Abstract:

The ever increasing adoption of online social networks and mobile technologies allows to measure human behaviors at unprecedented levels of detail and scale. This enables a data-driven investigation of complex processes over social networks, challenging established assumptions and paving the road to new models.

In the first part of the talk we will focus on human mobility, and in particular on dynamic networks of face-to-face proximity in indoor environments, measured by using wireless wearable sensors. We will show that simple spreading processes can be used as dynamical probes to expose important features of the interaction patterns such as burstiness and causal constraints. We will argue that in order to correctly model the arrival times of messages propagating over the network, it is useful to abandon the notion of wall-clock time in favor of a node-specific notion of time, defined in terms of activity levels. We will use this insight to build a model of dynamic social network that reproduces in simulation the spreading features observed for empirical data. Finally, we will highlight a few challenges in using empirical human contact networks to simulate more complex epidemic processes.

In the second part of the talk we will shift our focus to online social networks and discuss information spreading in the Twitter micro-blogging system. We will discuss the viral adoption of social annotations (hashtags) and track their popularity over time as a proxy for the collective attention of an online community. We will show that hashtag popularity defines discrete classes of collective attention that correspond to diverse social semantics of the hashtags. We will model hashtag adoption as an epidemic susceptible-infectious-recovered (SIR) process, and discuss the interpretation of the measured epidemic parameters for the different hashtag classes.

Date: 2012-May-23     Time: 11:00:00     Room: 020

For more information:

• goncalo,simoes@ist.utl.pt