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Event Detection in Twitter Stream Using Weighted Dynamic Heartbeat Graph Approach [Application Notes]
IEEE Computational Intelligence Magazine ( IF 10.3 ) Pub Date : 2019-08-01 , DOI: 10.1109/mci.2019.2919395
Zafar Saeed , Rabeeh Ayaz Abbasi , Muhammad Imran Razzak , Guandong Xu

Tweets about everyday events are published on Twitter. Detecting such events is a challenging task due to the diverse and noisy contents of Twitter. In this paper, we propose a novel approach named Weighted Dynamic Heartbeat Graph (WDHG) to detect events from the Twitter stream. Once an event is detected in a Twitter stream, WDHG suppresses it in later stages, in order to detect new emerging events. This unique characteristic makes the proposed approach sensitive to capture emerging events efficiently. Experiments are performed on three real-life benchmark datasets: FA Cup Final 2012, Super Tuesday 2012, and the US Elections 2012. Results show considerable improvement over existing event detection methods in most cases.

中文翻译:

使用加权动态心跳图方法检测 Twitter 流中的事件 [应用说明]

关于日常事件的推文发布在 Twitter 上。由于 Twitter 的内容多种多样且嘈杂,因此检测此类事件是一项具有挑战性的任务。在本文中,我们提出了一种称为加权动态心跳图 (WDHG) 的新方法来检测 Twitter 流中的事件。一旦在 Twitter 流中检测到事件,WDHG 就会在后期抑制它,以检测新出现的事件。这种独特的特性使所提出的方法能够有效地捕捉新兴事件。在三个真实的基准数据集上进行了实验:2012 年足总杯决赛、2012 年超级星期二和 2012 年美国大选。结果显示,在大多数情况下,现有事件检测方法有相当大的改进。
更新日期:2019-08-01
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