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Online Event Detection in Social Media with Bursty Event Recognition

  • Wanlun MaEmail author
  • Zhuo LiuEmail author
  • Xiangyu HuEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1095)

Abstract

The emergence of social media opens tremendous research opportunities. Many individuals, mostly teens and young adults around the world, share their daily lives and opinions about a wide variety of topics (e.g., crime, sports, and politics) on social media sites. Thus, social media becomes a valuable repository for data of different types, which could provide insights of social events happening around the world. However, it is still a challenge to identify the bursty and disruptive events from the massive and noisy user-generated content on social media sites. In this paper, we present a novel event detection framework for identifying surrounding real-world events that can support decision making and emergency management. Our proposed framework consists of four main components, including data pre-processing, event-related tweets classifying, online clustering, and bursty event recognition. We conducted a series of experiments on the real-world social media dataset collected from Twitter. The experimental results demonstrated the effectiveness of our proposed method.

Keywords

Event detection Social media Text mining Information extraction 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Information and Communication EngineeringUniversity of Electronic Science and Technology of ChinaChengduPeople’s Republic of China

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