An evaluation of twitter datasets from non-pandemic crises applied to regional COVID-19 contexts
18th International Conference on Information Systems for Crisis Response and Management, ISCRAM 2021
; 2021-May:808-815, 2021.
Article
in English
| Scopus | ID: covidwho-1589796
ABSTRACT
In 2020, we have witnessed an unprecedented crisis event, the COVID-19 pandemic. Various questions arise regarding the nature of this crisis data and the impacts it would have on the existing tools. In this paper, we aim to study whether we can include pandemic-type crisis events with general non-pandemic events and hypothesize that including labeled crisis data from a variety of non-pandemic events will improve classification performance over models trained solely on pandemic events. To test our hypothesis we study the model performance for different models by performing a cross validation test on pandemic only held-out sets for two different types of training sets, one containing only pandemic data and the other a combination of pandemic and non-pandemic crisis data, and comparing the results of the two. Our results approve our hypothesis and give evidence of some crucial information propagation upon inclusion of non-pandemic crisis data to pandemic data. © 2021 Information Systems for Crisis Response and Management, ISCRAM. All rights reserved.
Covid19; Cross-validation; Machine, learning; Transfer, learning; Trecis; Twitter; Information, dissemination; Information, management; Information, systems; Information, use; Social, networking, (online); Classification, performance; Crisis, events; Cross, validation; Cross-validation, tests; Information, propagation; Modeling, performance; Training, sets
Search on Google
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Experimental Studies
Language:
English
Journal:
18th International Conference on Information Systems for Crisis Response and Management, ISCRAM 2021
Year:
2021
Document Type:
Article
Similar
MEDLINE
...
LILACS
LIS