Forecasting COVID-19 Caseloads Using Unsupervised Embedding Clusters of Social Media Posts

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1471 - 1484 July 10-15, 2022 ©2022 Association for Computational Linguistics Forecasting COVID-19 Caseloads Using Unsupervised Embedding Clusters of Social Media Posts Felix Drinkall*, Stefan Zohren*†, Janet B. Pierrehumbert*‡ *Department of Engineering Science, University of Oxford †The Alan Turing Institute ‡Faculty of Linguistics, University of Oxford felix.drinkall@eng.ox.ac.uk Abstract We present a novel approach incorporating transformer-based language models into infec- tious disease modelling. Text-derived features are quantified by tracking high-density clus- ters of sentence-level representations of Red- dit 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 outper- form all other feature types at predicting up- ward trend signals, a significant result for infec- tious disease modelling in areas where epidemi- ological 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 supple- mentary datasets as covariate feature sets in a transformer-based time-series model. 1 Introduction Many papers have shown that web search data can be used to forecast the spread of infectious diseases (Lampos et al., 2017), (Lampos et al., 2021), (Mc- Donald et al., 2021), (Reinhart et al., 2021), (Al- ruily et al., 2022). Alongside this literature, social media has been exploited for its predictive poten- tial in several other fields such as quantitative fi- nance Xu and Cohen (2018), Sawhney et al. (2020), logistics forecasting Ni et al. (2017) and elec- tion forecasting (Bermingham and Smeaton, 2011), (Huberty, 2015). Research conjoining these two strands has produced results showing that social media can help predict rises in disease caseloads. Iso et al. (2016) and Samaras et al. (2020) both used pre-defined keywords in order to predict outbreaks of influenza; words such as "Influenza", "fever", "headache" were selected a-priori. These papers as- sume that useful feature sets have no geographical variation and use the same features regardless of the regional social dynamics; they also assume that useful features are limited to words that refer to Figure 1: HDBSCAN clusters of the SBERT-NLI-STSb- base representations of r/CoronavirusWA posts made at 50 dimensions but reduced to 2 for visualisation. symptoms. To address these limitations, Drinkall and Pierrehumbert (2021) set more general and ob- jective inclusion criteria. For each of four US state COVID-19 subreddits, all words over-represented in that US state’s COVID-19 subreddit compared to the rest of Reddit were considered to be potential keywords for forecasting. The most informative keywords proved to be highly dependent on the target state, and included many that did not refer to symptoms. However, the paper still relied on static word counts that miss more complex information as the discussion unfolds over time. The present paper extracts more informative features from so- cial media data and, to our knowledge, is the first work to incorporate modern NLP techniques in …

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