Can Cascades be Predicted?
Jure Leskovec
Assistant Professor of Computer Science, Stanford University
ABSTRACT
Social networks play a central role in spreading of information,
ideas, behaviors, and products. As such "contagions" diffuse from a
person to person they may go "viral," and large cascades can form.
However, a growing body of research has argued that virality and
cascades may be inherently unpredictable. Thus, one of the central
questions is whether information cascades can be predicted and
possibly even engineered. In this talk, I will discuss a framework for
predicting cascades and making them go viral. We study large sample of
cascades on Facebook and find strong performance in predicting whether
a cascade will continue to grow in the future. The models we develop
help us understand how to create viral social media content: by using
the right title, for the right community, at the right time.
BIO
Jure Leskovec is assistant professor of
Computer Science at Stanford University. His research focuses on
mining large social and information networks. Problems he investigates
are motivated by large scale data, the Web and on-line media. This
research has won several awards including a Microsoft Research Faculty
Fellowship, the Alfred P. Sloan Fellowship and numerous best paper
awards. Leskovec received his bachelor's degree in computer science
from University of Ljubljana, Slovenia, and his PhD in in machine
learning from the Carnegie Mellon University and postdoctoral training
at Cornell University. You can follow him on Twitter
@jure.
