Finding the needle in a haystack: Extraction of Informative COVID-19 Danish Tweets

Benjamin Olsen, Barbara Plank


Abstract
Finding informative COVID-19 posts in a stream of tweets is very useful to monitor health-related updates. Prior work focused on a balanced data setup and on English, but informative tweets are rare, and English is only one of the many languages spoken in the world. In this work, we introduce a new dataset of 5,000 tweets for finding informative COVID-19 tweets for Danish. In contrast to prior work, which balances the label distribution, we model the problem by keeping its natural distribution. We examine how well a simple probabilistic model and a convolutional neural network (CNN) perform on this task. We find a weighted CNN to work well but it is sensitive to embedding and hyperparameter choices. We hope the contributed dataset is a starting point for further work in this direction.
Anthology ID:
2021.wnut-1.2
Volume:
Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)
Month:
November
Year:
2021
Address:
Online
Editors:
Wei Xu, Alan Ritter, Tim Baldwin, Afshin Rahimi
Venue:
WNUT
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
11–19
Language:
URL:
https://aclanthology.org/2021.wnut-1.2
DOI:
10.18653/v1/2021.wnut-1.2
Bibkey:
Cite (ACL):
Benjamin Olsen and Barbara Plank. 2021. Finding the needle in a haystack: Extraction of Informative COVID-19 Danish Tweets. In Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021), pages 11–19, Online. Association for Computational Linguistics.
Cite (Informal):
Finding the needle in a haystack: Extraction of Informative COVID-19 Danish Tweets (Olsen & Plank, WNUT 2021)
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PDF:
https://aclanthology.org/2021.wnut-1.2.pdf