An Empirical Methodology for Detecting and Prioritizing Needs during Crisis Events
M. Janina Sarol, Ly Dinh, Rezvaneh Rezapour, Chieh-Li Chin, Pingjing Yang, Jana Diesner
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.- Anthology ID:
- 2020.findings-emnlp.366
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2020
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Trevor Cohn, Yulan He, Yang Liu
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4102–4107
- Language:
- URL:
- https://aclanthology.org/2020.findings-emnlp.366
- DOI:
- 10.18653/v1/2020.findings-emnlp.366
- Cite (ACL):
- M. Janina Sarol, Ly Dinh, Rezvaneh Rezapour, Chieh-Li Chin, Pingjing Yang, and Jana Diesner. 2020. An Empirical Methodology for Detecting and Prioritizing Needs during Crisis Events. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 4102–4107, Online. Association for Computational Linguistics.
- Cite (Informal):
- An Empirical Methodology for Detecting and Prioritizing Needs during Crisis Events (Sarol et al., Findings 2020)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2020.findings-emnlp.366.pdf
- Code
- janinaj/needs_detection