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Numerous successes have been achieved in combating the COVID-19 pandemic,initially using various precautionary measures like lockdowns, socialdistancing, and the use of face masks. More recently, various vaccinations havebeen developed to aid in the prevention or reduction of the severity of theCOVID-19 infection. Despite the effectiveness of the precautionary measures andthe vaccines, there are several controversies that are massively shared onsocial media platforms like Twitter. In this paper, we explore the use ofstate-of-the-art transformer-based language models to study people's acceptanceof vaccines in Nigeria. We developed a novel dataset by crawling multi-lingualtweets using relevant hashtags and keywords. Our analysis and visualizationsrevealed that most tweets expressed neutral sentiments about COVID-19 vaccines,with some individuals expressing positive views, and there was no strongpreference for specific vaccine types, although Moderna received slightly morepositive sentiment. We also found out that fine-tuning a pre-trained LLM withan appropriate dataset can yield competitive results, even if the LLM was notinitially pre-trained on the specific language of that dataset.

Analyzing COVID-19 Vaccination Sentiments in Nigerian Cyberspace: Insights from a Manually Annotated Twitter Dataset