@inproceedings{cornelius-etal-2020-covid,
title = "{COVID}-19 {T}witter Monitor: Aggregating and Visualizing {COVID}-19 Related Trends in Social Media",
author = "Cornelius, Joseph and
Ellendorff, Tilia and
Furrer, Lenz and
Rinaldi, Fabio",
editor = "Gonzalez-Hernandez, Graciela and
Klein, Ari Z. and
Flores, Ivan and
Weissenbacher, Davy and
Magge, Arjun and
O'Connor, Karen and
Sarker, Abeed and
Minard, Anne-Lyse and
Tutubalina, Elena and
Miftahutdinov, Zulfat and
Alimova, Ilseyar",
booktitle = "Proceedings of the Fifth Social Media Mining for Health Applications Workshop {\&} Shared Task",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.smm4h-1.1",
pages = "1--10",
abstract = "Social media platforms offer extensive information about the development of the COVID-19 pandemic and the current state of public health. In recent years, the Natural Language Processing community has developed a variety of methods to extract health-related information from posts on social media platforms. In order for these techniques to be used by a broad public, they must be aggregated and presented in a user-friendly way. We have aggregated ten methods to analyze tweets related to the COVID-19 pandemic, and present interactive visualizations of the results on our online platform, the COVID-19 Twitter Monitor. In the current version of our platform, we offer distinct methods for the inspection of the dataset, at different levels: corpus-wide, single post, and spans within each post. Besides, we allow the combination of different methods to enable a more selective acquisition of knowledge. Through the visual and interactive combination of various methods, interconnections in the different outputs can be revealed.",
}
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%0 Conference Proceedings
%T COVID-19 Twitter Monitor: Aggregating and Visualizing COVID-19 Related Trends in Social Media
%A Cornelius, Joseph
%A Ellendorff, Tilia
%A Furrer, Lenz
%A Rinaldi, Fabio
%Y Gonzalez-Hernandez, Graciela
%Y Klein, Ari Z.
%Y Flores, Ivan
%Y Weissenbacher, Davy
%Y Magge, Arjun
%Y O’Connor, Karen
%Y Sarker, Abeed
%Y Minard, Anne-Lyse
%Y Tutubalina, Elena
%Y Miftahutdinov, Zulfat
%Y Alimova, Ilseyar
%S Proceedings of the Fifth Social Media Mining for Health Applications Workshop & Shared Task
%D 2020
%8 December
%I Association for Computational Linguistics
%C Barcelona, Spain (Online)
%F cornelius-etal-2020-covid
%X Social media platforms offer extensive information about the development of the COVID-19 pandemic and the current state of public health. In recent years, the Natural Language Processing community has developed a variety of methods to extract health-related information from posts on social media platforms. In order for these techniques to be used by a broad public, they must be aggregated and presented in a user-friendly way. We have aggregated ten methods to analyze tweets related to the COVID-19 pandemic, and present interactive visualizations of the results on our online platform, the COVID-19 Twitter Monitor. In the current version of our platform, we offer distinct methods for the inspection of the dataset, at different levels: corpus-wide, single post, and spans within each post. Besides, we allow the combination of different methods to enable a more selective acquisition of knowledge. Through the visual and interactive combination of various methods, interconnections in the different outputs can be revealed.
%U https://aclanthology.org/2020.smm4h-1.1
%P 1-10
Markdown (Informal)
[COVID-19 Twitter Monitor: Aggregating and Visualizing COVID-19 Related Trends in Social Media](https://aclanthology.org/2020.smm4h-1.1) (Cornelius et al., SMM4H 2020)
ACL