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研究生:陳尹安
研究生(外文):Yin-An Chen
論文名稱:線上評論社群持續知識分享之預測
論文名稱(外文):Prediction of Continuance Knowledge Sharing in Online Opinion-sharing Communities
指導教授:魏志平魏志平引用關係
口試委員:曾新穆吳怡瑾
口試日期:2013-07-16
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:資訊管理學研究所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:英文
論文頁數:54
中文關鍵詞:持續知識分享持續分享意圖線上評論社群行為預測
外文關鍵詞:continuance knowledge sharingknowledge sharing intentiononline opinion-sharing communitiesbehavior prediction
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過去的文獻指出,存在某些內在或外在因素會影響成員分享知識的意圖,進而產生了「持續分享知識」的行為,而擁有持續且足夠的知識來源是線上社群經營成功與否的關鍵因素。此篇研究的目的為,建立線上評論社群持續知識分享之預測模型。我們嘗試提出客觀的衡量指標來推估某成員的未來是否會持續這樣的行為,利用該成員的資料、信任網路結構、評論行為以及與其他使用者互動情形來預測未來的狀況。此模型可以幫助研究者更進一步了解影響「持續分享知識」行為的重要因素,且能協助線上社群的經營者更有效率地找出對此社群有持續貢獻的成員,並訂定相關策略來提升知識分享的成員數量。實驗數據顯示,我們提出的預測模型有良好的預測能力和穩定度。

Prior works have proposed models based on expected disconfirmation theory and identified several factors that influence behaviors in online communities, such as reputation, relational capital, extrinsic rewards, etc. Instead of conducting a survey to measure users’ attitudes and beliefs in research environment, we observed the actual behavior and relationships of users in veritable opinion-sharing websites - Epinions. The goal of this study is to predict the behavior of individuals continuous share knowledge in online communities. An objective perspective is employed to build a prediction model based on data mining techniques, which was implemented in an idiosyncratic research context exploring the driving factors behind members’ knowledge contributions and the behavior of contributors. It is useful in aiding online community operators in understand the behaviors of their users by in which research to date has offered limited discussion.

口試委員會審定書 #
誌謝 i
中文摘要 ii
ABSTRACT iii
CONTENTS iv
LIST OF FIGURES vi
LIST OF TABLES vii
Chapter 1 Introduction 1
1.1 Background 1
1.2 Research Motivation 2
1.3 Research Objective 3
1.4 Organization of Research 4
Chapter 2 Literature Review 5
2.1 Knowledge Sharing in Online Communities 5
2.2 Continuance Knowledge Sharing (CKS) 7
Chapter 3 The Proposed Model for CKS Prediction 13
3.1 Epinions 13
3.2 Variables for CKS Prediction 14
3.2.1 Reputation 18
3.2.2 Reciprocity 20
3.2.3 Relational Capital 23
3.2.4 Knowledge Expertise 25
3.2.5 Other Factors 27
3.3 CKS Classifier 30
3.4 CKS Prediction Model 32
Chapter 4 Evaluation 34
4.1 Data Collection 34
4.2 Evaluation Criterion 36
4.3 Experiment Result 38
4.3.1 Prediction Results with Threshold = 5 38
4.3.2 Prediction Results with Different Thresholds 39
4.3.3 Prediction by Different Years 41
4.3.4 Analysis of Factor Importance 44
Chapter 5 Conclusion and Future Work 48
References 50


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