Prediction of Virality Timing Using Cascades in Social Media

Abstract

Predicting content going viral in social networks is attractive for viral marketing, advertisement, entertainment, and other applications, but it remains a challenge in the big data era today. Previous works mainly focus on predicting the possible popularity of content rather than the timing of reaching such popularity. This work proposes a novel yet practical iterative algorithm to predict virality timing, in which the correlation between the timing and growth of content popularity is captured by using its own big data naturally generated from users’ sharing. Such data is not only able to correlate the dynamics and associated timings in social cascades of viral content but also can be useful to self-correct the predicted timing against the actual timing of the virality in each iterative prediction. The proposed prediction algorithm is verified by datasets from two popular social networks—Twitter and Digg—as well as two synthesized datasets with extreme network densities and infection rates. With about 50% of the required content virality data available (i.e., halfway before reaching its actual virality timing), the error of the predicted timing is proven to be bounded within a 40% deviation from the actual timing. To the best of our knowledge, this is the first work that predicts content virality timing iteratively by capturing social cascades dynamics.