: 社交网络, 交互效用, UGC, 负二项回归

Abstract:

User generated content (UGC) is becoming a dominating way for people to communicate and share with each other on social platforms. For platform providers, a good UGC performance can attract more advertisers and directly influence their revenue. Therefore, how to encourage people to contribute content becomes a problem of interest. However, the underlying motivation on user generated content is still not well understood. Therefore, we propose in this paper a more comprehensive utility framework. Combining with previous intrinsic and image-related utility, we propose an interaction utility concept to describe the utility that users derive from interacting with their friends. Then incorporating interaction utility is our first contribution in this paper. As our second contribution, we exert a time budget constraint in our utility framework and this enables us to evaluate the tradeoff between generating and consuming content. Finally, a Sina Weibo dataset is used for illustration.

Key words: Social Networks, Interaction Utility, User Generated Content, Negative Binomial Regression