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An Empirical Methodology for Detecting and Prioritizing Needs During Crisis Events.

Computing Research Repository (CoRR)(2020)

Univ Illinois

Cited 7|Views29
Abstract
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.
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要点】:本文提出了一种新的实证方法,用于在危机事件中检测和优先排序需求,创新点在于提出了两种新颖的方法分别用于提取所需资源列表和检测具体需求句子。

方法】:研究采用文本分析方法,针对危机事件中的社交媒体数据进行需求检测。

实验】:实验在关于COVID-19危机的推文数据集上进行,使用官方资源列表和人工注释的推文进行评估,提取资源列表达到0.64的精确度,检测具体需求句子达到0.68的F1分数。