@inproceedings{santosh-etal-2020-detecting,
title = "Detecting Emerging Symptoms of {COVID}-19 using Context-based {T}witter Embeddings",
author = "Santosh, Roshan and
Schwartz, H. Andrew and
Eichstaedt, Johannes and
Ungar, Lyle and
Guntuku, Sharath Chandra",
editor = "Verspoor, Karin and
Cohen, Kevin Bretonnel and
Conway, Michael and
de Bruijn, Berry and
Dredze, Mark and
Mihalcea, Rada and
Wallace, Byron",
booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
month = dec,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.nlpcovid19-2.35",
doi = "10.18653/v1/2020.nlpcovid19-2.35",
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 to their being reported by the Centers for Disease Control (CDC).",
}
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%0 Conference Proceedings
%T Detecting Emerging Symptoms of COVID-19 using Context-based Twitter Embeddings
%A Santosh, Roshan
%A Schwartz, H. Andrew
%A Eichstaedt, Johannes
%A Ungar, Lyle
%A Guntuku, Sharath Chandra
%Y Verspoor, Karin
%Y Cohen, Kevin Bretonnel
%Y Conway, Michael
%Y de Bruijn, Berry
%Y Dredze, Mark
%Y Mihalcea, Rada
%Y Wallace, Byron
%S Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020
%D 2020
%8 December
%I Association for Computational Linguistics
%C Online
%F santosh-etal-2020-detecting
%X 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 to their being reported by the Centers for Disease Control (CDC).
%R 10.18653/v1/2020.nlpcovid19-2.35
%U https://aclanthology.org/2020.nlpcovid19-2.35
%U https://doi.org/10.18653/v1/2020.nlpcovid19-2.35
Markdown (Informal)
[Detecting Emerging Symptoms of COVID-19 using Context-based Twitter Embeddings](https://aclanthology.org/2020.nlpcovid19-2.35) (Santosh et al., NLP-COVID19 2020)
ACL