Rise and fall patterns of information diffusion: model and implications

The recent explosion in the adoption of search engines and new media such as blogs and Twitter have facilitated faster propagation of news and rumors. How quickly does a piece of news spread over these media? How does its popularity diminish over time? Does the rising and falling pattern follow a simple universal law? In this paper, we propose SpikeM, a concise yet flexible analytical model for the rise and fall patterns of influence propagation. Our model has the following advantages: (a) unification power: it generalizes and explains earlier theoretical models and empirical observations; (b) practicality: it matches the observed behavior of diverse sets of real data; (c) parsimony: it requires only a handful of parameters; and (d) usefulness: it enables further analytics tasks such as forecasting, spotting anomalies, and interpretation by reverseengineering the system parameters of interest (e.g. quality of news, count of interested bloggers, etc.). Using SpikeM, we analyzed 7.2G...

• Type: Journal
• Year: 2012
• Where: KDD
• Authors: Yasuko Matsubara, Yasushi Sakurai, B. Aditya Prakash, Lei Li, Christos Faloutsos
• Keywords: Data Mining, Empirical Observations, KDD 2012, Rise And Fall Patterns, System Parameters