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Social networks act as a great platform for information diffusion where millions of people can exchange information. With the advent of online social networks, spread of information has escalated, thus, making social networks an interesting area of research. Epidemic modeling is a substantial way of studying information diffusion. In this paper, we propose a Susceptible, Exposed-Infected (SEI) Model to analyze how a piece of information spreads in social networks and establish a gap between the number of suspectible, exposed and infected users. In the contemporary SI model, we introduce a set of exposed users which are very high in number as compared to infected users but have a high probability of being infected. Also, the paper aims to demonstrate the wide gap that exists between exposed and infected users with the help of real time data of Twitter.