Scalable social analytics for live viral event prediction

Abstract

Large-scale, predictive social analytics have proven effective. Over the last decade, research and industrial efforts have understood the potential value of inferences based on online behavior analysis, sentiment mining, influence analysis, epidemic spread, etc. The majority of these efforts, however, are not yet designed with realtime responsiveness as a first-order requirement. Typical systems perform a post-mortem analysis on volumes of historical data and validate their "predictions" against already-occurred events.We observe that in many applications, real-time predictions are critical and delays of hours (and even minutes) can reduce their utility. As examples: political campaigns could react very quickly to a scandal spreading on Facebook; content distribution networks (CDNs) could prefetch videos that are predicted to soon go viral; online advertisement campaigns can be corrected to enhance consumer reception.