A probabilistic method for emerging topic tracking in Microblog stream

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• 作者：Jiajia Huang ; Min Peng ; Hua Wang ; Jinli Cao ; Wang Gao ; Xiuzhen Zhang
• 关键词：Microblog stream ; Emerging topic ; LWLR ; Topic evolution ; Optimization problem
• 刊名：World Wide Web
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：20
• 期：2
• 页码：325-350
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Information Systems Applications (incl.Internet); Database Management; Operating Systems;
• 出版者：Springer US
• ISSN：1573-1413
• 卷排序：20

文摘

Microblog is a popular and open platform for discovering and sharing the latest news about social issues and daily life. The quickly-updated microblog streams make it urgent to develop an effective tool to monitor such streams. Emerging topic tracking is one of such tools to reveal what new events are attracting the most online attention at present. However, due to the fast changing, high noise and short length of the microblog feeds, two challenges should be addressed in emerging topic tracking. One is the problem of detecting emerging topics early, long before they become hot, and the other is how to effectively monitor evolving topics over time. In this study, we propose a novel emerging topics tracking method, which aligns emerging word detection from temporal perspective with coherent topic mining from spatial perspective. Specifically, we first design a metric to estimate word novelty and fading based on local weighted linear regression (LWLR), which can highlight the word novelty of expressing an emerging topic and suppress the word novelty of expressing an existing topic. We then track emerging topics by leveraging topic novelty and fading probabilities, which are learnt by designing and solving an optimization problem. We evaluate our method on a microblog stream containing over one million feeds. Experimental results show the promising performance of the proposed method in detecting emerging topic and tracking topic evolution over time on both effectiveness and efficiency.