The spread of the Coronavirus pandemic has been accompanied by an infodemic. The false information that is embedded in the infodemic affects people’s ability to have access to safety and follow proper procedures to mitigate the risks. Here, we present a novel supervised machine learning text mining algorithm that analyzes the content of a given news article and assign a label to it. The NeoNet algorithm is trained by noun-phrases features which contributes a network model. The algorithm was tested on a real-world dataset and predicted the label of never-seem articles and flags ones that are suspicious or disputed. In five different fold comparisons, NeoNet surpassed prominent contemporary algorithm such as Neural Networks, SVM, and Random Forests. The analysis shows that the NeoNet algorithm predicts a label of an article with a 100% precision using a non-pruned model. This highlights the promise of detecting disputed online contents that may contribute negatively to the COVID-19 pandemic. Indeed, using machine learning combined with powerful text mining and network science provide the necessary tools to counter the spread of misinformation, disinformation, fake news, rumors, and conspiracy theories that is associated with the COVID19 Infodemic.