Real-time Classification, Geolocation and Interactive Visualization of COVID-19 Information Shared on Social Media to Better Understand Global Developments

Andrei Mircea


Abstract
As people communicate on social media during COVID-19, it can be an invaluable source of useful and up-to-date information. However, the large volume and noise-to-signal ratio of social media can make this impractical. We present a prototype dashboard for the real-time classification, geolocation and interactive visualization of COVID-19 tweets that addresses these issues. We also describe a novel L2 classification layer that outperforms linear layers on a dataset of respiratory virus tweets.
Anthology ID:
2020.nlpcovid19-2.37
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.nlpcovid19-2.37
DOI:
10.18653/v1/2020.nlpcovid19-2.37
Bibkey:
Cite (ACL):
Andrei Mircea. 2020. Real-time Classification, Geolocation and Interactive Visualization of COVID-19 Information Shared on Social Media to Better Understand Global Developments. In Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020, Online. Association for Computational Linguistics.
Cite (Informal):
Real-time Classification, Geolocation and Interactive Visualization of COVID-19 Information Shared on Social Media to Better Understand Global Developments (Mircea, NLP-COVID19 2020)
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PDF:
https://aclanthology.org/2020.nlpcovid19-2.37.pdf
Code
 mirandrom/crisistweetmap