Detecting Emerging Symptoms of COVID-19 using Context-based Twitter Embeddings
This addresses the problem of early symptom detection for public health monitoring during novel outbreaks like COVID-19, though it appears incremental as it adapts existing methods to new data.
The paper tackles detecting emerging COVID-19 symptoms from Twitter data using a graph-based approach, finding it can identify symptom mentions substantially earlier than CDC reports.
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).