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    政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 >  Item 140.119/35250


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    題名: Web 2.0中的群體智慧價值創造──以社會性書籤網站為例
    Web 2.0 Collective Wisdom Creation – Case Study on Social Bookmarking Sites
    作者: 翁榮暉
    Weng, Jung Hui
    貢獻者: 苑守慈
    Yuan, Soe Tsyr
    翁榮暉
    Weng, Jung Hui
    關鍵詞: 社會性書籤網站
    群體智慧
    代理人模擬
    Social Bookmarking Site
    Collective Wisdom
    Agent-Based Modeling and Simulation
    日期: 2007
    上傳時間: 2009-09-18 14:32:58 (UTC+8)
    摘要: Web 2.0時代強調由使用者貢獻內容，並藉由使用者的互動來創造群體智慧的價值。社會性書籤網站統合散佈在各處的網路資訊（尤其是由使用者所產生的部落格文章），承接內容的生產及閱讀，是網路內容價值鏈樞紐；另一方面，從媒體的角度來看，書籤網站可視為是web 2.0下的公民新聞守門人（引路人），以公民取代專業編輯，提供了一個完全不一樣的公民媒體運作方式。本研究針對社會性書籤網站中的內容評價推薦機制，探討其群體智慧運作情形：參考動物群體行為的運作原則，加上文獻的整理及實際案例的觀察，建構出社會性書籤網站推薦機制的模擬運作架構；並透過代理人模擬方法，來找出影響網站群體智慧運作的原則，及相關屬性設定對運作結果的影響。研究結果發現，社會性書籤網站的運作成效，可以分為篩選效果及文章更新效率，兩者之間具有魚與熊掌不可兼得的特性，並可藉由不同的閱讀策略安排來調整。基於web 2.0的特性，使用者同時扮演服務的生產者與消費者。因此，使用者閱讀文章時的閱讀策略安排，可視為是群體智慧運作中的工作分配策略。而群體智慧的運作原則中，正回饋效應可以提升篩選效果，判斷獨立性可以提升文章的更新效率，抑制與負回饋則可以使系統較為穩定。本研究除了為web 2.0網站的群體智慧經營提供具體的參考方針，多重代理人模擬的方法也可做為往後web 2.0相關研究及網站經營時的工具。
    The core spirit for web 2.0 is the contribution of users, and the creation of value through the interaction between users. Social book marking sites integrate all kind of contents on the Internet (especially those generated by users), and play the role of pivot between content production and consumption. From the aspect of media, social bookmarking site can be regarded as news gatekeeper (or gateway) in the web 2.0 era. This study focuses on the rating and recommendation mechanism of social bookmarking sites, trying to find out the effects of collective wisdom with regard to different operations. The principle of collective animal behavior and the existing operations of some social bookmarking sites are first surveyed. Then, an operational model of social bookmarking sites and its recommendation mechanism is built and used for subsequent simulation.<br>The research findings show that the performance of social bookmarking sites has a tradeoff between sifting effect and efficiency, and that the performance can be controlled through a job allocation strategy. The operation of 「positive feedback」in collective wisdom can lead to sifting effect, 「integrity and variability」 leads to efficiency, and 「negative feedback」, 「inhibition」 lead to system stability. This research is believed to provide some managerial guidelines for web 2.0 sites operation.
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    5. 劉從哲，2004，自立晚報編輯部各級守門人行為之研究，銘傳大學傳播管理研究所碩士論文。
    6. Baran, S.J., and Davis, D.K. Mass Communication Theory: Foundations, Ferment, and Future Thomson Wadsworth, 2005.
    7. Bass, A.Z. "Refining the" gatekeeper" concept: A UN radio case study," Journalism Quarterly (46:1) 1969, pp 69-72.
    8. Bowman, S., and Willis, C. We media: How audiences are shaping the future of news and information, 2003.
    9. Bruns, A. "Gatewatching, Not Gatekeeping: Collaborative Online News," Media International Australia incorporating Culture and Policy (2003:107) 2003, pp 31-44.
    10. Fleischer, M. "Foundations of Swarm Intelligence: From Principles to Practice," Arxiv preprint nlin.AO/0502003) 2005.
