People lie, actions Don't! Modeling infodemic proliferation predictors among social media users

Social media is interactive, and interaction brings misinformation. With the growing amount of user-generated data, fake news on online platforms has become much more 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 elaborates primarily on fake news classification models. Approaches exploring fake news characteristics to distinguish it from real news are minimal. Not much research has focused on statistical testing and generating new factor discoveries. This study assumes fifteen 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. We determine the impact of conditional effects among sentiment, gender, and media usage. 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 user engagement factors like followers count, friends count, favorite count, and retweet count significantly differ in fake and real news. Though, the contribution of status count is currently disputed. This study identifies practical factors to be conjunctly utilized in developing fake news detection algorithms.