Social Media Driven Big Data Analysis for Disaster Situation Awareness: A Tutorial
Social Media Driven Big Data Analysis for Disaster Situation Awareness: A Tutorial
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DOI：
10.1109/tbdata.2022.3158431
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发表时间：
2023-02-01
影响因子：
7.2
通讯作者：
Sato，Kento
中科院分区：
文献类型：
--
作者：
Pal，Amitangshu;Wang，Junbo;Sato，Kento
情境意识试图通过感知、沟通和推理来把握物理世界中的重要事件和情况。跟踪不断变化的局势的演变是这种认识的一个重要组成部分，对于在灾害期间提供适当的资源和帮助至关重要。近年来，社交媒体，尤其是Twitter，在这一过程中发挥着越来越大的作用。然而，从现有数据中提取情报涉及若干挑战，包括（a）过滤掉大量不相关的数据，（B）融合社交媒体和其他来源产生的异构数据，以及（c）处理部分带有地理标记的社交媒体数据，以推断受影响民众的需求。数据的时空分析在理解情况方面起着关键作用，但只有很少的人可用，因为只有一小部分人发布相关文本，并且其中很少有人能够进行位置跟踪。在本文中，我们提供了一个关于数据分析的全面调查，以评估社交媒体大数据的态势感知。
Situational awareness tries to grasp the important events and circumstances in the physical world through sensing, communication, and reasoning. Tracking the evolution of changing situations is an essential part of this awareness and is crucial for providing appropriate resources and help during disasters. Social media, particularly Twitter, is playing an increasing role in this process in recent years. However, extracting intelligence from the available data involves several challenges, including (a) filtering out large amounts of irrelevant data, (b) fusion of heterogeneous data generated by the social media and other sources, and (c) working with partially geo-tagged social media data in order to deduce the needs of the affected people. Spatio-temporal analysis of the data plays a key role in understanding the situation, but is available only sparsely because only a small fraction of people post relevant text and of those very few enable location tracking. In this paper, we provide a comprehensive survey on data analytics to assess situational awareness from social media big data.