Vaccine Hesitancy Hotspots in Africa: An Insight From Geotagged Twitter Posts

Abstract

Many social media users express concerns about vaccines and their side effects on Twitter. These concerns lead to a compromise of confidence which brings about vaccine hesitancy. In Africa, vaccine hesitancy is a major challenge faced by health policymakers in the fight against COVID-19. Given that most tweets are geotagged, clustering them according to their sentiments could help identify locations that may likely experience vaccine hesitancy for health policy and planning. In this study, we collected 70 000 geotagged vaccine-related tweets in nine African countries, from December 2020 to February 2022. The tweets were classified into three sentiment classespositive, negative, and neutral. The quality of the classification outputs was achieved using Naíve Bayes (NB), logistic regression (LR), support vector machines (SVMs), decision tree (DT), and K-nearest neighbor (KNN) machine learning classifiers. The LR achieved the highest accuracy of 71% with an average area under the curve of 85%. The point-based location technique was used to calculate the hotspots based on the locations of the classified tweets. Locations with green, red, and gray backgrounds on the map signify a hotspot for positive, negative, and neutral sentiments. The outcome of this research shows that discussions on social media can be analyzed to identify hotspots during a disease outbreak, which could inform health policy in planning and management of vaccine hesitancy in Africa.

A natural language processing approach for analyzing COVID-19 vaccination response in multi-language and geo-localized tweets

“…Ogbuokiri et al. [26] gathered 70,000 geotagged vaccine-related tweets across nine African countries from December 2020 to February 2022, categorizing them into positive, negative, and neutral sentiments. The findings of this study demonstrate that social media data can be utilized to supplement current data in detecting outbreak hotspots and to address vaccine hesitancy.…”

Off-label drug use during the COVID-19 pandemic in Africa: topic modelling and sentiment analysis of ivermectin in South Africa and Nigeria as a case study

“…This again shows that providing real medication such as vaccines to people stops them from consuming off-label drugs such as ivermectin. Moreover, papers that study COVID-19 vaccination from a social media perspective confirm that as vaccine supplies become available and rollouts take place, sentiments and emotions towards vaccination level up and become more positive [ 19 , 44 – 46 ].…”

An Improved Ensemble Method for Predicting Hyperchloremia in Adults With Diabetic Ketoacidosis

“…Support Vector Machines (SVMs) are useful traditional machine learning algorithms used for classification and regression tasks on numeric or text data [64]. SVMs aim to find an optimal hyperplane that separates different classes of data points or predicts a continuous output value, while maximizing the margin between them.…”

“…Given a dataset with numeric features and corresponding class labels, the K-Nearest Neighbour (KNN) algorithm classifies a new data point by considering its K nearest neighbors based on a distance metric (e.g., Euclidean distance) [64]. The majority class among the K nearest neighbors determines the class label assigned to the new data point [64]- [66].The formula for KNN classification can be represented as follows:…”

“…Logistic Regression (LR) is a popular machine learning algorithm used for binary classification on numeric datasets [64]. It models the relationship between the independent variables (features) and the probability of the binary outcome using the logistic function.…”