    11. Friedman, T.L. "The World is Flat," New York: Farrar, Straus, and Giroux) 2005.
    12. Gilbert, G.N., and Troitzsch, K.G. Simulation for the Social Scientist Open University Press, 2005.
    13. Hagel, J., and Armstrong, A. "The Real Value of On-Line Communities," Harvard Business Review (74:3) 1996, pp 134-141.
    14. Hammond, T., Hannay, T., Lund, B., and Scott, J. "Social Bookmarking Tools (I)," D-Lib Magazine (11:4) 2005, pp 1082-9873.
    15. Heylighen, F. "Collective Intelligence and its Implementation on the Web: Algorithms to Develop a Collective Mental Map," Computational & Mathematical Organization Theory (5:3) 1999, pp 253-280.
    16. Johnson, N.L., Rasmussen, S., Joslyn, C., Rocha, L., Smith, S., and Kantor, M. "Symbiotic Intelligence: Self-Organizing Knowledge on Distributed Networks Driven by Human Interaction," Proceedings of the 6th International Conference on Artificial Life, The MIT Press, Cambridge, MA, 1998, pp. 403-407.
    17. Katz, M.L., and Shapiro, C. "Network Externalities, Competition, and Compatibility," American Economic Review (75:3) 1985, pp 424-440.
    18. Klaisubun, P., Kajondecha, P., and Ishikawa, T. "Behavior Patterns of Information Discovery in Social Bookmarking Service," Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence) 2007, pp 784-787.
    19. Lasica, J.D. "What is Participatory Journalism," Online Journalism Review (7) 2003, pp 1991-2003.
    20. Lerman, K. "Dynamics of collaborative document rating systems," Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis) 2007, pp 46-55.
    21. Lerman, K. "Social Information Processing in News Aggregation," IEEE INTERNET COMPUTING) 2007, pp 16-28.
    22. Lund, B., Hammond, T., Flack, M., and Hannay, T. "Social Bookmarking Tools (II)," D-Lib Magazine (11:4) 2005, pp 1082-9873.
    23. Macal, C.M., and North, M.J. "Tutorial on agent-based modeling and simulation," Winter Simulation Conference, 2005 Proceedings of the) 2005, pp 2-15.
    24. Menchen, E., and Student, G. "Feedback, Motivation and Collectivity in a Social Bookmarking System," Kairosnews Computers and Writing Online Conference) 2005.
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    26. North, M.J., Collier, N.T., and Vos, J.R. "Experiences Creating Three Implementations of the Repast Agent Modeling Toolkit," ACM Transactions on Modeling and Computer Simulation (16:1) 2006, pp 1-25.
    27. O`Reilly, T. "What is Web 2.0," Design Patterns and Business Models for the Next Generation of Software (30) 2005, p 2005.
    28. Railsback, S.F., Lytinen, S.L., and Jackson, S.K. "Agent-based Simulation Platforms: Review and Development Recommendations," SIMULATION (82:9) 2006, p 609.
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    30. Sinha, R. "Collaborative Filtering strikes back (this time with tags)." Retrieved April, 2008, from http://www.rashmisinha.com/archives/05_10/tags-collaborative-filtering.html.
    31. Sumpter, D.J.T. "The principles of collective animal behaviour," Philosophical Transactions of the Royal Society B: Biological Sciences (361:1465 ) 2006.
    32. Surowiecki, J. The wisdom of crowds: why the many are smarter than the few Abacus, 2005.
    33. Swearingen, K., and Sinha, R. "Beyond algorithms: An HCI perspective on recommender systems," ACM SIGIR 2001 Workshop on Recommender Systems) 2001.
    34. Zhu, H., Siegel, M.D., and Madnick, S.E. "Information Aggregation – A Value-added E-Service," Proceedings of the 5th International Conference on Technology, Policy, and Innovation¡VTheme: Critical Infrastructures) 2001, pp 26-29.
    描述: 碩士
    國立政治大學
    資訊管理研究所
    95356001
    96
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0095356001
    資料類型: thesis
    顯示於類別:[資訊管理學系] 學位論文

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