An evaluation of twitter datasets from non-pandemic crises applied to regional COVID-19 contexts

Document Type

Conference Proceeding

Publication Date

1-1-2021

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.

Identifier

85121266635 (Scopus)

ISBN

[9781949373615]

Publication Title

Proceedings of the International Iscram Conference

e-ISSN

24113387

First Page

808

Last Page

815

Volume

2021-May

This document is currently not available here.

Share

COinS