Background: Few-shot learning (FSL) is a class of machine learning methods that require
small numbers of labeled instances for training. With many medical topics having limited …

Objective: Few-shot learning (FSL) methods require small numbers of labeled instances for
training. As many medical topics have limited annotated textual data in practical settings …

Studies have shown that Twitter can be a complementary source of data for monitoring
personal experiences of COVID-19, such as symptoms [1-8]. Given the lack of manually …

The novel coronavirus pneumonia (COVID-19) has created great demands for medical
resources. Determining these demands timely and accurately is critically important for the …

Text classifiers are applied at scale in the form of one-size-fits-all solutions. Nevertheless,
many studies show that classifiers are biased regarding different languages and dialects …

Background: Public health surveillance plays a vital role in informing public health decision-
making. The onset of the COVID-19 pandemic in early 2020 caused a widespread shift in …

Despite the increased attention and substantial research into it claiming outstanding
successes, the problem of misinformation containment has only been growing in the recent …

Billions of people across the globe have been using social media platforms in their local
languages to voice their opinions about the various topics related to the COVID-19 …

Few-shot learning, a specialized branch of machine learning, tackles the challenge of
constructing accurate models with minimal labeled data. This is particularly pertinent in text …

Epidemics and outbreaks present arduous challenges requiring both individual and
communal efforts. Social media offer significant amounts of data that can be leveraged for …