Fighting the COVID-19 Infodemic in News Articles and False Publications: The NeoNet Text Classifier, a Supervised Machine Learning Algorithm

The spread of the Coronavirus pandemic has been accompanied by an infodemic. false information that is embedded in infodemic affects people’s ability to have access safety and follow proper procedures mitigate risks. This research aims target falsehood part infodemic, which prominently proliferates news articles medical publications. Here, we present NeoNet, a novel supervised machine learning algorithm analyzes content document (news article, publication) assigns label it. was trained Term Frequency Inverse Document (TF-IDF) bigram features, contribute network training model. tested on two different real-world datasets from CBC COVID-19 In five fold comparisons, predicted article with precision 97–99%. When compared prominent algorithms such as Neural Networks, SVM, Random Forests NeoNet surpassed them. analysis highlighted promise detecting disputed online contents, may negatively pandemic.