Discovering Events from Social Media for Emergency Planning
Discovering Events from Social Media for Emergency Planning
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DOI：
10.1145/3325112.3325213
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发表时间：
2019-06
期刊：
影响因子：
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通讯作者：
Momna Anam;Basit Shafiq;S. Shamail;Soon Ae Chun;N. Adam
中科院分区：
文献类型：
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作者：
Momna Anam;Basit Shafiq;S. Shamail;Soon Ae Chun;N. Adam
社交媒体是一个流行的日常交流平台。它是获取任何事件实时信息的最快媒介。事件识别和发现事件之间的关系对于信息检索非常重要，这在许多情况下都很有用。例如，在灾害管理方面，这些信息有助于更好地规划针对未来事件的反应行动。然而，由于庞大的数据量，从社交媒体数据中发现重要事件是一项具有挑战性的任务。在本文中，我们提出了一种从Twitter提要中发现事件及其关系的自动化方法。我们提出的方法使用两级聚类方法。第一级聚类识别不同tweet中的主要事件，第二级聚类通过考虑它们的时空和语义关系来识别给定主要事件的子事件。我们用一个来自twitter的数据集来评估我们的方法。结果表明，两级聚类能够以合理的准确率发现主要事件和相关子事件。我们还讨论了事件发现自动化方法在应急计划和应急响应评估中的意义。
Social media is a popular platform for daily communication. It is the fastest medium to get real-time information about any event. Event identification and finding relations between them is important for information retrieval, which can be useful in many situations. For example, in case of disaster management this information can be helpful in better planning of response operations for future events. However, discovering the important events from a social media data is a challenging task due to the sheer volume of data. In this paper, we present an automated approach for discovering events and their relationships from Twitter feeds. Our proposed approach uses a two-level clustering approach. The first level clustering identifies major events among diverse tweets, and the second level clustering identifies sub-events of a given major event by considering their spatio-temporal and semantic relationships. We evaluate our approach on a dataset taken from twitter. Results show that the two level clustering could discover major events and associated sub-events with reasonable accuracy. We also discuss the implications of the automated approach of event discovery in emergency planning and emergency response evaluation.