Predicting Content Virality in Social Cascade
Ming Cheung, James Pei Man She, Lei Cao · 2013
Predicting why and how certain content goes viral is attractive for many applications, such as viral marketing and social network applications, but is still a challenging task today. Existing prediction algorithms focus on predicting the content popularity without considering the timing. Those algorithms are based on information that may be uncommon or computationally expensive. This paper proposes a novel and practical algorithm to predict the virality of content. Instead of predicting the popularity, the algorithm predicts the time for the social cascade size to reach a given viral target. The algorithm is verified by the data from a popular social network - Digg.com and 2 synthesize datasets under different conditions. The results prove that the algorithm can achieve the lower bound with a practical significance for the time to reach the viral target.