Wikipedia是一個多語系且具有豐富內容的線上百科。Wikipedia以Web2.0為發展的概念，允許任何人進行分享編輯，這也使得Wikipedia很容易被破壞。因此，維持Wikipedia的內容品質是所有維基人長期持續努力的方向。過往針對Wikipedia破壞偵測的研究方向，主要專注在文字語意、特徵統計與機器學習。目前的方向則更注重非語言特徵分析與連續性內容的上下文關聯性分析。WikiSERM依據維基百科定義的編輯標籤進行關鍵字萃取，且該編輯標籤適用於各種語言維基，因此具有非語言相關的特性，並使得WikiSERM得以應用在各種語言版本的維基百科破壞偵測。WikiSERM使用完整的文章版本作為判斷風險趨勢的證據，分析個別關鍵字於維基文章的使用狀態(例如：保持使用中或完全被刪除)。透過關鍵字使用狀態的連續性事件分析，則可獲得每個關鍵字在每個對應版本的風險狀態。且WikiSERM將關鍵字的風險程度加以紀錄成二維陣列以便未來快速查詢比對，因此WikiSERM得以應對增量性的資料並立即地提供風險評估結果。最後，藉由針對異動版本版本中的關鍵字異動分析(例如：新增高風險關鍵字、新增低風險關鍵字、刪除高風險關鍵字，或刪除低風險關鍵字)，WikiSERM將超過風險臨界值的版本判定為高風險版本。本研究成果可以協助維基百科管理者快速找尋破壞性版本，並且識別該版本中的高風險關鍵字項目。

Wikipedia is a multi-language and wealth-content online encyclopedia. Based on the concept of Web2.0, Wikipedia allows anyone to share and edit Wikipedia content, which also makes Wikipedia easily to be destroyed. Therefore, all Wikipedians pay long-term sustained effort on maintaining the quality of Wikipedia content. The past research directions for Wikipedia vandalism detection focused on the text semantic, feature statistical and machine learning. The current directions focus on language-independent feature analysis and continuity content-context correlation analysis. WikiSERM extracts the key-item based on the Wikipedia edit tag which applies to various languages of Wikipedia. Therefore key-item based on the Wikipedia edit tag has the language-independent feature, and it makes WikiSERM be applied in various language versions of Wikipedia vandalism detection. WikiSERM take the full version of the article as evidence to judge risk trends and to analyze using-status of each key item in a Wikipedia article (e.g., keeps being used or completely deleted). Through analysis of the continuity of key item using-status, we can get risk status of each key item in each corresponding revision. WikiSERM records those risk results of previous revision as two-dimensional array for querying quickly, therefore WikiSERM has the ability to deal with the incremental data and to provide the risk assessment result immediately. Through the analysis of key item transaction in the Wikipedia revision (e.g., add high-risk key item, add low-risk key item, delete high-risk key item, delete low-risk key item), WikiSERM take those over-threshold revisions as a high risk version. Our approach can help Wikipedia administrators to quickly find vandalism revision, and identify which is the high-risk key item in the vandalism revision.

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