English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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

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