Abstract
We present a novel approach incorporating transformer-based language models into infectious disease modelling. Text-derived features are quantified by tracking high-density clusters of sentence-level representations of Reddit posts within specific US states’ COVID-19 subreddits. We benchmark these clustered embedding features against features extracted from other high-quality datasets. In a threshold-classification task, we show that they outperform all other feature types at predicting upward trend signals, a significant result for infectious disease modelling in areas where epidemiological data is unreliable. Subsequently, in a time-series forecasting task, we fully utilise the predictive power of the caseload and compare the relative strengths of using different supplementary datasets as covariate feature sets in a transformer-based time-series model.- Anthology ID:
- 2022.naacl-main.105
- Volume:
- Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Editors:
- Marine Carpuat, Marie-Catherine de Marneffe, Ivan Vladimir Meza Ruiz
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1471–1484
- Language:
- URL:
- https://aclanthology.org/2022.naacl-main.105
- DOI:
- 10.18653/v1/2022.naacl-main.105
- Cite (ACL):
- Felix Drinkall, Stefan Zohren, and Janet Pierrehumbert. 2022. Forecasting COVID-19 Caseloads Using Unsupervised Embedding Clusters of Social Media Posts. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1471–1484, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- Forecasting COVID-19 Caseloads Using Unsupervised Embedding Clusters of Social Media Posts (Drinkall et al., NAACL 2022)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.naacl-main.105.pdf