COVID-19 Twitter Monitor: Aggregating and Visualizing COVID-19 Related Trends in Social Media
Joseph Cornelius, Tilia Ellendorff, Lenz Furrer, Fabio Rinaldi
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.- Anthology ID:
- 2020.smm4h-1.1
- Volume:
- Proceedings of the Fifth Social Media Mining for Health Applications Workshop & Shared Task
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Editors:
- Graciela Gonzalez-Hernandez, Ari Z. Klein, Ivan Flores, Davy Weissenbacher, Arjun Magge, Karen O'Connor, Abeed Sarker, Anne-Lyse Minard, Elena Tutubalina, Zulfat Miftahutdinov, Ilseyar Alimova
- Venue:
- SMM4H
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1–10
- Language:
- URL:
- https://aclanthology.org/2020.smm4h-1.1
- DOI:
- Cite (ACL):
- Joseph Cornelius, Tilia Ellendorff, Lenz Furrer, and Fabio Rinaldi. 2020. COVID-19 Twitter Monitor: Aggregating and Visualizing COVID-19 Related Trends in Social Media. In Proceedings of the Fifth Social Media Mining for Health Applications Workshop & Shared Task, pages 1–10, Barcelona, Spain (Online). Association for Computational Linguistics.
- Cite (Informal):
- COVID-19 Twitter Monitor: Aggregating and Visualizing COVID-19 Related Trends in Social Media (Cornelius et al., SMM4H 2020)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2020.smm4h-1.1.pdf