TY - GEN
T1 - An empirical methodology for detecting and prioritizing needs during crisis events
AU - Janina Sarol, M.
AU - Dinh, Ly
AU - Rezapour, Rezvaneh
AU - Chin, Chieh Li
AU - Yang, Pingjing
AU - Diesner, Jana
N1 - This work was supported in part by the U.S. Department of Homeland Security under Grant Award Number 2015-ST-061-CIRC01. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the U.S. Department of Homeland Security.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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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U2 - 10.18653/v1/2020.findings-emnlp.366
DO - 10.18653/v1/2020.findings-emnlp.366
M3 - Conference contribution
AN - SCOPUS:85118430480
T3 - Findings of the Association for Computational Linguistics Findings of ACL: EMNLP 2020
SP - 4102
EP - 4107
BT - Findings of the Association for Computational Linguistics Findings of ACL
PB - Association for Computational Linguistics (ACL)
T2 - Findings of the Association for Computational Linguistics, ACL 2020: EMNLP 2020
Y2 - 16 November 2020 through 20 November 2020
ER -