  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.

Computation and Language;Information Retrieval;Social and Information Networks