@inproceedings{sarol-etal-2020-empirical,
title = "An Empirical Methodology for Detecting and Prioritizing Needs during Crisis Events",
author = "Sarol, M. Janina and
Dinh, Ly and
Rezapour, Rezvaneh and
Chin, Chieh-Li and
Yang, Pingjing and
Diesner, Jana",
editor = "Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.366",
doi = "10.18653/v1/2020.findings-emnlp.366",
pages = "4102--4107",
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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%0 Conference Proceedings
%T An Empirical Methodology for Detecting and Prioritizing Needs during Crisis Events
%A Sarol, M. Janina
%A Dinh, Ly
%A Rezapour, Rezvaneh
%A Chin, Chieh-Li
%A Yang, Pingjing
%A Diesner, Jana
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F sarol-etal-2020-empirical
%X 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.
%R 10.18653/v1/2020.findings-emnlp.366
%U https://aclanthology.org/2020.findings-emnlp.366
%U https://doi.org/10.18653/v1/2020.findings-emnlp.366
%P 4102-4107
Markdown (Informal)
[An Empirical Methodology for Detecting and Prioritizing Needs during Crisis Events](https://aclanthology.org/2020.findings-emnlp.366) (Sarol et al., Findings 2020)
ACL