臺灣博碩士論文加值系統

近年來，由於網際網路的興起，網際網路已成為新聞媒體重要的傳播管道之一，許多新聞網站如雨後春筍般的成立，而讀者也樂於使用這類更加便利、高互動性的新聞網站。但是媒體使用網路作為傳播管道，同時也面臨在傳統傳播模式所未遭遇的新挑戰，網路新聞媒體被迫需要創造獨特的內容吸引使用者，也需發展具黏性的社群經營服務，才能與其他具有類似社群互動機制的Web 2.0網站一較長短，留住廣大的使用者群。
本研究嘗試利用新聞為日常生活人們獲得資訊不可或缺管道的獨特優勢，提出一套有效利用新聞使用社群集體智慧(Collective Intelligence）機制，能夠自動化依據使用者顯隱性回饋，針對每篇新聞分析出分歧度、熱門度、話題性三個社群資訊，並以上述三個社群資訊挑選出合適的焦點新聞，以此促進新聞社群使用者對於焦點新聞的討論與互動，進而提昇新聞傳播的效益與新聞社群的凝聚力。實驗結果證實，本研究所提出的機制確實能夠探勘出滿足大多數使用者關注焦點新聞資訊的需求，並且對於輔助記者掌握讀者對於新聞資訊需求及促進新聞社群經營方面都有很大的助益。

In recent years, due to the rise of the information and communication technologies, the internet has become one of most important communication channel for Journalism. A long with drastically flourished on-line Journalism, models of readers’ information reception changed while they are enjoyed more convenient and interactive websites providing instant information.
At the same time, while mass media utilize internet as communication channel, it has also brought unprecedented challenge to traditional communication. On-line Journalism has not only need to create unique content (information) to attract readers; but it also need to develop a more engaging community management services to interact with other communities with similar mechanisms of Web 2.0 sites to retain user’s attention.
This study attempts to exam the proposed on-line journalism system for University Press community, which could automatically analyze readers’ community dataset of University newspaper; including opinion deviation indicators, popularity indicator, and topicality indicator of each news (information). This system selects targeted news (information) according to above indicators to promote discussion and interactivity within readers’ community in hope to promote efficiency of news (information) communication and engagement within readers’ community. Experiment results reveal this proposed mechanism could satisfy most readers’ need for headline news; as well as assist Journalists’ understanding on their readers’ need while promoting on-line journalism social networking management.

第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 3
1.3 問題陳述 4
1.4 章節架構 4
第二章 文獻探討 6
2.1 線上新聞網站的發展與未來 6
2.2 團體討論與決策行為 10
2.3 使用者回饋之分類 15
2.4 協同式推薦系統介紹 18
2.5 隱性回饋 20
2.6 小結 22
第三章 研究方法 25
3.1 新聞社群互動模式 25
3.2 系統架構 27
3.2.1 系統運作流程 29
3.3 推論代理人 30
3.3.1 分歧度計算 31
3.3.2 熱門度與話題性 31
3.3.3 焦點分數 32
3.3.4 模糊邏輯推論 33
3.3.5 決定模糊歸屬函數 34
3.3.6 建立模糊規則 36
3.3.7 解模糊化 38
3.4 系統實作 38
第四章 實驗設計 42
4.1 實驗對象 42
4.2 社群分析 43
4.3 網站使用狀況探勘 44
4.4 問卷調查及分析 46
4.5 研究限制 47
第五章 研究結果分析與討論 48
5.1 新聞基本資料分析 48
5.2 讀者回饋資訊分析 49
5.3 網站使用分析 56
5.4 社會網路分析 59
5.5 問卷分析 65
5.6 新聞案例分析 69
5.7 大學報實習記者訪談 71
第六章 結論與未來展望 73
6.1 結論 73
6.2 未來展望 75
參考文獻 77
附錄一 使用者問卷調查表 81
附錄二 使用者問卷之對於本系統的意見調查表 83
附錄三 新聞《人肉搜索揪關鍵人物》 86
附錄四 大學報實習記者與編輯訪談實錄 88
附錄五 大學報總編輯及副總編輯訪談實錄 93

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