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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.
Deep Neural Networks Ensemble for Detecting Medication Mentions in Tweets
A fundamental step to incorporating Twitter data in pharmacoepidemiological research is to automatically recognize medication mentions in tweets.
Dealing with Medication Non-Adherence Expressions in Twitter
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.
Detecting Personal Medication Intake in Twitter: An Annotated Corpus and Baseline Classification System
Social media sites (e. g., Twitter) have been used for surveillance of drug safety at the population level, but studies that focus on the effects of medications on specific sets of individuals have had to rely on other sources of data.
