- 學位論文
網路社群成員之動態發展預測方法
Dynamic Development of Social Network Members Prediction
摘要
網路社群的蓬勃發展有目共睹，但管理者需能掌握社群動態，才能維護社群的長遠發展。然而，前人研究多著力於社群成員影響力之預測與訊息傳遞方式，對於個別成員或總體動態發展變化，則少有著墨。有鑑於此，本研究針對網路社群成員的動態發展，提出一套有效的預測方法。首先，我們利用節點分支度、中心性等七種指標，作為描述社群成員特質的依據。接著，利用k-means演算法對社群成員進行分群，根據群心來了解成員的典型特質，並據以建立成員狀態，本研究提出5大類型成員：外向型、受關注型、積極互動型、一般型、邊緣型。接著，利用馬可夫鏈模型產生預測模型，透過建立狀態轉換矩陣，了解社群成員未來可能的狀態轉變。其次，我們也透過循序樣式探勘概念，建立社群的常見狀態變化循序樣式，以預測成員未來的狀態變遷。此外，本研究亦以滑動視窗概念建立迴歸模型，期能預測討論區之發文量。 為驗證提出方法之有效性，本研究蒐集PTT不同討論區的實際資料來進行實驗。結果顯示，以馬可夫鏈為基礎之預測方法準確率可為68.5%∼74%，且隨著order值增加而上升；以循序樣式為基礎之方法準確率，則會隨著實驗週數增加而增加。此外，透過線性迴歸對社群發文量進行預測時，結果顯示與實際值具有良好的相關度。綜合上述結果，驗證本研究提出方法確實有助於網路社群成員之動態發展預測，能作為管理當局規劃未來的發展之重要參考依據。
並列摘要
With the advancement of social network, social network manager must care development of social network, then we can maintain long development of social network. However, previous studies have focused on prediction of the influence of members and method of message delivery. Have not focused on change of dynamic development of members or social network. Because of this, this research found an effective method to predict members of dynamic development. First, we used 7 kinds of attributes with Node degree、Centrality etc. as a basis for describing the characteristics of members. Next, we used k-means clustering algorithm to separate social network members. According to the cluster centroids, we can understand typical characteristics of members then create members status. This research divided into five types of members : Extrovert、Be-Concerned、Positive Interaction、Borderline、Neutrals. Then, we use the Markov Chain Model to generate predictive model, understanding the possible changes of members in all future through the establishment of state transition matrix. Second, build Common state changes of social network sequential pattern based on the concepts of sequential pattern. To predict changing of the future status of members. In addition, this research establish a regression model based on the concept of sliding windows. Hope can predict numbers of published article of forums. To verify the effectiveness of the proposed method, This research data download from PTT forum are used for experiment, and the results show that based on the Markov chain, the accuracy rate can be 68% to 74%, and be increased as the value of order increases; Based on the Sequential Pattern, the accuracy rate can be increased with increased number of weeks of experiment. In addition, predict numbers of publish article through linear regression, the results has good correlated with value of actual. The above results that demonstrate the effectiveness of the proposed method can understand dynamic development of social network member’s prediction, it can help social network manager planning the future development and policy.