###### Abstract  
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 information and follow proper procedures to mitigate the risks. This research aims to target the falsehood part of the infodemic, which prominently proliferates in news articles and false medical publications. Here, we present NeoNet, a novel supervised machine learning algorithm that analyzes the content of a document (news article, a medical publication) and assigns a label to it. The algorithm was trained by Term Frequency Inverse Document Frequency (TF-IDF) bigram features, which contribute a network training model. The algorithm was tested on two different real-world datasets from the CBC news network and COVID-19 publications. In five different fold comparisons, the algorithm predicted a label of an article with a precision of 97-99%. When compared with prominent algorithms such as Neural Networks, SVM, and Random Forests NeoNet surpassed them. The analysis highlighted the promise of NeoNet in detecting disputed online contents, which may contribute negatively to the COVID-19 pandemic. COVID-19 infodemic; text classification; TF-IDF features; network training modes; supervised learning; misinformation; news classification; false publications; PubMed anomaly detection +
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the pandemic in attempt to explain the unusual circumstances [11]. In fact, similar theories emerged during the SARS outbreak in China and the Ebola outbreak the Congo [12]; (3) Misinformation and public health damage has been related to tweeting bad advice from people of authority. For instance, [PERSON] et al., stated that in a tweet, the French minister of health warned the citizens of his country not to use certain drugs (e.g., cortisone), advice that has gone viral during the pandemic [13]. Later on, clinical trials proved that cortisone is beneficial. Clearly, such events have the effect of dispensing significant treatment, and in this case, any reference to cortisone was eliminated; (4) Stigma which overwhelmed social media in the form of hashtags contributed to a backlash against countries and people (e.g., stigma against China and Chinese people) [12], (5) disinformation, which is an intentional act to deliver false information to mislead the general public. A significant instance that took place during the pandemic was the promotion of vitamin D by an Indonesian author. The article and its recommendations turned out to be from a suspicions source, as the authors' names were never linked to the listed affiliation. Such an article was downloaded 17,000 times and mentioned 8000 times on social media platforms. The matter made worse when the article was also broadcasted by DailyMail, a major news network, in an article entitled: \"Terriving chart shows how Covid-19 patients who end up in hospital may be almost certain to die if they have a vitamin D deficiency\" [14]. Indeed, it is terrifying to witness major news organizations making make life and death assertions based on suspicious sources such as this suspicious kind of publication. Presenting the above evidence begged the question of \"Who do you trust? How to better mitigate?\". Several efforts have also emerged to address the flood of information and provided guidelines and recommendations on how to answer such questions. In fact, this exact question has been answered an article titled: \"Who do you trust? The digital destruction of shared situational awareness and the COVID-19 infodemic\" [15]. The authors of the referenced article referred to how the digital disruption of social media and search engines are responsible for the digital destruction by the act of propagating misinformation. The article urged for the development of new methods and approaches to establish and build trust among the users and their platforms. Another prominent reference was titled: \"How to fight an Infodemic: The Four Pillars of Infodemic Management\" [16].