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.