Detection of Behavioral Facilitation information in Disaster Situation
Detection of Behavioral Facilitation information in Disaster Situation
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DOI：
10.1145/3366030.3366129
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发表时间：
2019-12
期刊：
影响因子：
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通讯作者：
Yoshiki Yoneda;Yumiko Suzuki;Akiyo Nadamoto
中科院分区：
文献类型：
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作者：
Yoshiki Yoneda;Yumiko Suzuki;Akiyo Nadamoto
近年来，发生了许多类型的灾害，如强烈地震，暴雨和台风。在这种灾害情况下，人们经常使用社交网络服务（SNS）并交换各种信息以相互帮助。特别是，人们在灾难期间使用Twitter交换信息。这样的推文信息包括许多促进人们行为的信息。我们将这种推文称为行为促进推文。当人们在灾难后心理不稳定时，行为促进推文会强烈影响人们，无论消息的真实性如何。我们认为自动提取行为促进推文是很重要的。在本文中，我们提出了一种方法，在灾难情况下提取行为促进推文。具体来说，我们提出并比较了三种方法来提取灾难情况下的行为促进推文：基于规则的，支持向量机（SVM）和长短期记忆（LSTM）。此外，我们进行了实验，以评估我们提出的方法的好处。
Disasters of many types have occurred in recent years, such as strong earthquakes, heavy rain, and typhoons. In such disaster situations, people often use social network services (SNS) and exchange information of all types to help each other. Especially, people exchange information using Twitter during disasters. Such tweet messages include much information that promotes people's behaviors. We designate such tweets as behavioral facilitation tweets. When psychologically unstable in the aftermath of a disaster, behavioral facilitation tweets can strongly affect people, irrespective of a message's authenticity. We regard the extraction of the behavioral facilitation tweets automatically as important. In this paper, we propose a method that extracts behavioral facilitation tweets in disaster situations. Specifically, we propose and compare three methods to extract behavioral facilitation tweets in disaster situations: rule-based, support vector machine (SVM) and long short-term memory (LSTM). Furthermore, we conducted experiments to assess the benefits of our proposed method.