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- Title
Using centrality measures to improve the classification performance of tweets during natural disasters.
- Authors
Vásquez, Rodrigo; Riquelme, Fabián; González-Cantergiani, Pablo; Vásquez, Cristobal
- Abstract
Online social networks like Twitter facilitate instant communication during natural disasters. A key problem is to distinguish in real-time the most assertive and contingent tweets related to the current disaster from the whole streaming. To address this problem, machine learning allows to classify tweets according to their relevance or credibility. In this article, it is proposed to use centrality measures to improve the training data sample of active learning classifiers. As a case study, tweets collected during the massive floods in Santiago of Chile at 2016 are considered. This approach improves the consistency and pertinence of the labeling process, as well as the classifiers' performance.
- Subjects
SANTIAGO (Chile); NATURAL disasters; ACTIVE learning; TWITTER (Web resource); ONLINE social networks; EMERGENCY management; MACHINE learning; SOCIAL media
- Publication
INGENIARE - Revista Chilena de Ingeniería, 2021, Vol 29, Issue 1, p73
- ISSN
0718-3291
- Publication type
Article