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Social Network Analysis and Mining
Published in:

01-12-2023 | Original Article

Identifying COVID-19 english informative tweets using limited labelled data

Authors: Srinivasulu Kothuru, A. Santhanavijayan

Published in: Social Network Analysis and Mining | Issue 1/2023

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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.
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Footnotes
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Metadata
Title
Identifying COVID-19 english informative tweets using limited labelled data
Authors
Srinivasulu Kothuru
A. Santhanavijayan
Publication date
01-12-2023
Publisher
Springer Vienna
Published in
Social Network Analysis and Mining / Issue 1/2023
Print ISSN: 1869-5450
Electronic ISSN: 1869-5469
DOI
https://doi.org/10.1007/s13278-023-01025-8

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