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Detecting Emerging Symptoms of COVID-19 using Context-based Twitter Embeddings

2020-11-08EMNLP (NLP-COVID19) 2020Code Available0· sign in to hype

Roshan Santosh, H. Andrew Schwartz, Johannes C. Eichstaedt, Lyle H. Ungar, Sharath C. Guntuku

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Abstract

In this paper, we present an iterative graph-based approach for the detection of symptoms of COVID-19, the pathology of which seems to be evolving. More generally, the method can be applied to finding context-specific words and texts (e.g. symptom mentions) in large imbalanced corpora (e.g. all tweets mentioning #COVID-19). Given the novelty of COVID-19, we also test if the proposed approach generalizes to the problem of detecting Adverse Drug Reaction (ADR). We find that the approach applied to Twitter data can detect symptom mentions substantially before being reported by the Centers for Disease Control (CDC).

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