论文标题
在危机事件中检测和确定需求的经验方法论
An Empirical Methodology for Detecting and Prioritizing Needs during Crisis Events
论文作者
论文摘要
在危机时期，确定基本需求是为受影响实体提供适当资源和服务的关键步骤。 Twitter之类的社交媒体平台包含有关公众需求的大量信息。但是，信息的稀疏性以及嘈杂内容的数量给从业人员带来了挑战，即有效地识别这些平台上的共享信息。在这项研究中，我们为两个不同但相关的检测任务提出了两种新方法：识别1）按优先级排名所需的资源列表，以及2）指定谁需要资源的句子。我们评估了有关COVID-19危机的一系列推文的方法。对于任务1（检测最高需求），我们将结果与两个给定的资源清单进行了比较，并获得了64％的精度。对于任务2（检测到谁需要），我们在1,000个带注释的推文中比较了我们的结果，并达到了68％的F1分数。
In times of crisis, identifying the essential needs is a crucial step to providing appropriate resources and services to affected entities. Social media platforms such as Twitter contain vast amount of information about the general public's needs. However, the sparsity of the information as well as the amount of noisy content present a challenge to practitioners to effectively identify shared information on these platforms. In this study, we propose two novel methods for two distinct but related needs detection tasks: the identification of 1) a list of resources needed ranked by priority, and 2) sentences that specify who-needs-what resources. We evaluated our methods on a set of tweets about the COVID-19 crisis. For task 1 (detecting top needs), we compared our results against two given lists of resources and achieved 64% precision. For task 2 (detecting who-needs-what), we compared our results on a set of 1,000 annotated tweets and achieved a 68% F1-score.
