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Found 7 papers, 2 papers with code
Leveraging Large Language Models and Weak Supervision for Social Media data annotation: an evaluation using COVID-19 self-reported vaccination tweets
The COVID-19 pandemic has presented significant challenges to the healthcare industry and society as a whole.
Identifying epidemic related Tweets using noisy learning
Supervised learning algorithms are heavily reliant on annotated datasets to train machine learning models.
TweetDIS: A Large Twitter Dataset for Natural Disasters Built using Weak Supervision
Social media is often utilized as a lifeline for communication during natural disasters.
Characterizing drug mentions in COVID-19 Twitter Chatter
Since the classification of COVID-19 as a global pandemic, there have been many attempts to treat and contain the virus.
A large-scale COVID-19 Twitter chatter dataset for open scientific research -- an international collaboration
As the COVID-19 pandemic continues its march around the world, an unprecedented amount of open data is being generated for genetics and epidemiological research.
Social Media Mining Toolkit (SMMT)
There has been a dramatic increase in the popularity of utilizing social media data for research purposes within the biomedical community.
A large-scale Twitter dataset for drug safety applications mined from publicly existing resources
With the increase in popularity of deep learning models for natural language processing (NLP) tasks, in the field of Pharmacovigilance, more specifically for the identification of Adverse Drug Reactions (ADRs), there is an inherent need for large-scale social-media datasets aimed at such tasks.
