Table 11 The results comparison

Our research

The first experiment explored the development of ML models. Totally, 132,523 texts on various topics, including Covid-19, were gathered

The best results of accuracy were achieved by DT (0.91–0.95) and RF (0.96–0.99) with the Random oversampling technique

Akpatsa et al. [16]

This paper analyzed topics, discussions, and concerns about Covid-19 vaccination using Twitter datasets. The final dataset contains 15,239 unique tweets

It achieved the following accuracies with an LR (0.83), an RF (0.83), an SVM (0.84), and an NB (0.77)

Yeasmin et al. [71]

This research explored Twitter datasets to analyze sentiments on the Covid-19 topic. The dataset included tweets from different states of the USA for 15 days. A total number of 832,528 tweets were gathered

The following results of classification were achieved with ML algorithms: an LR (0.91), an SVM (0.94), an NB (0.91), k-NN (0.90), a DT (0.96), a RF (0.97), and XGBoost (0.83)

Daradkeh et al. [72]

This paper describes SA of topics related to Covid-19 vaccine misinformation. A corpus of 40,359 tweets has been collected for the dates between January 2021 and March 2021

It got the following values of accuracy: a DT (0.81), an SVM (0.78), a k-NN (0.76), and an NB (0.74)

Mishra et al. [73]

This research paper analyzed the public’s sentiments towards the Covid-19 vaccination in India. The dataset included 5977 tweets before the second wave and 42,936 tweets after the second wave

The following values of accuracy were achieved: an LR (0.61), a DT (0.45), a k-NN (0.58), an RF (0.59), and an XGBoost (0.54)

Iwendi et al. [74]

This paper focuses on gathering real and fake news data on the topics related to Covid-19. The dataset consisted of 586 true news and 578 fake news and 1100 news articles and social media posts regarding Covid-19

The ML algorithms achieved the following values of accuracy: a k-NN (0.69), a DT (0.77), and AdaBoost (0.83)