CLEar: A Real-time Online Observatory for Bursty and Viral Events

• Main Authors: XIE, Runquan, ZHU, Feida, MA, Hui, XIE, Wei, LIN, Chen
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2014
• Subjects: Databases and Information Systems; Social Media
• Online Access: https://ink.library.smu.edu.sg/sis_research/2647; https://ink.library.smu.edu.sg/context/sis_research/article/3647/viewcontent/p1637_xie.pdf
• Institution: Singapore Management University

We describe our demonstration of CLEar (Clairaudient Ear), a real-time online platform for detecting, monitoring, summarizing, contextualizing and visualizing bursty and viral events, those triggering a sudden surge of public interest and going viral on micro-blogging platforms. This task is challenging for existing methods as they either use complicated topic models to analyze topics in a off-line manner or define temporal structure of fixed granularity on the data stream for online topic learning, leaving them hardly scalable for real-time stream like that of Twitter. In this demonstration of CLEar, we present a three-stage system: First, we show a real-time bursty event detection module based on a data-sketch topic model which makes use of acceleration of certain stream quantities as the indicators of topic burstiness to trigger efficient topic inference. Second, we demonstrate popularity prediction for the detected bursty topics and event summarization based on clustering related topics detected in successive time periods. Third, we illustrate CLEar's module for contextualizing and visualizing the event evolution both along time-line and across other news media to offer an easier understanding of the events.