  High-resolution data of online chats are studied as a physical system in
laboratory in order to quantify collective behavior of users. Our analysis
reveals strong regularities characteristic to natural systems with additional
features. In particular, we find self-organized dynamics with long-range
correlations in user actions and persistent associations among users that have
the properties of a social network. Furthermore, the evolution of the graph and
its architecture with specific k-core structure are shown to be related with
the type and the emotion arousal of exchanged messages. Partitioning of the
graph by deletion of the links which carry high arousal messages exhibits
critical fluctuations at the percolation threshold.
