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Found 10 papers, 1 papers with code
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.
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.
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.
Towards Automatic Bot Detection in Twitter for Health-related Tasks
Our approach obtains F_1 scores of 0. 7 for the "bot" class, representing improvements of 0. 339.
Automatically Identifying Comparator Groups on Twitter for Digital Epidemiology of Pregnancy Outcomes
Despite the prevalence of adverse pregnancy outcomes such as miscarriage, stillbirth, birth defects, and preterm birth, their causes are largely unknown.
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.
Automatically Detecting Self-Reported Birth Defect Outcomes on Twitter for Large-scale Epidemiological Research
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.
An unsupervised and customizable misspelling generator for mining noisy health-related text sources
Our proposed spelling variant generator has several advantages over the current state-of-the-art and other types of variant generators-(i) it is capable of filtering out lexically similar but semantically dissimilar terms, (ii) the number of variants generated is low as many low-frequency and ambiguous misspellings are filtered out, and (iii) the system is fully automatic, customizable and easily executable.
