Abstract:
The narratives shared on social media during a health crisis such as COVID-19
reflect public perceptions of the crisis. This article provides findings from a study of
the perceptions of South African citizens regarding the government’s response to
the COVID-19 pandemic from March to May 2020. The study analysed Twitter
data from posts by government officials and the public in South Africa to measure
the public’s confidence in how the government was handling the pandemic. A third
of the tweets dataset was labelled using valence aware dictionary and sentiment
reasoner (VADER) lexicons, forming the training set for four classical machinelearning
algorithms—logistic regression (LR), support vector machines (SVM),
random forest (RF), and extreme gradient boosting (XGBoost)—that were employed
for sentiment analysis. The effectiveness of these classifiers varied, with error rates of
17% for XGBoost, 14% for RF, and 7% for both SVM and LR. The best-performing
algorithm (SVM) was subsequently used to label the remaining two-thirds of the
tweet dataset. In addition, the study used, and evaluated the effectiveness of, two
topic-modelling algorithms—latent dirichlet allocation (LDA) and non-negative
matrix factorisation (NMF)—for classification of the most frequently occurring
narratives in the Twitter data. The better-performing of these two algorithms, NMF,
identified a prevalence of positive narratives in South African public sentiment
towards the government’s response to COVID-19.
