Classification of tweets containing self-reported COVID-19 symptoms

This task involves identifying personal mentions of COVID-19 symptoms tweets. It is a three-way classification problem, requiring participants to distinguish personal symptom mentions (self-reports) from other mentions such as symptoms reported by others (non-personal reports) and references to external sources (literature/news mentions).

The annotated set of tweets for this task is a set of manually curated Spanish-native language tweets.

The task is hosted on Codalab at https://codalab.lisn. upsaclay.fr/competitions/3535

Publication
Davy Weissenbacher, Juan Banda, Vera Davydova, Darryl Estrada Zavala, Luis Gasco Sánchez, Yao Ge, Yuting Guo, Ari Klein, Martin Krallinger, Mathias Leddin, Arjun Magge, Raul Rodriguez-Esteban, Abeed Sarker, Lucia Schmidt, Elena Tutubalina, and Graciela Gonzalez-Hernandez. 2022. Overview of the Seventh Social Media Mining for Health Applications (#SMM4H) Shared Tasks at COLING 2022. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 221–241, Gyeongju, Republic of Korea. Association for Computational Linguistics.
Language
Spanish
Abstract task
Year
2022
Ranking metric
F1

Task results

System Precision Recall F1 Sort ascending
14 0.9000 0.9000 0.9000
26 0.8600 0.8600 0.8600
46 0.8500 0.8500 0.8500
13 0.8500 0.8500 0.8500
5 0.8500 0.8500 0.8500

If you have published a result better than those on the list, send a message to odesia-comunicacion@lsi.uned.es indicating the result and the DOI of the article, along with a copy of it if it is not published openly.