no code implementations • NAACL (SMM4H) 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
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.
no code implementations • SMM4H (COLING) 2022 • 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, Graciela Gonzalez-Hernandez
For the past seven years, the Social Media Mining for Health Applications (#SMM4H) shared tasks have promoted the community-driven development and evaluation of advanced natural language processing systems to detect, extract, and normalize health-related information in public, user-generated content.
no code implementations • SMM4H (COLING) 2020 • Ari Klein, Ilseyar Alimova, Ivan Flores, Arjun Magge, Zulfat Miftahutdinov, Anne-Lyse Minard, Karen O’Connor, Abeed Sarker, Elena Tutubalina, Davy Weissenbacher, Graciela Gonzalez-Hernandez
The vast amount of data on social media presents significant opportunities and challenges for utilizing it as a resource for health informatics.
no code implementations • WS 2019 • Davy Weissenbacher, Abeed Sarker, Arjun Magge, Ashlynn Daughton, Karen O{'}Connor, Michael J. Paul, Gonzalez-Hern, Graciela ez
We present the Social Media Mining for Health Shared Tasks collocated with the ACL at Florence in 2019, which address these challenges for health monitoring and surveillance, utilizing state of the art techniques for processing noisy, real-world, and substantially creative language expressions from social media users.
no code implementations • SEMEVAL 2019 • Davy Weissenbacher, Arjun Magge, Karen O{'}Connor, Matthew Scotch, Gonzalez-Hern, Graciela ez
We also analyze the methods, the results and the errors made by the competing systems with a focus on toponym disambiguation.
no code implementations • 10 Apr 2019 • Davy Weissenbacher, Abeed Sarker, Ari Klein, Karen O'Connor, Arjun Magge Ranganatha, Graciela Gonzalez-Hernandez
A fundamental step to incorporating Twitter data in pharmacoepidemiological research is to automatically recognize medication mentions in tweets.
no code implementations • 22 Oct 2018 • Ari Z. Klein, Abeed Sarker, Davy Weissenbacher, Graciela Gonzalez-Hernandez
The primary objective of this study was to take the first step towards scaling the use of social media for observing pregnancies with birth defect outcomes, namely, developing methods for automatically detecting tweets by users reporting their birth defect outcomes.
no code implementations • WS 2018 • Davy Weissenbacher, Abeed Sarker, Michael J. Paul, Gonzalez-Hern, Graciela ez
The goals of the SMM4H shared tasks are to release annotated social media based health related datasets to the research community, and to compare the performances of natural language processing and machine learning systems on tasks involving these datasets.
no code implementations • WS 2018 • Takeshi Onishi, Davy Weissenbacher, Ari Klein, Karen O{'}Connor, Gonzalez-Hern, Graciela ez
Through a semi-automatic analysis of tweets, we show that Twitter users not only express Medication Non-Adherence (MNA) in social media but also their reasons for not complying; further research is necessary to fully extract automatically and analyze this information, in order to facilitate the use of this data in epidemiological studies.