Analyzing COVID-19 Vaccination Sentiments in Nigerian Cyberspace:
  Insights from a Manually Annotated Twitter Dataset
Ibrahim Said Ahmad Lukman Jibril Aliyu Abubakar Auwal Khalid Saminu Muhammad Aliyu Shamsuddeen Hassan Muhammad Idris Abdulmumin Bala Mairiga Abduljalil Bello Shehu Bello Amina Imam Abubakar
Numerous successes have been achieved in combating the COVID-19 pandemic,
initially using various precautionary measures like lockdowns, social
distancing, and the use of face masks. More recently, various vaccinations have
been developed to aid in the prevention or reduction of the severity of the
COVID-19 infection. Despite the effectiveness of the precautionary measures and
the vaccines, there are several controversies that are massively shared on
social media platforms like Twitter. In this paper, we explore the use of
state-of-the-art transformer-based language models to study people's acceptance
of vaccines in Nigeria. We developed a novel dataset by crawling multi-lingual
tweets using relevant hashtags and keywords. Our analysis and visualizations
revealed that most tweets expressed neutral sentiments about COVID-19 vaccines,
with some individuals expressing positive views, and there was no strong
preference for specific vaccine types, although Moderna received slightly more
positive sentiment. We also found out that fine-tuning a pre-trained LLM with
an appropriate dataset can yield competitive results, even if the LLM was not
initially pre-trained on the specific language of that dataset.