Eulalia Farre


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Overview of the Sixth Social Media Mining for Health Applications (#SMM4H) Shared Tasks at NAACL 2021
Arjun Magge | Ari Klein | Antonio Miranda-Escalada | Mohammed Ali Al-Garadi | Ilseyar Alimova | Zulfat Miftahutdinov | Eulalia Farre | Salvador Lima López | Ivan Flores | Karen O’Connor | Davy Weissenbacher | Elena Tutubalina | Abeed Sarker | Juan Banda | Martin Krallinger | Graciela Gonzalez-Hernandez
Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task

The global growth of social media usage over the past decade has opened research avenues for mining health related information that can ultimately be used to improve public health. The Social Media Mining for Health Applications (#SMM4H) shared tasks in its sixth iteration sought to advance the use of social media texts such as Twitter for pharmacovigilance, disease tracking and patient centered outcomes. #SMM4H 2021 hosted a total of eight tasks that included reruns of adverse drug effect extraction in English and Russian and newer tasks such as detecting medication non-adherence from Twitter and WebMD forum, detecting self-reported adverse pregnancy outcomes, detecting cases and symptoms of COVID-19, identifying occupations mentioned in Spanish by Twitter users, and detecting self-reported breast cancer diagnosis. The eight tasks included a total of 12 individual subtasks spanning three languages requiring methods for binary classification, multi-class classification, named entity recognition and entity normalization. With a total of 97 registering teams and 40 teams submitting predictions, the interest in the shared tasks grew by 70% and participation grew by 38% compared to the previous iteration.