Sarol M Janina, Dinh Ly, Rezapour Rezvaneh, Chin Chieh-Li, Yang Pingjing, Diesner Jana
University of Illinois at Urbana-Champaign, IL, USA.
Proc Conf Empir Methods Nat Lang Process. 2020 Nov;2020:4102-4107. doi: 10.18653/v1/2020.findings-emnlp.366.
In times of crisis, identifying essential needs is crucial to providing appropriate resources and services to affected entities. Social media platforms such as Twitter contain a vast amount of information about the general public's needs. However, the sparsity of information and the amount of noisy content present a challenge for practitioners to effectively identify relevant information on these platforms. This study proposes two novel methods for two needs detection tasks: 1) extracting a list of needed resources, such as masks and ventilators, and 2) detecting sentences that specify who-needs-what resources (e.g., we need testing). We evaluate our methods on a set of tweets about the COVID-19 crisis. For extracting a list of needs, we compare our results against two official lists of resources, achieving 0.64 precision. For detecting who-needs-what sentences, we compared our results against a set of 1,000 annotated tweets and achieved a 0.68 F1-score.
在危机时期，确定基本需求对于向受影响的实体提供适当的资源和服务至关重要。诸如推特这样的社交媒体平台包含了大量有关公众需求的信息。然而，信息的稀疏性和大量嘈杂的内容给从业者在这些平台上有效识别相关信息带来了挑战。本研究针对两项需求检测任务提出了两种新颖的方法：1）提取所需资源列表，如口罩和呼吸机，以及2）检测指定谁需要什么资源的句子（例如，我们需要检测）。我们在一组关于新冠疫情危机的推文上评估我们的方法。对于提取需求列表，我们将结果与两份官方资源列表进行比较，精确率达到0.64。对于检测谁需要什么的句子，我们将结果与一组1000条带注释的推文进行比较，F1分数达到0.68。
