21世紀的人們，越來越依賴社群網站。使用者在社群網站上發布或分享的大量資料，往往反映了真實的事件。有些事件甚至比新聞媒體更早被揭露。本論文的目標在運用社群演變序列偵測事件。我們的方法是運用移動窗戶式的統計理論來擷取候選事件。接著，我們使用觀念式演變圖形序列來模擬資訊的傳遞，並且根據這個特性偵測候選事件是否為事件。實驗結果顯示我們能有效的偵測真實事件。

Social networks, which have become extremely popular in the 21st century, contain a tremendous amount of user-generated content about real-world events. This user-generated content relays real-world events as they happen, and sometimes even ahead of the newswire. The goal of this work is to identify events from social streams. The proposed model utilizes sliding-window-based statistical techniques to extract event candidates from social streams. Subsequently, the “Concept-based evolving graph sequences”(cEGS) approach is employed to verify information propagation trends of event candidates and to identify those events. The experimental results show the usefulness of our approach in identifying real-world events in social streams.

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