COVID-19 South African Vaccine Hesitancy Models Show Boost in
  Performance Upon Fine-Tuning on M-pox Tweets
Nicholas Perikli Srimoy Bhattacharya Blessing Ogbuokiri Zahra Movahedi Nia Benjamin Lieberman Nidhi Tripathi Salah-Eddine Dahbi Finn Stevenson Nicola Bragazzi Jude Kong Bruce Mellado
Very large numbers of M-pox cases have, since the start of May 2022, been
reported in non-endemic countries leading many to fear that the M-pox Outbreak
would rapidly transition into another pandemic, while the COVID-19 pandemic
ravages on. Given the similarities of M-pox with COVID-19, we chose to test the
performance of COVID-19 models trained on South African twitter data on a
hand-labelled M-pox dataset before and after fine-tuning. More than 20k
M-pox-related tweets from South Africa were hand-labelled as being either
positive, negative or neutral. After fine-tuning these COVID-19 models on the
M-pox dataset, the F1-scores increased by more than 8% falling just short of
70%, but still outperforming state-of-the-art models and well-known
classification algorithms. An LDA-based topic modelling procedure was used to
compare the miss-classified M-pox tweets of the original COVID-19 RoBERTa model
with its fine-tuned version, and from this analysis, we were able to draw
conclusions on how to build more sophisticated models.