Relevancy Classification of Multimodal Social Media Streams for Emergency Services
Relevancy Classification of Multimodal Social Media Streams for Emergency Services
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DOI：
10.1109/smartcomp.2019.00040
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发表时间：
2019-06
期刊：
影响因子：
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通讯作者：
Ganesh Nalluru;Rahul Pandey;Hemant Purohit
中科院分区：
文献类型：
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作者：
Ganesh Nalluru;Rahul Pandey;Hemant Purohit
社交媒体已经成为我们日常生活中不可或缺的一部分。在时间紧迫的事件中，公众在社交媒体上分享各种帖子，包括资源需求，损失和为受影响社区提供帮助的报告。这些帖子可能具有相关性，并可能包含有价值的情况了解信息。然而，社交媒体的信息过载对应急服务及时处理和提取相关信息提出了挑战。此外，近年来社交媒体帖子中多媒体内容的使用日益增加，进一步增加了从社交媒体及时挖掘相关信息的挑战。在本文中，我们提出了一种新的方法，多模态相关性分类的社交媒体职位，相关性的定义方面的应急管理机构的信息需求。具体来说，我们实验与语义文本特征与图像特征的组合，以有效地分类相关的多模态社交媒体帖子。我们验证了我们的方法，使用三个现实世界的危机事件的数据分类的评价。我们的实验表明，利用文本和图像内容的建议的混合框架的基础上的功能，提高识别相关职位的性能。根据这些实验，应用所提出的分类方法可以在大规模过滤多模式公共帖子时减少紧急服务的认知负荷。
Social media has become an integral part of our daily lives. During time-critical events, the public shares a variety of posts on social media including reports for resource needs, damages, and help offerings for the affected community. Such posts can be relevant and may contain valuable situational awareness information. However, the information overload of social media challenges the timely processing and extraction of relevant information by the emergency services. Furthermore, the growing usage of multimedia content in the social media posts in recent years further adds to the challenge in timely mining relevant information from social media. In this paper, we present a novel method for multimodal relevancy classification of social media posts, where relevancy is defined with respect to the information needs of emergency management agencies. Specifically, we experiment with the combination of semantic textual features with the image features to efficiently classify a relevant multimodal social media post. We validate our method using an evaluation of classifying the data from three real-world crisis events. Our experiments demonstrate that features based on the proposed hybrid framework of exploiting both textual and image content improve the performance of identifying relevant posts. In the light of these experiments, the application of the proposed classification method could reduce cognitive load on emergency services, in filtering multimodal public posts at large scale.