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UPennHLP at WNUT-2020 Task 2 : Transformer models for classification of COVID19 posts on Twitter
Increasing usage of social media presents new non-traditional avenues for monitoring disease outbreaks, virus transmissions and disease progressions through user posts describing test results or disease symptoms.
Overview of the Fifth Social Media Mining for Health Applications (#SMM4H) Shared Tasks at COLING 2020
The vast amount of data on social media presents significant opportunities and challenges for utilizing it as a resource for health informatics.
Overview of the Sixth Social Media Mining for Health Applications (#SMM4H) Shared Tasks at NAACL 2021
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.
Overview of the Seventh Social Media Mining for Health Applications (#SMM4H) Shared Tasks at COLING 2022
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.
ReportAGE: Automatically extracting the exact age of Twitter users based on self-reports in tweets
The objective of this study was to develop and evaluate a method that automatically identifies the exact age of users based on self-reports in their tweets.
Overview of the Fourth Social Media Mining for Health (SMM4H) Shared Tasks at ACL 2019
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.
SemEval-2019 Task 12: Toponym Resolution in Scientific Papers
We also analyze the methods, the results and the errors made by the competing systems with a focus on toponym disambiguation.
Social media mining for identification and exploration of health-related information from pregnant women
We hypothesize that we can use social media to identify cohorts of pregnant women and follow them over time to analyze crucial health-related information.
