Repository logo
  • English
  • 中文
Log In
  1. Home
 
  • Details

Modeling content and membership growth dynamics of user-generated content sharing networks with two case studies

Journal
IEEE Access
Journal Volume
6
Pages
4779-4796
Date Issued
2018
Author(s)
Chen, R.-H.
SHI-CHUNG CHANG 
DOI
10.1109/ACCESS.2017.2789334
URI
https://scholars.lib.ntu.edu.tw/handle/123456789/499222
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040056235&doi=10.1109%2fACCESS.2017.2789334&partnerID=40&md5=650253bee548c84d9660d2046c11d54c
Abstract
User-generated content sharing networks (UGCSNets), in which members are content contributors as well as users, have had a significant impact on the sharing economy and on society via the sharing and reuse of contents. In a UGCSNet, managing for growth requires a quantitative grasp of how individual members' participation and sharing affect and are affected by the membership and content volume; these interactions form a dynamic loop. In this paper, a quantitative modeling approach for the loop dynamics of UGCSNet growth is developed by exploiting limited empirical data. A teaching material sharing network serves as a baseline case study, and Wikipedia serves as a validation case for the modeling approach design. The novel modeling approach consists of 1) set of generalized bass diffusion model-embedded stochastic difference equations (GBDSDEs) of the loop dynamics and 2) a quasi-bootstrap-based nonlinear least square method to extract from the limited empirical data and periodically update the model parameters as the UGCSNet evolves. In GBDSDEs, two difference equations describe the number of members and content volume evolution. The stochastic drives consist of measures of individual participation and content uploading. The drive models are an innovative generalization of the bass diffusion model as probabilistic models of known qualitative descriptions regarding how the individual willingness to participate and share is affected by the total membership and content volume. Analyses of the coefficients of determination show good fits between model predictions and actual outcomes for both Smart Creative Teachers Net and Wikipedia growths. Applications of the modeling approach to what-if analyses demonstrate its value to predict and assess the effects of specific managerial strategies - such as the initial content volume and the number of founding altruistic members - on the growth of a UGCSNet. © 2018 IEEE.
Subjects
Bass diffusion model; Growth dynamics; Network state-dependent generalization; Positive feedback to individual; Quantitative model; Sharing network; User-generated content
Other Subjects
Data structures; Difference equations; Diffusion; Digital storage; Dynamics; Electronic publishing; Least squares approximations; Mathematical models; Nonlinear equations; Stochastic systems; Bass Diffusion Model; Encyclopedias; Growth dynamics; Network state; Quantitative modeling; Sharing network; Solid model; User-generated content; Stochastic models
Type
journal article

臺大位居世界頂尖大學之列,為永久珍藏及向國際展現本校豐碩的研究成果及學術能量,圖書館整合機構典藏(NTUR)與學術庫(AH)不同功能平台,成為臺大學術典藏NTU scholars。期能整合研究能量、促進交流合作、保存學術產出、推廣研究成果。

To permanently archive and promote researcher profiles and scholarly works, Library integrates the services of “NTU Repository” with “Academic Hub” to form NTU Scholars.

總館學科館員 (Main Library)
醫學圖書館學科館員 (Medical Library)
社會科學院辜振甫紀念圖書館學科館員 (Social Sciences Library)

開放取用是從使用者角度提升資訊取用性的社會運動,應用在學術研究上是透過將研究著作公開供使用者自由取閱,以促進學術傳播及因應期刊訂購費用逐年攀升。同時可加速研究發展、提升研究影響力,NTU Scholars即為本校的開放取用典藏(OA Archive)平台。(點選深入了解OA)

  • 請確認所上傳的全文是原創的內容,若該文件包含部分內容的版權非匯入者所有,或由第三方贊助與合作完成,請確認該版權所有者及第三方同意提供此授權。
    Please represent that the submission is your original work, and that you have the right to grant the rights to upload.
  • 若欲上傳已出版的全文電子檔,可使用Open policy finder網站查詢,以確認出版單位之版權政策。
    Please use Open policy finder to find a summary of permissions that are normally given as part of each publisher's copyright transfer agreement.
  • 網站簡介 (Quickstart Guide)
  • 使用手冊 (Instruction Manual)
  • 線上預約服務 (Booking Service)
  • 方案一:臺灣大學計算機中心帳號登入
    (With C&INC Email Account)
  • 方案二:ORCID帳號登入 (With ORCID)
  • 方案一:定期更新ORCID者,以ID匯入 (Search for identifier (ORCID))
  • 方案二:自行建檔 (Default mode Submission)
  • 方案三:學科館員協助匯入 (Email worklist to subject librarians)

Built with DSpace-CRIS software - Extension maintained and optimized by 4Science