Quantifying Virality of Information in Online Social Networks

Abstract

The aim of this research is to propose a model through which the viral nature of an information item in an online social network can be quantified. Further we propose an alternate technique for information asset valuation by accommodating virality in it which not only complements the existing valuation system, but also improves the accuracy of the results. We used a popularly available YouTube dataset to collect attributes and used it to measure critical factors such as share-count, Appreciation, User rating, Controversiality and Comment rate. These variables are then used with a proposed formula to obtain viral index of each video on a given date. We then identify a conventional and a hybrid asset valuation technique to demonstrate how virality can fit in to provide accurate results. The research demonstrates the dependency of virality on critical social network factors. With the help of second dataset acquired by us, we determine the pattern virality of an information item takes over time. The findings provide a clear cut manifestation for the practitioner or researcher to utilize the model in real-world scenario.