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Leveraging burst in twitter network communities for event detection
World Wide Web ( IF 3.7 ) Pub Date : 2020-03-04 , DOI: 10.1007/s11280-020-00786-y
Jeffery Ansah , Lin Liu , Wei Kang , Jixue Liu , Jiuyong Li

Detecting protest events using social media is an important task with many useful applications to emergency services, law enforcement agencies, and other stakeholders. A plethora of research on event detection using social media has presented myriad approaches relying on tweet contents (text) to solve the event detection problem, with notable improvements over time. Despite the myriad of existing research, the use of the structural relationships among users in online Twitter network communities for event detection is rarely observed. In this work, we present a novel protest event detection framework called SensorTree. SensorTree utilizes the network structural connections among users in a community for protest event detection. The SensorTree framework tracks information propagation in Twitter network communities to model the sudden change in growth of these communities as burst for event detection. Once burst is detected, SensorTree builds a tensorized topic model to extract events. To show the prowess of SensorTree for event detection, we conduct extensive experiments on geographically diverse Twitter datasets using qualitative and quantitative evaluations. We further show the superiority of SensorTree by comparing our results to several existing state-of-the-art methods. SensorTree outperforms the baselines as well as the comparison models. The results further suggest that utilizing network community structure yields concise and accurate event detection. We also present case studies on real-world protest event to further show that SensorTree is capable of detecting events with fine granularity description without any language restrictions.

中文翻译:

利用Twitter网络社区中的突发事件进行事件检测

使用社交媒体检测抗议事件是一项重要任务,在应急服务,执法机构和其他利益相关者中有许多有用的应用程序。大量使用社交媒体进行事件检测的研究提出了多种方法,这些方法依靠推文内容(文本)来解决事件检测问题,并且随着时间的推移有了显着改进。尽管进行了大量的现有研究,但很少观察到在线Twitter网络社区中的用户之间的结构关系用于事件检测。在这项工作中,我们提出了一个名为SensorTree的新颖的抗议事件检测框架。SensorTree利用社区中用户之间的网络结构连接来进行抗议事件检测。SensorTree框架跟踪Twitter网络社区中的信息传播,以将这些社区的增长突然变化建模为事件检测的爆发。一旦检测到突发,SensorTree就会构建张量主题模型以提取事件。为了展示SensorTree在事件检测方面的能力,我们使用定性和定量评估对地理上各异的Twitter数据集进行了广泛的实验。通过将我们的结果与几种现有的最新方法进行比较,我们进一步展示了SensorTree的优越性。SensorTree的性能优于基线以及比较模型。结果进一步表明,利用网络社区结构可以产生简洁而准确的事件检测。
更新日期:2020-03-04
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