Identifying COVID-19 english informative tweets using limited labelled data.
Soc Netw Anal Min
; 13(1): 25, 2023.
Article
in English
| MEDLINE | ID: covidwho-2242301
ABSTRACT
Identifying COVID-19 informative tweets is very useful in building monitoring systems to track the latest updates. Existing approaches to identify informative tweets rely on a large number of labelled tweets to achieve good performances. As labelling is an expensive and laborious process, there is a need to develop approaches that can identify COVID-19 informative tweets using limited labelled data. In this paper, we propose a simple yet novel labelled data-efficient approach that achieves the state-of-the-art (SOTA) F1-score of 91.23 on the WNUT COVID-19 dataset using just 1000 tweets (14.3% of the full training set). Our labelled data-efficient approach starts with limited labelled data, augment it using data augmentation methods and then fine-tune the model using augmented data set. It is the first work to approach the task of identifying COVID-19 English informative tweets using limited labelled data yet achieve the new SOTA performance.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Prognostic study
Language:
English
Journal:
Soc Netw Anal Min
Year:
2023
Document Type:
Article
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