ArticleSocial network analysis and mining2023
Identifying COVID-19 english informative tweets using limited labelled data.
Article in Social network analysis and mining, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Comparison of pretrained transformer-based models for influenza and COVID-19 detection using social media text data in Saskatchewan, Canada.Frontiers in digital health · 2023Article
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2 authors.
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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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