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Social media is interactive, and interaction brings misinformation. With thegrowing amount of user-generated data, fake news on online platforms has becomemuch frequent since the arrival of social networks. Now and then, an eventoccurs and becomes the topic of discussion, generating and propagating falseinformation. Existing literature studying fake news primarily elaborates onfake news classification models. Approaches exploring fake news characteristicsand ways to distinguish it from real news are minimal. Not many researches havefocused on statistical testing and generating new factor discoveries. Thisstudy assumes fourteen hypotheses to identify factors exhibiting a relationshipwith fake news. We perform the experiments on two real-world COVID-19 datasetsusing qualitative and quantitative testing methods. This study concludes thatsentiment polarity and gender can significantly identify fake news. Dependenceon the presence of visual media is, however, inconclusive. Additionally,Twitter-specific factors like followers count, friends count, and retweet countsignificantly differ in fake and real news. Though, the contribution of statuscount and favorites count is disputed. This study identifies practical factorsto be conjunctly utilized in the development of fake news detection algorithms.

People Lie, Actions Don't! Modeling Infodemic Proliferation Predictors among Social Media Users