  Social media is interactive, and interaction brings misinformation. With the
growing amount of user-generated data, fake news on online platforms has become
much frequent since the arrival of social networks. Now and then, an event
occurs and becomes the topic of discussion, generating and propagating false
information. Existing literature studying fake news primarily elaborates on
fake news classification models. Approaches exploring fake news characteristics
and ways to distinguish it from real news are minimal. Not many researches have
focused on statistical testing and generating new factor discoveries. This
study assumes fourteen hypotheses to identify factors exhibiting a relationship
with fake news. We perform the experiments on two real-world COVID-19 datasets
using qualitative and quantitative testing methods. This study concludes that
sentiment polarity and gender can significantly identify fake news. Dependence
on the presence of visual media is, however, inconclusive. Additionally,
Twitter-specific factors like followers count, friends count, and retweet count
significantly differ in fake and real news. Though, the contribution of status
count and favorites count is disputed. This study identifies practical factors
to be conjunctly utilized in the development of fake news detection algorithms.
