Abstract
Real-time social media data can provide useful information on evolving hazards. Alongside traditional methods of disaster detection, the integration of social media data can considerably enhance disaster management. In this paper, we investigate the problem of detecting geolocation-content communities on Twitter and propose a novel distributed system that provides in near real-time information on hazard-related events and their evolution. We show that content-based community analysis leads to better and faster dissemination of reports on hazards. Our distributed disaster reporting system analyzes the social relationship among worldwide geolocated tweets, and applies topic modeling to group tweets by topics. Considering for each tweet the following information: user, timestamp, geolocation, retweets, and replies, we create a publisher-subscriber distribution model for topics. We use content similarity and the proximity of nodes to create a new model for geolocation-content based communities. Users can subscribe to different topics in specific geographical areas or worldwide and receive real-time reports regarding these topics. As misinformation can lead to increase damage if propagated in hazards related tweets, we propose a new deep learning model to detect fake news. The misinformed tweets are then removed from display. We also show empirically the scalability capabilities of the proposed system.
Abstract (translated)
实时社交媒体数据可以提供关于演化危险的有用的信息,与传统的灾难检测方法相结合,社交媒体数据的集成可以极大地增强灾难管理。在本文中,我们研究了在推特上检测地理定位内容社区的问题,并提出了一种全新的分布式系统,可以提供与危险相关的事件及其演化的实时信息。我们证明了基于内容社区分析可以更好地和更快地传播关于危险的报告。我们的分布式灾难报告系统分析了全球范围内地理定位推特上的社交关系,并应用主题建模将推特按主题分组。考虑到每个推特的以下信息:用户、时间戳、地理定位、转发、回复,我们为每个主题建立了作者-读者分布模型。我们使用内容相似性和节点接近创建了一个新的基于地理定位内容社区模型。用户可以在特定的地理区域或全球范围内订阅不同的主题,并接收关于这些主题的实时报告。由于虚假信息在危险相关的推特中传播可能导致增加伤害,我们提出了一种新的深度学习模型来检测虚假信息。然后将虚假信息推特从显示中删除。我们还 empirical 证明了所提出的系统的可扩展性能力。
URL
https://arxiv.org/abs/2301.12984
