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        <title>Finding the needle in a haystack: Extraction of Informative COVID-19 Danish Tweets</title>
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        <namePart type="given">Benjamin</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Finding the needle in a haystack: Extraction of Informative COVID-19 Danish Tweets
%A Olsen, Benjamin
%A Plank, Barbara
%S Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online
%F olsen-plank-2021-finding
%X 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.
%R 10.18653/v1/2021.wnut-1.2
%U https://aclanthology.org/2021.wnut-1.2
%U https://doi.org/10.18653/v1/2021.wnut-1.2
%P 11-19

Markdown (Informal)

[Finding the needle in a haystack: Extraction of Informative COVID-19 Danish Tweets](https://aclanthology.org/2021.wnut-1.2) (Olsen & Plank, WNUT 2021)

• Finding the needle in a haystack: Extraction of Informative COVID-19 Danish Tweets (Olsen & Plank, WNUT 2021)

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.