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.

翻译：在抗击COVID-19疫情过程中，初期通过封锁、社交距离和佩戴口罩等预防措施取得了诸多成功。近期，各类疫苗的研发进一步助力预防或减轻COVID-19感染严重程度。尽管预防措施与疫苗效果显著，但推特等社交媒体平台上仍广泛传播着诸多争议性言论。本文探索利用前沿的Transformer语言模型，研究尼日利亚民众对疫苗的接受度。我们通过相关话题标签与关键词爬取多语言推文，构建了一个新型数据集。分析与可视化结果表明：大多数推文对COVID-19疫苗持中性态度，部分个体表达积极观点，且未发现对特定疫苗类型的强烈偏好（尽管莫德纳疫苗获得了略高的正面评价）。此外，研究发现：使用恰当数据集微调预训练大语言模型（LLM）可取得竞争性结果，即使该LLM最初并未基于该数据集的特定语言进行预训练。