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

TL;DR

This study analyzes Nigerian Twitter data to understand public sentiments towards COVID-19 vaccines, revealing mostly neutral opinions and demonstrating the effectiveness of fine-tuned language models in sentiment analysis.

Contribution

The paper introduces a novel multilingual Twitter dataset on COVID-19 vaccines in Nigeria and evaluates transformer models for sentiment analysis in this context.

Findings

Most tweets expressed neutral sentiments about vaccines

Slightly more positive sentiment towards Moderna

Fine-tuning pre-trained models yields competitive results

Abstract

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.