Tracking Disaster Footprints with Social Streaming Data
Tracking Disaster Footprints with Social Streaming Data
复制标题
DOI：
10.1609/aaai.v34i01.5372
复制
发表时间：
2020-04
期刊：
影响因子：
--
通讯作者：
Lu Cheng;Jundong Li;K. Candan;Huan Liu
中科院分区：
文献类型：
--
作者：
Lu Cheng;Jundong Li;K. Candan;Huan Liu
社交媒体因其广泛的吸引力和快速传播信息的能力，成为面对自然灾害不可或缺的工具。例如，Twitter 是救灾人员搜索 (1) 随着时间的推移被认为特别感兴趣的主题的重要来源，即“救灾”等常见主题； (2) 与灾害相关的讨论的新主题正在社交媒体流中迅速聚集（Saha 和 Sindhwani 2012），即“最近的海啸破坏”等独特主题。为了了解现状并将有限的资源分配给最紧急的领域，应急管理人员需要快速筛选随着时间推移产生的相关主题，并调查其共性和独特性。然而，有效使用社交媒体的一个主要障碍是其大量的噪音和不需要的数据。因此，简单的方法，例如设置交集/差异来查找共同/不同的主题，通常是不切实际的。为了应对这一挑战，本文研究了一种新的主题跟踪问题，旨在利用社交流数据有效识别共同和不同的主题。这个问题很重要，因为它提供了一种在紧急响应期间有效搜索准确信息的有前途的新方法。这是通过在线非负矩阵分解（NMF）方案和联合 NMF 技术来实现的，该方案可以更快地更新潜在因子，而联合 NMF 技术则寻求主题识别的重构误差与发现共同和不同主题引起的损失之间的平衡。在飓风哈维和佛罗伦萨期间收集的真实世界数据集的广泛实验结果揭示了我们框架的有效性。
Social media has become an indispensable tool in the face of natural disasters due to its broad appeal and ability to quickly disseminate information. For instance, Twitter is an important source for disaster responders to search for (1) topics that have been identified as being of particular interest over time, i.e., common topics such as “disaster rescue”; (2) new emerging themes of disaster-related discussions that are fast gathering in social media streams (Saha and Sindhwani 2012), i.e., distinct topics such as “the latest tsunami destruction”. To understand the status quo and allocate limited resources to most urgent areas, emergency managers need to quickly sift through relevant topics generated over time and investigate their commonness and distinctiveness. A major obstacle to the effective usage of social media, however, is its massive amount of noisy and undesired data. Hence, a naive method, such as set intersection/difference to find common/distinct topics, is often not practical. To address this challenge, this paper studies a new topic tracking problem that seeks to effectively identify the common and distinct topics with social streaming data. The problem is important as it presents a promising new way to efficiently search for accurate information during emergency response. This is achieved by an online Nonnegative Matrix Factorization (NMF) scheme that conducts a faster update of latent factors, and a joint NMF technique that seeks the balance between the reconstruction error of topic identification and the losses induced by discovering common and distinct topics. Extensive experimental results on real-world datasets collected during Hurricane Harvey and Florence reveal the effectiveness of our framework.