Multi-Label Classification of COVID-Tweets Using Large Language Models
Aniket Deroy Subhankar Maity
Vaccination is important to minimize the risk and spread of various diseases.
In recent years, vaccination has been a key step in countering the COVID-19
pandemic. However, many people are skeptical about the use of vaccines for
various reasons, including the politics involved, the potential side effects of
vaccines, etc. The goal in this task is to build an effective multi-label
classifier to label a social media post (particularly, a tweet) according to
the specific concern(s) towards vaccines as expressed by the author of the
post. We tried three different models-(a) Supervised BERT-large-uncased, (b)
Supervised HateXplain model, and (c) Zero-Shot GPT-3.5 Turbo model. The
Supervised BERT-large-uncased model performed best in our case. We achieved a
macro-F1 score of 0.66, a Jaccard similarity score of 0.66, and received the
sixth rank among other submissions. Code is available
at-https://github.com/anonmous1981/AISOME