Artificial Intelligence

Social Dynamics of Information

Friday, December 07, 2012, 11:00am - 12:00pm PDTiCal
USC/ISI 11th Floor Conf. Rm (#1135)
Kristina Lerman


Webcast link: tba 
It is widely believed that information spreads through a social network much like a virus, with “infected” individuals transmitting it to their friends, enabling information to reach many people. However, our studies of social media indicate that most information epidemics fail to reach viral proportions. We show that psychological factors fundamentally distinguish social contagion from viral contagion. Specifically, people have finite attention, which they divide over all incoming stimuli. This makes highly connected people less “susceptible” to infection and stops information spread.
In the second part of the talk I explore the connection between dynamics and network structure. I show that to find interesting structure, network analysis has to consider not only network’s links, but also dynamics of information flow. I introduce dynamics-aware network analysis methods and demonstrate that they can identify more meaningful structures in social media networks than popular alternatives.

Kristina Lerman is a Project Leader at the University of Southern California Information Sciences Institute and holds a joint appointment as a Research Associate Professor in the USC Computer Science Department. Trained as a physicist, she now applies network- and machine learning-based methods to problems in social computing.



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