Detecting Emerging Symptoms of COVID-19 using Context-based Twitter Embeddings
Roshan Santosh, H. Andrew Schwartz, Johannes Eichstaedt, Lyle Ungar, Sharath Chandra Guntuku
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).- Anthology ID:
- 2020.nlpcovid19-2.35
- Volume:
- Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020
- Month:
- December
- Year:
- 2020
- Address:
- Online
- Editors:
- Karin Verspoor, Kevin Bretonnel Cohen, Michael Conway, Berry de Bruijn, Mark Dredze, Rada Mihalcea, Byron Wallace
- Venue:
- NLP-COVID19
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- Language:
- URL:
- https://aclanthology.org/2020.nlpcovid19-2.35
- DOI:
- 10.18653/v1/2020.nlpcovid19-2.35
- Cite (ACL):
- Roshan Santosh, H. Andrew Schwartz, Johannes Eichstaedt, Lyle Ungar, and Sharath Chandra Guntuku. 2020. Detecting Emerging Symptoms of COVID-19 using Context-based Twitter Embeddings. In Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020, Online. Association for Computational Linguistics.
- Cite (Informal):
- Detecting Emerging Symptoms of COVID-19 using Context-based Twitter Embeddings (Santosh et al., NLP-COVID19 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2020.nlpcovid19-2.35.pdf
- Code
- rsk2327/Covid-Symptoms-NLP