運用雙層訊息表示模型進行遞增社會事件偵測

Abstract

隨著社群媒體的快速發展，近年來從社群媒體串流中偵測事件受到的關注日益增加。因為它可以快速地突顯重大事件，有助於人們更有效且全面地瞭解事件的樣貌。然而，從社群媒體串流（如Twitter串流）中偵測事件具有相當大的挑戰性，因為社群媒體串流具有順序性且龐大。此外，它們包含各種元素，像是文本、時間、用戶、命名實體、主題標籤和隱含的社交網絡結構。再者，它們的內容通常很短，充斥著非標準的縮寫，並且嚴重傾向於熱門和流行的主題。先前的研究主要從單詞級或訊息級觀點提取訊息的表示式。然而，前者可能忽略了隱含在整個訊息中的重要語義和結構資訊，而後者可能忽略了某些事件與訊息中特定單詞高度相關的事實。為了解決上述問題，我們提出一個創新的雙層表示方法，稱為DLR (Dual-Level Representation)，它整合了單詞級和訊息級觀點，可更全面地萃取訊息的特徵。實驗結果顯示，我們提出的方法不論是在離線或遞增情境中，在標準化互信息(NMI)、調整互信息(AMI)和調整蘭德係數(ARI)上均優於當前最先進的方法。另外，我們的方法可以幫助用戶、組織和政府進行危機管理、輿情分析和擬定相關決策。

With the rapid growth of social media, detecting events from social media streams has received considerable attention in recent years. This is because it highlights major happenings, assisting people in comprehensively understanding the real world more effectively. However, detecting events from social media streams, like Twitter streams, is quite challenging due to their sequential and large-scale nature. Furthermore, they contain diverse elements such as text, time, users, named entities, hashtags, and implicit social network structures. Moreover, the content is often short, filled with non-standard abbreviations, and heavily skewed towards hot and trending topics. Previous studies have mainly focused on deriving the message representations from either the token-level or the message-level perspective for social event detection. However, the former may overlook important semantic and structural information implicitly hidden in the whole message, while the latter may ignore the fact that some events are highly associated with specific tokens in the message. To resolve such problems, we propose a novel method, called Dual-Level Representation (DLR), which simultaneously considers both the message-level and token-level perspectives to obtain a more comprehensive understanding of message content. The experimental results show that our proposed method outperforms the state-of-the-art methods in terms of the normalized mutual information (NMI), adjusted mutual information (AMI), and adjusted rand index (ARI) in both offline and incremental scenarios. Furthermore, our method can benefit users, organizations, and governments to do crisis management, public opinion analysis, and decision making.