论文标题
在危机期间，社交媒体信息的人道主义援助反应的聚集
Clustering of Social Media Messages for Humanitarian Aid Response during Crisis
论文作者
论文摘要
社交媒体迅速发展成为人们在危机活动中传达和表达需求的重要工具。先前在分析社交媒体数据的危机管理中的工作主要集中在自动识别可操作（或信息性）危机相关的消息。在这项工作中，我们表明，深度学习和自然语言处理方面的最新进展优于先前的方法，以对信息进行分类并鼓励该领域采用它们进行研究甚至部署。我们还将这些方法扩展到了两个信息的子任务，并发现深度学习方法在这里也很有效。
Social media has quickly grown into an essential tool for people to communicate and express their needs during crisis events. Prior work in analyzing social media data for crisis management has focused primarily on automatically identifying actionable (or, informative) crisis-related messages. In this work, we show that recent advances in Deep Learning and Natural Language Processing outperform prior approaches for the task of classifying informativeness and encourage the field to adopt them for their research or even deployment. We also extend these methods to two sub-tasks of informativeness and find that the Deep Learning methods are effective here as well.
