隨著社群媒體的普及，群眾產製的內容（User-generated content, UGC）時常成為新聞記者取材的對象，但現今隨著社群媒體爆發的資料量，記者不易從資料中看到事件的全貌，僅將社群媒體當作一種消息來源，因此報導的內容經常抄襲網友的意見或是落入片面討論的窠臼，無法駕馭社群媒體帶來的豐富資料。考慮改善這樣的現象，本研究透過將新聞取材的過程分為探索事件、收集素材以及回溯情境三個動作來協助記者探索新聞主題。以推特（Twitter）的資料為例，以網路為系統平台，開發一個輔助記者探索社群媒體上的事件、挖掘新聞主題的資訊系統，利用網絡分析以及自然語言處理的技術，結合視覺化的介面將事件資料集用故事元素的方式呈現，四種故事元素模型提供不同的觀察資料集的角度，並利用調整四種故事元素的權重，還原推文文本的語境，找出使用者想看的內容。我們設計了兩階段的任務式實驗以及評估問卷來證明系統的可用性，透過實驗結果驗證了本研究在以社群媒體輔助記者探索新聞主題的系統之價值，能讓對事件不同熟悉程度的傳播記者在此平台上探索新聞主題，並寫下深度報導的編採線索或是一篇新聞報導，透過本系統的輔助，讓使用者在探索及追蹤一起事件時，變得較為快速。

With the popularity of social media, news reporters usually draw the news materials from mass user-generated content. However, with the outbreak of social media data, the reporter is not easy to see from the data in the whole picture of event. They only use the social media as a news source, so the reported content often copied the views of users, or fall into the stereotype of a one-sided discussion. The reporters can not control the wealth of information brought from social media. Consider improving this phenomenon, our study use Twitter data for example, develop an information system to assist reporters to explore the events on social media, and mine the news topics. We use network analysis and natural language processing as our technique, and show the story elements with the visualization interface. We apply four different story elements model, support the different way to explore data, and let user can adjust the weights from different model to retrospect to the context of tweets, help user find the news topics. We have designed a two-stage task experiment and assessment questionnaire to prove the availability of the system through experimental results. We can allow the reporters who are varying degrees of familiarity of the event to explore news topics from our system. We make the reporter to explore and track some events faster.

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