SocialDisNER focuses on the recognition of disease mentions in Twitter posts. Tweets originate from the following: patients’ accounts, with firsthand health reports; friends, support network and relatives, who share the difficulties faced by patients; and medical professionals, who disseminate reliable information about diseases. Tweets in this task include information on rheumatic diseases such as lupus erythematosus, highly prevalent diseases such as cancer, diabetes, obesity and mental disorders, fibromyalgia and autism spectrum conditions.
Publication
Luis Gasco Sánchez, Darryl Estrada Zavala, Eulàlia Farré-Maduell, Salvador Lima-López, Antonio Miranda-Escalada, and Martin Krallinger. 2022. The SocialDisNER shared task on detection of disease mentions in health-relevant content from social media: methods, evaluation, guidelines and corpora. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 182–189, Gyeongju, Republic of Korea. Association for Computational Linguistics.
Language
Spanish
NLP topic
Abstract task
Dataset
Year
2022
Publication link
Ranking metric
F1
Task results
| System | Precision | Recall | F1 Sort ascending |
|---|---|---|---|
| CASIA | 0.9060 | 0.8760 | 0.8910 |
| READ-BioMed | 0.8680 | 0.8750 | 0.8710 |
| Clac | 0.8510 | 0.8880 | 0.8690 |
| PLN CMM | 0.8820 | 0.8430 | 0.8620 |
| NLP-CIC-WFU | 0.8420 | 0.8600 | 0.8510 |
