  A detailed analysis of Twitter-based information cascades is performed, and
it is demonstrated that branching process hypotheses are approximately
satisfied. Using a branching process framework, models of agent-to-agent
transmission are compared to conclude that a limited attention model better
reproduces the relevant characteristics of the data than the more common
independent cascade model. Existing and new analytical results for branching
processes are shown to match well to the important statistical characteristics
of the empirical information cascades, thus demonstrating the power of
branching process descriptions for understanding social information spreading.
